Case Presentation. We report a case of a 43-year-old female patient who developed acute symptomatic hyponatremia (104 mEq/L) and pulmonary edema secondary to hysteroscopic resection of leiomyoma and hastily approached with rapid sodium correction measures.  Hysteroscopic surgery is a minimally invasive procedure used to diagnose and treat intrauterine pathologies. It requires distension of the uterine cavity for the adequate visualization of the operative field. Glycine (1.5%) is one of the most commonly used solutions because it is nonconductive and also has good optical properties. However, acute hyponatremia is a critical complication that can develop after the absorption of a sufficient amount of the irrigation medium.  Multiple strategies can be taken to reduce the risk of fluid absorption and subsequent hyponatremia. Moreover, attention should be paid to the treatment approach for patients with acute hyponatremia following hysteroscopic procedures; rapid correction of acute hyponatremia for such patients might be safe, although there is no consensus in the literature, and further trials are needed. Hysteroscopy has implemented a minimally invasive procedure for treating and diagnosing intrauterine pathology. A uterine distending medium is used to allow an optimal and global view of the endometrial cavity. Nonconductive  and conductive solutions are widely used as a distension medium for hysteroscopic procedures. Glycine is one of the most commonly used irrigation fluids in hysteroscopy; it is a hypoosmotic solution compared to serum osmolality, and it has an osmolality of 200 mosmol/kg .Complications from hysteroscopy are rare, with a reported rate of 0.22%. Hyponatremia and fluid overload are considered as feared but rare complications of hysteroscopy; according to a multicenter study of over 21,000 operative hysteroscopic procedures, the incidence rate of fluid overload and hyponatremia is 0.06% .Patients with acute hyponatremia due to hysteroscopy can be asymptomatic. They can also have variable symptoms, such as nausea, vomiting, confusion, and convulsions, depending on the severity of hyponatremia and irrigant absorption rate. Furthermore, the use of general anesthesia can also conceal the symptoms of hyponatremia –6.Hyponatremia in hysteroscopic procedures has been reported with the use of 1.5% glycine, 3% sorbitol, 5% mannitol, and 5% dextrose which are the major nonconductive  irrigation and distension fluids –10.A number of factors should be considered when selecting distention media for hysteroscopy, including the procedure to be performed and the instruments to be used. The traditional electrosurgery devices have a monopolar design and can be only used with electrolyte-free irrigation fluids . HoweverHere, we report on a patient undergoing a hysteroscopic removal of leiomyoma with an endometrial ablation, who developed acute symptomatic hyponatremia (104 mEq/L) due to the irrigation fluid (1.5% glycine).A 43-year-old woman  presented with a history of 7-month duration of intermittent and prolonged vaginal bleeding. A physical examination and an ultrasound showed a submucosal leiomyoma that protruded into the uterine cavity. There were no other symptoms or pathological findings. The patient had a free past medical and surgical history. She had no known allergies. She was taking daily doses of ethinyl estradiol (0.03 mg) and drospirenone (3 mg) continuously over 3 months and iron bisglycinate (36 mg) over the same period. The patient was offered scheduled hysteroscopic removal of the leiomyoma, and an endometrial ablation under general anesthesia (GA) was decided on, after which the patient gave written informed consent.2)). Immediately before the anesthesia induction, vital signs were blood pressure 104/69 mmHg, heart rate 107 beats/min, pulse oximetry revealed 100% of SpO2, and temperature of 37.6°C before anesthetic induction; general anesthesia was induced intravenously with propofol and sufentanil. An endotracheal tube (ETT) was facilitated with rocuronium, and after the correct positioning was verified, the lungs were ventilated with a fraction of inspired oxygen (FiO2) of 0.5 and a minute volume (MV) of 74 mL/min/kg. After anesthesiological clearance, the hysteroscopy commenced, dilation was done by Hegar dilator till #9. Anesthesia was maintained with continuous infusion of propofol. Glycine (1.5%) solution was used as the distension medium. This standard hypotonic solution contains 1.5 g glycine per 100 mL of water and has an osmolality of 200. A pressure of 120 mmHg was applied to unfold the uterine cavity. Endometrial ablation and dissection of a 2.5∗3∗3 cm submucosal fundal myoma were performed within 20 min by monopolar resection. The patient remained in a hemodynamically stable condition throughout the procedure.Preoperative laboratory findings, which included a complete blood exam (CBC), electrolytes analyses, coagulation parameters, showed anemia with hemoglobin of 7.5 g/dL, and a 12-lead electrocardiogram (ECG) showing sinus tachycardia. Most notably, both creatinine and sodium were within normal levels, that is, 1.1 mg/dL and 139 mEq/L, respectively. Upon arrival at the operating room, the patient was placed in the lithotomy position followed by the application of a peripheral venous catheter and standard monitoring devices , and pulse oximetry , abdominal ultrasound was free, and vaginal examination revealed no active bleeding. The bladder was catheterized, with a urine output of 2500 mL for a duration of 30 mins, and the patient was sent to the gynecological ward. Five minutes later, the patient showed desaturation of SpO2 85%, became drossy, and developed blurred vision. Supplemental O2 at a rate of 5 Liters per minute by face mask was applied, 20 mg of furosemide was given, and blood tests and electrolyte analyses indicated a drop of hemoglobin to 6 g/dL and hyponatremia (Na 104 mEq/L). About 500 mL of 3% saline solution was immediately administered at an infusion rate of 500 mL/h and 2 units of PRBCs were given with precisely monitoring for electrolyte changes every hour. Meanwhile, the patient started complaining of headaches, nausea, and vomiting. One hour later, serum electrolyte analyses showed an elevation of Na level to 118. The order was given to switch fluids to normal saline with a rate of 100 cc/h, repeat serum analysis every hour, and do a neurological exam every 30 minutes. Plasma sodium levels increased to 119 mEq/L after 1 h and to 134 mEq/L after 6 h, as shown in The ET tube was removed, and the patient was immediately sent to the recovery area. Upon recovery, the SpO2 was 98% on room air, no complaints, and sodium level was within normal (Na 135 mEq/L). Until then, the cumulative renal excretion was 8.5 L. 9 liters of distension medium was used intraoperatively with a calculated uptake of 4 L. A sample was not obtained for serum osmolarity determination. The patient recovered uneventfully and was discharged from the hospital on the second postoperative day. During a 6-month follow-up, the patient remained asymptomatic with no neurologic complications.The neurological assessment revealed no detectable deficits, and symptoms were revealed except for a headache. 12 hours after the onset of hyponatremia, the patient was off oxygen, and SpOFluid overload and hyponatremia are life-threatening complications associated with the use of nonconductive hypoosomolar distension media in hysteroscopy. Sufficient absorption of solutions like 1.5% glycine, 3% sorbitol, and 5% mannitol is more associated with dilutional hyponatremia, whereas using isotonic normal saline is associated with fluid overload . Fluid aThe data regarding the rule of type of anesthesia on fluid absorption are conflicting; a randomized prospective comparative study by Goldenberg et al. found that fluid absorption is significantly higher with hysteroscopies done by epidural anesthesia when compared to general anesthesia. They hypothesized that epidural anesthesia causes vasodilation of the pelvic vessels, a consequence caused by the known effect of epidural anesthesia in sympathetic blocking , 16. ConIn a randomized trial of 33 women who had a hysteroscopic endometrial ablation, administration of intracervical vasopressin solution (0.05 U/mL) was associated with a significant decrease in plasma glycine concentration but a small nonsignificant reduction of dilutional hyponatremia . In anotThe 1.5% glycine is one of the most common solutions used in monopolar hysteroscopic procedures due to its nonhemolytic, nonconductive, and transparent properties, although it is more toxic and has poorer outcomes when confronted with other options . The absThe severity of hyponatremia is highly connected to the volume deficit of the glycine solution; a volume deficit of 1000 mL will reduce sodium concentration in the plasma by 10 mEq/L . An exceModerate to severe hyponatremia, associated with glycine irrigant absorption, can cause many serious neurological symptoms like seizures, confusion, coma, and visual disturbances . Visual Hyponatremia caused by using glycine solution has a less pronounced fall in the measured plasma osmolality due to the presence of osmotically active particles of glycine in extracellular space. Although there is a modest reduction in serum osmolality, yet marked neurologic symptoms still occur , 44. ThiAmerican College of Obstetricians and Gynecologists (ACOG) suggests to temporarily halt the procedure after 750 mL absorption deficit and termination of the procedure if the deficit exceeds 1000 mL [It is well established that distending media containing electrolytes urge the use of bipolar resectoscope instruments. These newer devices are compatible with electrolyte-containing irrigation and distension solutions such as isotonic saline and Ringer's lactate. The use of isotonic saline or Ringer's lactate solutions as a distension medium is not associated with hyponatremia or hypoosmolality , 46. On  1000 mL . Careful 1000 mL .The treatment of excessive fluid absorption and subsequent hyponatremia depends on the patient's clinical picture. Asymptomatic hyponatremia does not require any urgent intervention, whereas marked, symptomatic hyponatremia must be treated as quickly as possible to reduce the risk of complications. Rapid correction of acute hyponatremia is probably safe, as the cerebral adaptation needs time to take place . Based oUnfortunately, there is no evidence from controlled trials, as to what the best or optimal correction rate is in acute hyponatremia following hysteroscopic procedures.Nevertheless, ACOG guidelines recommended that the serum sodium level should not be raised more than 12 mEq/L in the first 24 h with strict monitoring; to prevent reaching normal or hypernatremic levels –54. ACOGWhen the hyponatremia is associated with a relatively normal measured serum osmolality, in such cases where glycine remains confined to the extracellular fluid, hemodialysis should be considered since hypertonic saline therapy is associated with an increased risk of pontine myelinolysis. Hemodialysis showed to safely stabilize the patients and correct the electrolyte and fluid disturbances, and it gives the benefits of correcting the hyponatremia while eliminating the unmeasured solute , 44. IndThe systemic absorption of distending medium used in hysteroscopic producers can have multiple complications, of which acute hyponatremia can have severe consequences. Therefore, multiple measures can be taken to avoid those complications. Using electrolyte-based distending media instead of hypoosmolar solutions significantly reduces the risk. In addition, attention should be on reducing and precisely monitoring fluid intake. Furthermore, some studies have focused on the role of anesthetic technique on fluid absorption, using local anesthesia instead of other types, combining intravenous oxytocin infusion with spinal anesthesia, or the concomitant use of intrauterine vasopressin revealed promising protection against volume overload and hyponatremia. Future randomized trials ought to focus on the impact of anesthesia and such medications on developing hyponatremia. Besides, we should review the role and the outcome of rapid correction of acute hyponatremia in such cases, since anecdotal reports are not in line with the literature regarding developing osmotic demyelinating syndrome. Conclusively, our case supports the advantage of the rapid correction of acute hyponatremia.


A chicken ToxChip PCR array, comprising 43 target genes, was used to determine effects on gene expression, and similar to results for cell viability, gene expression profiles were highly variable among the megacities. PLS modeling was used to determine if gene expression was related to the OFR profiles of the extracts. Weak relationships to the ToxChip expression profiles could be detected for only three of the 35 OFRs . While this in vitro approach shows promise in terms of evaluating effects of complex mixtures, we also identified several limitations that, if addressed in future studies, might improve its performance.Assessing complex environmental mixtures and their effects is challenging. In this study, we evaluate the utility of an avian in vitro screening approach to determine the effects of passive air sampler extracts collected from different global megacities on cytotoxicity and gene expression. Concentrations of a suite of organic flame retardants (OFRs) were quantified in extracts from a total of 19 megacities/major cities in an earlier study, and levels were highly variable across sites. Chicken embryonic hepatocytes were exposed to serial dilutions of extracts from the 19 cities for 24 h. Cell viability results indicate a high level of variability in cytotoxicity, with extracts from Toronto, Canada, having the lowest LC50 value. Partial least squares (PLS) regression analysis was used to estimate LC50 values from OFR concentrations. PLS modeling of OFRs was moderately predictive of LC50 ( Assessment of complex mixtures that organisms are exposed to in the natural environment is a longstanding challenge . MixtureEffects-based approaches, such as in vitro bioassays, allow a direct measure of discrete endpoints following exposure to a complex mixture, which can provide insight into potential toxicity, related mechanisms of actions, and information regarding the contents of a mixture ,6,7. EffComplex mixtures of air pollutants emitted from industrial, residential and transportation sectors have raised great concerns with regard to their potential impact on human health and the environment ,16. EffeHere, we conducted a study to evaluate whether a well-established avian hepatic in vitro screening approach represented a useful effects-based monitoring tool for assessing effects of complex mixtures. The tested mixtures were passive air sampler extracts with known concentrations of one particular class of air pollutant, OFRs, collected from 19 global megacities as part of the GAPS-MC project . OFRs arv/v). Unlike the chemical analysis of OFRs in samplers from period 1, no surrogate or internal standards were added to samples for in vitro screening prior to extraction to ensure no interference with downstream toxicity testing. Each air sample extract was concentrated to 5 mL using rotary evaporation. The samples were further reduced to incipient dryness under nitrogen blow down and then solvent exchanged into 0.5 mL DMSO for in vitro screening.Passive air samples were collected between March 2018 and June 2019 for two periods of 3 months each at 19 selected megacities , with poGallus gallus domesticus) eggs (n = 36), obtained from the Canadian Food Inspection Agency , were incubated (Petersime Model XI) at 37 °C and 60% relative humidity until 2 days pre-hatch [2) prior to dosing with 2.5 μL of the solvent control (0.5% DMSO) or serial dilutions of “neat” air extracts. The extract dilutions used for the cell viability plates were 1 (“neat”), 0.1 and 0.01. For PCR array analysis, an extract dilution of 0.1 was included, with three replicates per treatment group. Following dosing, cells were incubated for 24 h, the medium was aspirated, and the plates were either immediately frozen and stored at −80 °C for subsequent RNA isolation or assayed for cell viability. Fertilized, unincubated domestic chicken , which measures cellular ATP concentrations as a proxy for viability, according to the manufacturer’s protocol with one modification: prior to the cell lysis step, all medium was aspirated, 100 μL of fresh medium was added to each well, followed by 50 μL of lysis reagent. Viability of cells treated with air extracts was compared to untreated and DMSO controls and a positive control sample that yielded 100% cell death . Luminesn = 3 technical replicates per treatment group) following the manufacturer’s protocols, with two modifications: (a) 1 volume of 50% ethanol (instead of 70% ethanol) was used following cell lysis (to enhance RNA yield), and (b) final elution was performed with 30 μL RNAse-free water. Total RNA concentration and purity were determined by measuring absorbance at 230, 260 and 280 nm using a NanoDrop 2000 spectrophotometer . Samples with an A260/280 > 1.8 were used for cDNA synthesis. Total RNA (~300 ng) was reverse transcribed to cDNA using the Quantitect Reverse Transcription Kit (Qiagen), according to manufacturer’s protocol, with two modifications: the 42 °C genomic DNA elimination incubation was extended to 8 min, and the 42 °C reverse transcription incubation was extended to 30 min [2 SYBR Green Mastermix (Qiagen) and 25 μL was aliquoted to each well of a custom-designed sixth-generation chicken ToxChip qPCR array . The ToxChip was built by Qiagen according to our specifications and was described in detail previously [Total RNA was extracted from CEH exposed to the highest non-cytotoxic dilution  of the air extracts using the Qiagen RNeasy 96 kit   vs. normalized response) using GraphPad Prism  to calculate LC50 values.−ΔCT method [p-value < 0.05 and fold change > 2. Cluster analysis and principal component analysis (PCA) of OFR chemical concentrations and gene expression fold changes across the different megacities were performed using the R package, ComplexHeatmap [Raw cycle thresholds (Ct) from the Chicken ToxChip were analyzed using the 2T method . Raw Ct xHeatmap .p predictor variables. PLS performs simultaneous decomposition of X and Y into latent variables (T) and associated loading vectors (Q). Regression is performed on these components, thus [−Ct) or LC50 data. A two-step process was used for model optimization: first, a preliminary model was derived using all available predictor variables . This model was assessed for irregularities  prior to optimization. The preliminary model was optimized by keeping only the predictor variables that had variable importance in projection (VIP) values > 1. A leave-one-out cross validation was used to predict all response variables . Model performance was assessed by comparing the regression slope and root mean square error (RMSE) of prediction when comparing predicted responses to actual responses.To determine if cytotoxicity could be estimated from OFR concentrations, or if OFR concentrations could be estimated using gene expression information, partial least square regression (PLS) was performed on paired chemical and bioactivity data . PLS is ts, thus Y = TQ +x-axis, A previous analysis revealed that levels of OFRs detected in passive air samples varied greatly among global megacities . We obseIt is important to note that these air sample extracts are complex mixtures, comprising many more chemicals/particulate matter than just the OFRs we measured. In particular, for air samples collected from cities, polycyclic aromatic compounds (PACs) would likely contribute to the toxicological signature. Furthermore, as recently shown by the same GAPS megacity study, oxidation of parent chemicals present in air results in a multitude of transformation products, which could increase the complexity and toxicity of these mixtures . However2 < 0.01, Cell death represents a clear adverse outcome that can be measured immediately following exposure of hepatocytes, thus providing an initial ranking of the efficacy of the extracts to elicit a response. The rank order of PUF extracts from the 19 different megacities based on LC50 values is shown in General trends in OFR concentrations  did not correlate with LC50 . We usedp-value = 0.0003, r2 = 0.66, slope = 0.76, rmse = 0.19)  C. The to = 0.19) . These rWe next investigated whether changes in the gene expression patterns in CEH exposed to the extracts had any relationship to the OFR concentrations. A chicken ToxChip PCR array , which is not surprising given that these transcripts are commonly dysregulated in response to xenobiotics [Cyp1a4 following activation of the aryl hydrocarbon receptor (AHR) by diverse agonists has been studied extensively, as well as Alas1, which is induced by xenobiotics to ensure sufficient heme production for cytochrome P450 synthesis and other hemoproteins [Cyp1a4 and Alas1 were significantly upregulated [Cyp1a4 and therefore require further evaluation in future studies. A target gene associated with the thyroid hormone pathway, Thrsp, which encodes the thyroid hormone-inducible hepatic protein and is expressed in liver and adipose tissue, where it plays a role in lipogenesis regulation [Thrsp in CEH following exposure [Despite the lack of overall patterns between gene expression and OFR concentrations, we did observe some expected and interesting responses. The most highly dysregulated transcripts from the ToxChip were d Ugt1a9 . Severalobiotics ,35. Thesobiotics . The uprproteins . In a siegulated , consistegulated . Contamigulation , was dowgulation . Extractexposure . If a dyexposure .Ca3b, Cdkn1a, Nos2, Batf3, Mat1a, Tp63, Aldh1a1, Mgmt, Msh2 and Polk. This indicates that the expression of the ToxChip gene set is generally not sufficient to make confident predictions about the OFR content of these complex mixtures. Given that these samples did exhibit a relationship between OFR and LC50, it is possible that gene expression relationships might be identifiable if a larger gene set and sample size is used in future studies.We then used PLS modeling again to determine if any relationships between gene expression bioactivity and OFR concentrations could be identified. When we included all available expression data in the analysis, no obvious relationships were observed . However(i)Although the toxicological analysis conducted in this study is based on a unique data set representing average air concentrations from 19 megacities from around the world, information on the contaminant content of these samples is limited. The samples have so far been analyzed for one general class of contaminants—organic flame retardants—of which organophosphate flame retardants (OPFRs) were by far the dominant class. A future priority is to analyze the samples for a broader suite of contaminants, including for instance, polycyclic aromatic compounds, which could be especially relevant to urban population exposures and related biological responses. Transformation products of commercial chemicals such as the OPFRs that are abundant in air and formed through oxidation reactions have recently been shown to significantly contribute to exposure and risk assessment . This hi(ii)Although best efforts were made to collect samples that were “representative” of a large area from each city, it is likely that megacities have a degree of heterogeneity in terms of contaminant mixtures in air, depending on the location and proximity to sources. This introduces some uncertainty in the characterization and comparison of results among cities. Additionally, in the current study, contaminant analysis and toxicological assessment were conducted on consecutive three-month samples from the same site, which may have introduced additional uncertainty associated with temporal variability of contaminants in air. Future work should evaluate the degree of heterogeneity in biological response and chemical profiles for air collected in different parts of a city with differing land-use characteristics  and at different seasons. Some work on this topic has recently begun in Toronto ,42, but (iii)In this study, in vitro effects were determined using avian hepatic cells to provide a theoretical basis for the approach. Future work should explore other in vitro models  which could be more relevant for assessing human health risks. Although liver is a commonly used organ for toxicological studies, performing this assay with a broader range of cell types  would be warranted, especially given the negative health effects of air pollution associated with cardiovascular/respiratory endpoints ,44.(iv)The dose range used for cell viability and gene expression evaluation was limited . For example, Toronto had the lowest LC50 value  and the (v)Finally, gene expression data are based on a reduced transcriptome (43 genes), meaning that a large percentage of biological pathways/processes were not considered in the analysis.This study represents a first attempt to evaluate the ability and performance of an avian in vitro screening approach to assess effects (cytotoxicity and gene expression) of exposure to complex air extracts and to explore relationships between biological and chemical profiles for a particular class of chemicals, OFRs. We think this proof of concept of the general approach helps to advance the science. However, we acknowledge that there are some limitations of the study, which preclude a more in-depth evaluation of the in vitro indicators and the complex chemical mixture in the air samples, and these likely impacted the strength of relationship observed between the various endpoints evaluated . We thinOverall, we evaluated the utility of an avian in vitro screening approach to characterize the bioactivity of complex mixtures derived from air sampler extracts collected from diverse regions across the globe. Cytotoxicity results and transcriptomic profiles showed high variability between cities. OFR concentrations provided some ability to predict mixture LC50 values. However, we did not identify any convincing relationships between OFR burdens and gene expression. There were some consistent gene expression responses observed among broad geographic regions. Follow-up studies are warranted to expand the chemical space and include additional dose groups for quantitative dose-response evaluations of cell viability and gene expression. However, from these initial results, the avian in vitro screening approach shows potential to be a useful effects-based monitoring tool for evaluating effects of complex mixtures.


Adrenocortical carcinoma (ACC) is a rare malignancy with an overall unfavorable prognosis. Clinicians treating patients with ACC have noted accelerated growth in metastatic liver lesions that requires rapid intervention compared to other metastatic locations. This study measured and compared the growth rates of metastatic ACC lesions in the lungs, liver, and lymph nodes using volumetric segmentation. A total of 12 patients with metastatic ACC  were selected based on their medical history. Computer tomography (CT) exams were retrospectively reviewed and a sampling of ≤5 metastatic lesions per organ were selected for evaluation. Lesions in the liver, lung, and lymph nodes were measured and evaluated by volumetric segmentation. Statistical analyses were performed to compare the volumetric growth rates of the lesions in each organ system. In this cohort, 5/12 had liver lesions, 7/12 had lung lesions, and 5/12 had lymph node lesions. A total of 92 lesions were evaluated and segmented for lesion volumetry. The volume doubling time per organ system was 27 days in the liver, 90 days in the lungs, and 95 days in the lymph nodes. In this series of 12 patients with metastatic ACC, liver lesions showed a faster growth rate than lung or lymph node lesions. Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an estimated incidence of 1.5–2 per million people per year. It has a very poor prognosis with an overall 5-year mortality rate of 75–90% and an average survival from the time of diagnosis of 14.5 months . MetastaIn many cancers, the growth rate of metastases is used as a prognostic factor that guides treatment choice ,14,15. SThis study aimed to quantify the growth pattern and doubling time of metastatic ACC lesions to the liver, lung and lymph nodes using volumetric analysis of serial computer tomography (CT) images. Primary adrenal carcinomas were not evaluated in this study; rather, metastatic lesions in the major organ hosts in liver, lung, and lymph nodes were compared to investigate differences in growth rates across organ systems.A retrospective chart review at the National Institutes of Health identified 56 patients with a pathologic confirmation of primary ACC from 1999 to 2019. Consent was obtained to participate in an institutional review board (IRB)-approved investigational study at time of treatment. Additionally, the present analysis was also performed under another IRB-approved retrospective reporting protocol that met the criteria for waiver of further consent.An independent review of medical records by two investigators identified 56 patients with metastatic ACC. Forty patients who were on active systemic treatment were excluded from this study to exclude variations in treatment response. In total, 16 out of 56 patients were identified as not receiving systemic therapy during the evaluation window. A total of 5 of the 16 patients with metastatic lesions had a history of systemic therapy > 1 year preceding the evaluation window. Subject 1 had treatment consisting of PSC Velban, mitotane 3 years prior; subject 2 had records that indicated MAVE treatment from June 1999–Feb 2000, subject 4 had treatment consisting of tariquidar/MAVE  × 6 cycles 2 years prior to evaluation window and Gemcitabine and cisplatin × 6 cycles 1 year prior. Subject 10 was initially treated with cyclophosphamide, vincristine, and dacarbazine (CVD) × 6 cycles for presumed metastatic pheochromocytoma and then pembrolizumab (based on her history of Lynch syndrome and loss of expression for MSH-2 and MSH-6 in her tumor cells). She then presented to our institution and the diagnosis of ACC was confirmed and she received EDP and mitotane. However, this treatment was started after the measurement period .Four patients who were included in our study  received locoregional therapy during the interval of study; however, treated lesions were excluded from our growth rate evaluations. Eight patients were on mitotane. In all patients, mitotane levels were <6 µg/mL and since there were sub-therapeutic levels it is unclear, however, not likely, to have had an effect on the growth rate.18FDG-PET scans. 18FDG-PET is highly sensitive for detecting metastatic disease in ACC, yet only 3 in our cohort had the aforementioned scan because it was not widely available >15 years ago for patients undergoing treatment. Moreover, this imaging modality is underutilized in ACC patients since it is not routinely reimbursed by health insurance [18FDG-PET confirmation of ACC metastatic lesions and another patient had avid pulmonary lesions but non-avid liver lesions, therefore only the pulmonary lesions were included in the analysis. The third patient was excluded due to negative PET lesions. Ultimately, 12 patients  were included, with an average age of diagnosis at 50 years of age   . Eight pAxial soft tissue window series of chest/abdomen/pelvis CT scans with slice thickness of 2–10 mm was selected for evaluation. Portal venous phase CT series were used when available. A non-contrast axial CT series was used when the lesions were clearly evaluable, for example in the lung, and the radiologist was confident about the lesion’s boundary for segmentation. All lung lesions were evaluated with the standard lung window and kernel setting, and the rest of the lesions were measured and segmented in soft tissue series generated with a soft tissue kernel.All baseline and follow-up lesions were measured two-dimensionally and segmented using the Carestream picture archiving and communication system (PACS) 11.1 software . Lesion inclusion criteria were as follows: lesions in the liver and soft tissue with a long axis diameter of ≥10 mm, lymph node lesions having a short axis diameter of ≥10 mm, and pulmonary lesions having a long axis of ≥6 mm. Up to five lesions per organ system were recorded to provide enough sampling of the metastatic lesion growth rate .Volumetric segmentation was performed manually by tracing the lesion within each slice and was carried in Carestream PACS using the Livewire Segmentation Mode tool by a postdoc with 3 years’ experience in image processing (A.S.) and was supervised by a radiologist with 27 years of experience (H.B.). After finalizing the segmentation on each slice, the lesion volume was automatically calculated and saved in the PACS. Lesions were classified by the organ involved into 4 categories: Liver (L), Pulmonary (P), Lymph Node (N), and other locations (O).F-test. Additional F-tests were performed using age at diagnosis and sex as single factors included in the location model. Age was modeled as a covariate having a linear trend effect. Equal effects across locations were assumed. Additional analyses are included in the Because of the instability of doubling times in potentially slow-growing lesions, the growth rates (GR) were analyzed with an exponential (base 10) lesion growth model. A mixed model analysis of variance was used to analyze growth rates. The lesion location  was specified as a fixed effect, and patient by location was specified as the random effect. Deviations from expected values were graphically examined for normality and homoscedasticity; residual variance was partitioned because of heteroscedasticity (liver vs. lymph node and lung). The Satterthwaite approximation for the denominator degrees of freedom was utilized. Lesion growth rates and their paired differences were estimated with model-based LS-Means, and uncertainty was estimated with 95% confidence intervals (CIs for differences are Bonferroni adjusted). For the lesion effect only model, the null hypothesis of equal rates was assessed by an The final patient cohort included 12 patients, 6 males and 6 females: 5/12 had a total of 11 liver lesions, 7/12 had 27 lung lesions, 5/12 had 6 lymph node lesions, and 2/12 patients had other retroperitoneal recurrence (patients 6 and 7); 5/12 patients had two lesion locations . We usedF-test p = 0.025). Liver lesions had a CI of 0.177–0.504  and age , indicating a faster growth in females and the elderly.As secondary aims, we tested degrees of associations between growth rate as compared to sex and age at diagnosis, while adjusting for location  as those scans were old and thin section images were not available.Lesion selection bias may also have been a factor, since lesions requiring interventional radiology treatment during the chemotherapy-free window were necessarily excluded from our evaluation. Four patients fell in this category of receiving radiofrequency ablation or targeted therapy during the evaluation window. Excluding the more concerning lesions likely predisposed our data to reflect more conservative growth rates than would be seen in a general patient stream and to diminish the true differential of growth rates. Furthermore, although ablated lesions were excluded from our analysis, there is an unlikely possibility that the intervention affected the tumors of interest for this study in a spill-over effect on neighboring target lesions. This retrospective analysis used volumetric segmentation in CT examinations to assess the growth rates of ACC metastatic lesions to the liver, lung, and lymph nodes in 12 patients where systemic treatment was not indicated. Lesions in the liver grew the fastest, with an expected doubling time of 27 days as compared to the lungs or liver, which had doubling times of 90 and 95 days, respectively. Based on this limited observation, metastatic ACC liver lesions should be monitored closely for signs of rapid growth to guide us on treatment approaches.


Treponema pallidum subsp. pallidum, has an estimated 6.3 million cases worldwide per annum. In the past ten years, the incidence of syphilis has increased by more than 150% in some high-income countries, but the evolution and epidemiology of the epidemic are poorly understood. To characterize the global population structure of T. pallidum, we assembled a geographically and temporally diverse collection of 726 genomes from 626 clinical and 100 laboratory samples collected in 23 countries. We applied phylogenetic analyses and clustering, and found that the global syphilis population comprises just two deeply branching lineages, Nichols and SS14. Both lineages are currently circulating in 12 of the 23 countries sampled. We subdivided T. p.pallidum into 17 distinct sublineages to provide further phylodynamic resolution. Importantly, two Nichols sublineages have expanded clonally across 9 countries contemporaneously with SS14. Moreover, pairwise genome analyses revealed examples of isolates collected within the last 20 years from 14 different countries that had genetically identical core genomes, which might indicate frequent exchange through international transmission. It is striking that most samples collected before 1983 are phylogenetically distinct from more recently isolated sublineages. Using Bayesian temporal analysis, we detected a population bottleneck occurring during the late 1990s, followed by rapid population expansion in the 2000s that was driven by the dominant T. pallidum sublineages circulating today. This expansion may be linked to changing epidemiology, immune evasion or fitness under antimicrobial selection pressure, since many of the contemporary syphilis lineages we have characterized are resistant to macrolides.Syphilis, which is caused by the sexually transmitted bacterium  Treponema pallidum samples reveal globally circulating lineages linked to a rapid expansion occurring since the end of the twentieth century.Global syphilis prevalence has been increasing. Sequencing and analysis of a global collection of 726  Treponema pallidum subsp. pallidum (TPA), is a prevalent sexually transmitted infection that can cause severe long-term sequelae when left untreated. Historically, syphilis is commonly believed to have caused a large epidemic across Renaissance Europe, having previously been absent or unrecognized2. Although the origins of syphilis and the accurate dating of the most recent common ancestor of TPA are still the subject of debate6, it is suggested that the strains of TPA that persist in human populations today can be traced back to its introduction into Western Europe approximately 500 years ago, and its subsequent dissemination globally6.Syphilis, caused by the bacterium 7 without disappearing, until the 1980s and 1990s during the HIV/AIDS crisis when disease incidence declined markedly8, linked to community-wide changes in sexual behaviour, shifting of affected populations, AIDS-related mortality and widespread antimicrobial prophylaxis of HIV-infected populations. However, since the beginning of the twenty-first century, there has been a substantial resurgence in syphilis incidence14. In many countries, this has been associated with populations of men who have sex with men (MSM) engaging in high-risk sexual activity15. Transmission between MSM and heterosexuals is a particular concern due to the risk of in utero transmission to the fetus, leading to congenital syphilis16.Following the introduction of effective antibiotics after World War II, syphilis incidence fluctuated3. SS14-lineage strains represent the vast majority of published genomes4, and phylogenetic analysis showed that the origins of the SS14 lineage can be traced back to the 1950s3, followed by subsequent expansion of sublineages occurring during the 1990s4. Our understanding of the Nichols lineage is predominantly limited to laboratory strains from the USA, with relatively few clinical strains sequenced17. However, most TPA genomes published to date originate from the USA4, Western Europe18 and China20, and our understanding of the true breadth of diversity of TPA is incomplete21. Our view of the diversity of syphilis samples pre-dating the twenty-first century is even more limited, and these issues are partly explained by the fact that it has not been possible to culture TPA outside of a rabbit until recently22.Previous genomic analyses of TPA genomes have described two deep-branching phylogenetic lineages, ‘SS14’ and ‘Nichols’In this multi-centre collaborative study, we used direct whole genome sequencing (WGS) to generate a global view of contemporary syphilis from patients in Africa, Asia, the Caribbean, South America and Australia sampled between 1951 and 2019. Our dataset also includes a detailed analysis of the ‘within-country’ variation seen in TPA genomes in North America and Europe. We present evidence of globally spanning transmission networks with identical strains found in dispersed countries, indicating that, based on our data, TPA is genetically homogenous. Furthermore, we show that this genetic homogeneity is the result of a rapid and global expansion of TPA sublineages occurring within the last 30 years following a population bottleneck. This means that the TPA population infecting patients in the twenty-first century is not the same as that infecting patients in the twentieth century.26. After assessment for genome coverage and quality, we had a total of 726 genomes with >25% of genome positions at >5X coverage , which is sufficient for primary lineage classification. This dataset included 577 new genomes sequenced directly from clinical samples and 16 new genomes sequenced from samples passaged in rabbits.We performed targeted sequence-capture whole genome sequencing on residual genomic DNA extracted from diagnostic swabs taken from TPA PCR-positive syphilis patients and on TPA strains previously isolated in rabbits. We combined these data with 133 previously published genomesn = 1) and Zimbabwe (n = 18)), Scandinavia , Central Europe , Central Asia  and Australia (n = 5), as well as substantially increasing the sampling from North America  and Western Europe . We also included previously published genomes from South America , the Caribbean  and elsewhere. Due to a lack of long-term archived samples, 96.0% (n = 697) of samples were collected from 2000 onwards  were passaged in the in vivo rabbit model  of the genomes belonged to the SS14 lineage and 18.7% (n = 136) belonged to the Nichols lineage, and in the 12 out of 23 countries where both lineages were present, 80.3%  were of the SS14 lineage , Sweden (n = 7) and Russia (n = 10)   Fig. .6, we used Gubbins27 to infer a further 19 regions of putative recombination  threshold as previously described4, and evaluated their consistency using hierarchical clustering  and a mean coverage of 111X (range 11X–727X). This filtered dataset comprised 401 new and 127 published genomes (Supplementary Data n = 365), composed of closely related genomes present in 18 countries and 6 continental groupings . The oldest example of sublineage 1 was collected in 1981  and the most recent samples were from 2019  on the basis of temporal and geospatial divergence, and the independent evolution of different macrolide resistance alleles. However, by adding new genomes  and Canada (n = 4), indicating that this is not a geographically restricted group.From Fig. 019 Fig. . Sublinen = 8, also including a distantly related singleton from the US 1981 sublineage, TPA_USL-SEA-81-8) and the other within the Nichols lineage  and exclusively found in Zimbabwe. We also examined 10 genomes collected in the Tuva Republic, central Russia in 2013/2014, and these were distributed between three different SS14 sublineages . Sublineage 5 was found only in Tuva, while sublineage 3 was found throughout Europe , reflecting the predicted time of lineage diversification. However, our increased sampling also revealed two recent clonal expansions within the Nichols lineage: sublineage 14 (n = 55), comprising samples from Canada, Hungary, Spain and the UK , comprising samples from Australia, Canada, France, Ireland, the Netherlands, the UK and the USA , which occupy positions basal to all Nichols-lineage strains  and one singleton. Notably, the last sample belonging to this clade was collected in 1987 (TPA_USL-Phil-3). Hence, within our sampling framework, this appears to be an example of a historic lineage declining to rarity or even becoming extinct. More broadly, we note that although this cluster of both clinical and laboratory strains were all passaged to varying degrees through the rabbit model, other samples passaged in the rabbit model were distributed throughout the phylogeny and were present in 9 out of 17 sublineages  isolated in 1912 were included in our analysis . Figure 31 under a Strict Clock model with a Bayesian Skyline population distribution. We excluded 8 samples from strains known to be heavily passaged or with uncertain collection dates from the previous dataset of 528, giving a dataset of 520 samples and 883 variable sites. We inferred a median molecular clock rate of 1.28 × 10−7 substitutions per site per year (95% highest posterior density 1.07 × 10−7–1.48 × 10−7), which is equivalent to one substitution per genome every 6.9 years, consistent with recent analyses6.To infer temporal patterns within the global phylogeny, we performed Bayesian phylogenetic reconstruction using BEAST6. These data also showed that the common ancestor of these lineages was separated from recent samples by long phylogenetic branches and an absence of older ancestral nodes, suggesting unsampled historical diversity and that most contemporary TPA descended from much more recent ancestral nodes. We previously dated the common ancestors for clonal expansions of 9 SS14 sublineages between the 1980s and early 2000s4. With this expanded dataset, we focused on the major clonally expanded sublineages 1, 2, 8 and 14, each having at least 15 samples.Within the global TPA phylogeny Fig. , we obseNext, we used Bayesian Skyline analysis to determine the relative genetic diversity over different time periods in the phylogeny Fig. , and foun = 8), private to a country (n = 8) or multi-country (n = 9), and found that 20 out of 22 countries contained at least one multi-country sublineage , we found fewer than 26 (SS14 lineage) and 80 (Nichols lineage) pairwise SNPs separating genomes within any one country , correlation was lower for the SS14 lineage  and for the four largest multi-country sublineages   and four non-referring laboratories (n = 42) between 2012 and 2018. In BC, SS14 sublineage 1 dominated throughout the 18 year survey period, representing 82% of all BC genomes  and England, both of which have experienced a recent rise in syphilis incidence  found in BC were also present in England, as well as 4 additional sublineages  and one SS14 singleton strain not detected in BC  and English samples (n = 78) sharing zero pairwise SNPs over the core genome alignment for isolates collected between 2004 and 2019. To understand the effect of temporal distance, we compared both the pairwise SNP distance and the pairwise time distance (in years) between genomes from the same sublineage , with a mean of 4.9 SNPs (range 0–23) separating genomes from the same year and sublineage, compared with 7.8 SNPs (range 6–11) after 19 years  and singleton (5/8) sublineages were actually from Canada, the UK or the USA Fig. , suggestn = 2) contained a macrolide-resistant sample, suggesting that macrolide resistance is potentially linked to expansion in multi-country sublineages.Given our observations of individual sublineage expansion, we investigated whether the expansion could be related to antimicrobial resistance. Overlaying SNPs known to confer macrolide resistance  in the ribosomal 23S rRNA gene on the phylogeny showed evidence of macrolide resistance in 6 out of 9 multi-country sublineage expansions , but none of these genes is known to confer a fitness advantage that may distinguish the two lineages.We further explored the genomic differences between SS14 and Nichols lineages in our core genome alignment, using ancestral reconstruction to infer the common ancestral sequences of contemporary SS14 and Nichols lineages .30, and independently cultured and sequenced—within the same clade contradicts this hypothesis and may suggest that this clade represents the dominant TPA of the period. The complete absence of related genomes in contemporary sampling could represent a decline to becoming a rare or even extinct lineage, and therefore implies that the Nichols reference strain is not representative of contemporary syphilis or even contemporary Nichols-lineage strains.Our observation that the well-studied Nichols reference genomes  form an isolated clade that is not represented in contemporary TPA is important. One possible explanation is that these samples form a distinct clade due to convergent evolution in the rabbit model. However, this clade contains samples that are both extensively and minimally passaged, while other samples passaged in rabbits are distributed throughout the broader phylogeny and are included in three SS14 sublineages  and two additional Nichols sublineages . This indicates that passage in the rabbit model has not biased other parts of the phylogeny. The majority of Nichols-lineage strains collected before 1988 belong to this clade, and these samples mostly come from a small group of laboratories in the USA. Therefore, it is also tempting to suggest that this reflects a sampling bias for that time period. However, the phylogenetic placement of TPA_AUSMELT-1—isolated in 1977 in AustraliaOur data show that for some sublineages, modern syphilis is a truly global disease with shared lineages, sublineages and indeed nearly identical strains found all over the world. The large expansions of highly related genomes, in particular sublineage 1, represent the bulk of our dataset, and the widespread sharing of major lineages suggests that we have sampled from a series of globally contiguous sexual networks.32. Azithromycin and other macrolides are no longer recommended treatments at any stage of syphilis in the European syphilis management guidelines33. A further possibility is that partial host immunity plays a role in sublineage dynamics, as suggested by previous modelling of aggregated national surveillance data34 and reported attenuated symptoms on reinfection36; this will require detailed investigation of well-characterized sexual networks and examination of hypervariable antigenic genes excluded from the core genome used in our phylogenetic analysis.Furthermore, we find evidence of a striking change in the genetic diversity and effective population size of TPA genomes, suggestive of a possible population bottleneck occurring between the late 1990s and early 2000s. This was followed by a rapid expansion of certain sublineages, leading to the contemporary TPA population structure. This bottleneck, potentially a consequence of post-HIV safe-sex messaging, persistent antimicrobial prophylaxis in at-risk HIV-positive populations, and possibly HIV-associated mortality, appears to have led to a striking duality in the TPA dominating populations before and after its occurrence. The rapid expansion may be attributed to a relaxation in sexual behaviour following the widespread introduction of highly active antiretroviral therapy. Notably, although macrolide resistance was neither universally distributed throughout the phylogeny nor present in all sublineages, most of the multi-country sublineages were largely macrolide resistant, and this could have also played a role through off-target effects, for example during treatment of other  infections22 and the lack of long-term storage of clinical swabs. Most  older strains come from the USA, and this could mean that we do not accurately reflect the global population structure before 2000. Moreover, because of limited sampling before 2000, historic lineages that lack extant descendants (for example due to extinction) would not be modelled by our Bayesian Skyline, and this could limit our estimates of historic population diversity.Our study has a number of limitations. Our samples were collected in an opportunistic manner using residual samples available in regional or national archives. Since our sampling coverage was uneven, with some countries either missing or undersampled, it was not possible to infer the direction of transmission between countries. While sampling was particularly limited in Africa, Asia and South America, we still provide a snapshot of strains from these regions, all of which overlap with the genetic diversity of our more deeply sampled regions (North America and Europe), suggesting that we have captured dominant global lineages. We also show that even deeply sampled countries can harbour rare sublineages, and it is therefore likely that future studies will reveal further novel diversity. Samples collected before the widespread adoption of molecular diagnostics in the early 2000s are limited here, and this is largely influenced by the difficulty in isolating new strains before the recent development of in vitro cultureT. pallidum infecting patients today is not the same T. pallidum infecting patients even 30 years ago – ancestral sublineages may have become extinct, being replaced by new sublineages that have swept to dominance across the globe with the dramatic upswing in syphilis cases in the USA, the UK and other Western European countries, which were heavily sampled in our study. That such a bottleneck is linked to HIV-related behavioural change during the 1990s rather than the introduction of antibiotics after the Second World War, further supports the importance of sexual behaviour in transmission dynamics. In future work, it would be interesting to integrate epidemiological evidence of sexual networks in purpose-designed cohort studies to explore this further.Despite these limitations, our data show that the Overall ethical approval for receipt, handling and sequencing of all clinical samples, as well as for use of UK samples collected as part of public health surveillance and for research was granted by the London School of Hygiene and Tropical Medicine Observational Research Ethics Committee (REF No. 16014) and the National Health Service (UK) Health Research Authority and Health and Care Research Wales . Samples were de-identified and not linked to any personal identifiable information. As no patient contact took place, no change to clinical care occurred, and as the study consisted only of the use of residual DNA from samples that were already routinely collected, patient consent was deemed unnecessary during ethical approval. Ethical approval for sequencing of samples from Belgium was covered by a provision of the Institutional Review Board of the Institute of Tropical Medicine that allows the further characterization of residual patient samples without additional Ethical Committee clearance. In addition, at the Institute for Tropical Medicine outpatient clinic, the patients are informed that their remnant samples may be used; if they do not consent, they have a form to complete (opt out). Samples from Hungary were collected and preserved as part of the routine diagnostics (standard care), and stored at laboratories that have approval for preservation of such and other clinical samples, and no patient identification information was available; accordingly, these samples did not need a separate ethical approval for use in an anonymized manner. Samples from Russia were collected as part of a previous study that involved molecular epidemiology, and this had ethical approval from The State Research Center of Dermatology, Venereology and Cosmetology of The Russian Ministry of Health (SRCDVC), Moscow, Russia. Samples from South Africa were collected as part of a study on the impact of episodic acyclovir therapy on ulcer duration and HIV shedding from genital ulcers in men, and ethical approval was granted by the Human Research Ethics Committee of the University of the Witwatersrand in South Africa (Clearance Certificate Nos. M040548 and M10201). All participants gave permission to store samples for future testing for infectious diseases. Zimbabwe samples were collected as part of the Zimbabwe STI Etiology Study, which had a provision for specimen storage and future studies, and the consent form had a specific opt-in/opt-out addendum for specimen storage and future studies. It also specifically asked for consent to have specimens shipped to the National Institute for Communicable Diseases in South Africa. The protocol and consent forms were approved by the Research Council and Medical Research Council of Zimbabwe. Samples from Canada (British Columbia and Alberta) were collected as part of public health surveillance, were de-identified before transfer between labs and were deemed exempt from requiring additional ethical approval. Samples from Australia were covered by Human Research Ethics Committee approval, and this approval included a waiver to obtain individual informed consent that was consistent with the requirements outlined in the Australian National Health and Medical Research Council National Statement. For samples from Spain, all the patients enrolled in the study provided written consent for collection of an additional ulcer swab and/or whole blood specimen to perform the TPA molecular studies. Institutional Review Board approval PR(AG)297/2014 was obtained from the Ethics Committee of Vall d’Hebron Research Institute. An amendment was also approved to allow WGS. For samples from Ireland, the study was approved by the ethics board of St James’s Hospital and Tallaght Hospital, and this included approval for molecular analyses.4.Novel samples from Australia , Belgium (Antwerp), Canada , Hungary , Ireland (Dublin), Russia (Tuva Republic), South Africa (Johannesburg), Spain (Barcelona), Sweden , the UK  and Zimbabwe (3 regions) were sequenced directly from genomic DNA extracted from clinical patient swabs or biopsies, typically utilizing de-identified residual diagnostic samples that were further pseudonymized before analysis. Additional novel samples from Australia (Melbourne), Haiti and the USA (6 cities) were sequenced from historic freezer archives after previous passage in the rabbit model4, and grouped into pools of either 32 or 48 with similar (within 2 CT) treponemal load, with high-concentration outlier samples diluted as necessary. We added 4 μl pooled commercial human genomic DNA (Promega) to all samples to ensure total genomic DNA > 1 μg per 35 μl, which was sufficient for library prep.DNA extracts were quantified by quantitative real-time PCR  as previously described37. Enriched libraries were sequenced on Illumina HiSeq 4000 to generate 150 bp paired-end reads at the Wellcome Sanger Institute (UK) as previously described38. For one rabbit-passaged sample from Melbourne, Australia (TPA_AUSMELT-1)30, genomic DNA extracted from historically archived tissue lysate was sequenced on Illumina NextSeq 500  without any previous enrichment to an estimated 1 Gb per sample at the Doherty Institute .Extracted genomic DNA was sheared to 100–400 bp (mean distribution 150 bp) using an LE220 ultrasonicator (Covaris). Libraries were prepared  using initial adaptor ligation and barcoding with unique dual indexed barcodes (Integrated DNA Technologies). Dual indexed samples were amplified , quantified , then pooled in pre-assigned groups of 48 or 32 to generate equimolar pools on the basis of total DNA concentration. Pooled DNA (500 ng) was hybridized using 120-mer RNA baits Treponema genus-specific sequencing reads using the full bacterial and human Kraken 2 (ref. 39) v2.0.8 database (March 2019), followed by trimming with Trimmomatic40 v0.39 and downsampling to a maximum of 2,500,000 using seqtk v1.0 (available at https://github.com/lh3/seqtk) as previously described4. For publicly available genomes, raw sequencing reads were downloaded from the European Nucleotide Archive and subjected to the same binning and downsampling procedure. For five public genomes (Supplementary Data https://github.com/sanger-pathogens/Fastaq).We filtered  March 209, followTreponema-specific reads to a custom version of the SS14_v2 reference genome (NC_021508.1) after first masking 12 repetitive Tpr genes (Tpr A–L), two highly repetitive genes  and five FadL homologues  using bedtools41 v2.29 maskfasta , followed by indel realignment using GATK v3.7 IndelRealigner, de-duplication with Picard MarkDuplicates v1.126 (available at http://broadinstitute.github.io/picard/), and variant calling and consensus pseudosequence generation using samtools43 v1.6 and bcftools v1.6, requiring a minimum of two supporting reads per strand and five in total to call a variant, and a variant frequency/mapping quality cut-off of 0.8. Sites not meeting our filtering criteria were masked to ‘N’ in the final pseudosequence. After mapping and pseudosequence generation, we repeated the masking of the 19 genes on the final multiple sequence alignment using ‘remove_block_from_aln.py’ available at https://github.com/sanger-pathogens/remove_blocks_from_aln/ to ensure sites originally masked in the reference were not inadvertently called with SNPs due to mapped reads overlapping the masked region. These 19 regions of recombination and genomic uncertainty due to gene orthology or repetitive regions6 accounted for 30,071 genomic sites . We used Gubbins27 v2.4.1 (20 iterations) to generate recombination-masked full genome length and SNP-only alignments. Gubbins27 identified 19 further putative regions of recombination affecting 2.1% of genomic sites  and 27 genes  and used this to determine phylogenetic clusters with a 10 SNP threshold in rPinecone29 v0.1.0 . We further investigated phylogenetic clustering by using IQ-tree to generate 100 standard non-parametric bootstraps on the maximum likelihood phylogeny and used the resulting 100 trees as independent inputs to rPinecone, as described in the rPinecone manuscript29. We used the hierarchical clustering ‘hclust’ algorithm in R48 to group rPinecone clusters and evaluated different proportions of trees supporting clusters against the phylogeny  and an Uncorrelated Relaxed Clock model53, with HKY substitution model54 and diffuse gamma distribution prior55  over 100 million Markov Chain Monte Carlo (MCMC) cycles with 10 million cycle burn-in. We evaluated constant, relaxed lognormal, exponential and Bayesian Skyline (10 categories) population distributions56. All MCMC chains converged with high effective sample sizes, and on inspection of the marginal distribution of ‘ucld.stdev’, we could not reject a Strict Clock. We used the marginal likelihood estimates from the triplicate BEAST runs as input to Path Sampling and Stepping Stone Sampling analysis58, and this suggested that the Strict Clock with Bayesian Skyline was the optimal model for this dataset  would be expected to increase this rate. To ensure that our findings were not artefactual to the downsampled dataset, we re-stratified the dataset by sublineage, country and year, selecting a maximum of 3 genomes from each stratum, plus all singleton strains, yielding a dataset of 168 genomes with 466 variable sites. We ran BEAST on this new subsampled dataset using the optimal Strict Clock  with Bayesian Skyline from over 100 million MCMC cycles, with the equivalent results  after evaluating temporal signal  in a BEAST259 v2.6.3 implementation with BEAGLE51 libraries optimized for Graphical Processing Units, analysing the 520 genomes over 500 million MCMC cycles in triplicate. To compare the phylodynamics of the individual Nichols and SS14 lineages, we repeated the BEAST2 analysis described above using multiple sequence alignments specific to Nichols (n = 94) and SS14 (n = 426); we used Tracer31 v1.7.1 to extract Bayesian Skyline distributions and lineage accumulation for plotting in R.To analyse the full dataset , which uses the BEAST log and tree files to identify the first increase in relative genetic diversity from the PopSizes columns and the date of this increase using the corresponding number of nodes in the GroupSizes columns and the node heights in the respective tree. We required a 2-fold population expansion (defined by setting ‘-p’ to 100). The script outputs the proportion of trees in the posterior distribution that support an increase in relative genetic diversity, along with the distribution of expansion dates, which we plotted in R48. We repeated this analysis using the script ‘population_change_support_BEAST.py’ (available at https://github.com/chrisruis/tree_scripts), which looks for an increase or decrease of effective population size within a defined window, testing for supported start dates of a 2-fold population decline or expansion between 1990 and 2015.We used logcombiner v2.6.3 with a 10% burn-in to generate consensus log and tree files, resampling 100,000 states for the full 520 sample analysis, and treeannotator v1.8.4 to create median maximum credibility trees. We generated Bayesian Skyline and lineage accumulation plots using the combined log and tree files in Tracer v1.7.161 v0.7.4. We extracted SNPs from the resulting multiple sequence alignment using snp-sites, functionally annotated variants using SnpEff62 v4.3 with the most recent National Center for Biotechology Information annotation for the NC_021508.1 SS14 reference genome (June 2021), and imported data into R48 v3.6.0 for analysis using vcfR63 v1.12.0. We selected the common ancestral nodes leading to contemporary SS14 and Nichols lineages  and British Columbia .For comparison of TPA sublineage trends with national syphilis rates, we downloaded and plotted publicly available incidence data for England . To infer pairwise SNP distances between samples, we used pairsnp v0.1.0 (available at https://github.com/gtonkinhill/pairsnp commit: 0acddba060cc076946dab9969a95ab3c21f110fb). We constructed networks of minimum pairwise distance and shared lineages in R, and plotted these as heatmaps using ggplot264 v3.3.2. Nucleotide diversity (π) for different clades was inferred from the multiple sequence alignments using EggLib65 v3.0.0b21, including variable sites present in at least 5% of genomes. For geospatial analysis, we used the CoordinateCleaner66 v2.0-17 package in R to define the centroid position for each country, except for Russia (where we used the centroid of the Tuva Republic) and Mexico (where we used Mexico City). Geographic distances between countries (using the country centroid or location defined above) were determined using the ‘distVincentyEllipsoid’ function from the geosphere67 v1.5-10 package. Correlations between pairwise genetic, geographic and temporal distance were inferred using two-sided Pearson’s R Correlation via the ‘cor’ function in R, where we compared ‘real’ correlation with 1,000 bootstraps resampled with replacement to obtain a P value. Sample counts were plotted using ggmap68 v3.0.0 over maptiles downloaded from Stamen Design (http://maps.stamen.com). All phylogenetic trees were plotted in R using ggtree69 v2.5.1. All figures used ggplot2 (ref. 64) for plotting and multi-panel figures were constructed using cowplot70 v1.1.1.Macrolide resistance alleles were inferred using the competitive mapping approach previously describedFurther information on research design is available in the Supplementary InformationSupplementary Figs. 1–13 and Discussion.Reporting SummaryPeer Review InformationSupplementary Data 1–4Supplementary Data 1–4 .


Methanosarcina, only the presence of ferrihydrite enriched for iron reducing bacteria Geobacter. Additionally, the presence of ferrihydrite continued to influence microbial community development after the onset of methanogenesis, with the dissimilarity between communities growing in ferrihydrite compared to no-Fe-added controls increasing over time. This work improves our understanding of how the presence of different iron oxides influences microbial community composition and methane production in soils and sediments.Quantifying the flux of methane from terrestrial environments remains challenging, owing to considerable spatial and temporal variability in emissions. Amongst a myriad of factors, variation in the composition of electron acceptors, including metal (oxyhydr)oxides, may impart controls on methane emission. The purpose of this research is to understand how iron (oxyhydr)oxide minerals with varied physicochemical properties influence microbial methane production and subsequent microbial community development. Incubation experiments, using lake sediment as an inoculum and acetate as a carbon source, were used to understand the influence of one poorly crystalline iron oxide mineral, ferrihydrite, and two well-crystalline minerals, hematite and goethite, on methane production. Iron speciation, headspace methane, and 16S-rRNA sequencing microbial community data were measured over time. Substantial iron reduction only occurred in the presence of ferrihydrite while hematite and goethite had little effect on methane production throughout the incubations. In ferrihydrite experiments the time taken to reach the maximum methane production rate was slower than under other conditions, but methane production, eventually occurred in the presence of ferrihydrite. We suggest that this is due to ferrihydrite transformation into more stable minerals like magnetite and goethite or surface passivation by Fe(II). While all experimental conditions enriched for  Methane is an important greenhouse gas, possessing a global warming potential about 28 times greater than carbon dioxide over a 100-year period . In natu3) (2O3) (3O4), a mixed Fe(II)/Fe(III) mineral, can be produced as a byproduct of ferrihydrite reduction (2+(aq) (Iron(III) (oxyhydr)oxide minerals, hereafter referred to as iron oxides, are widely found in environmental systems . These m3) , goethit3) , and hem3) (2O3) . Additioeduction , and be  (2+(aq) . Due to  (2+(aq) . This ca (2+(aq) .Competition between microbes possessing different metabolic capabilities, like iron reduction or methanogenesis, has been summarized in terms of a “thermodynamic ladder” of terminal electron acceptors . When miIndeed, many studies have shown that iron reduction outcompetes methanogenesis in wetlands and soils when iron is present as a poorly crystalline iron oxide . HoweverThe influence of iron oxide crystallinity on competition between iron reduction and methanogenesis has already been studied under a number of conditions. Work by Furthermore, in some environments carbon may not be the limiting factor for iron reduction, such as in the soils of the Artic Coastal Plain  and a laAccordingly, the goal of this study is to understand how different iron oxide minerals affect methane production by influencing competition between iron reducing bacteria and methanogens under conditions not limited by electron donor (acetate). Both chemical and microbial community analysis are used to understand the balance between methane production and iron reduction in the presence of poorly crystalline ferrihydrite compared to well-crystalline hematite and goethite. Thermodynamic calculations under a range of acetate concentrations are used to predict conditions under which iron reducers should outcompete methanogens, and the resulting predictions are compared to the chemical and microbial community results obtained in incubation experiments. The results of this work improve our understanding of interactions between iron-reducing bacteria and methanogens, and consequently methane and iron cycling, in natural environments, including soils and sediments.rxn for each considered metabolic reaction was tabulated based on published ΔG°f values for all reactants and products. These values are summarized in rxn, at 298K and 1 M activities of all reactants and products, is calculated based on Equation 1.The standard state ΔG°rxn values were then converted to ΔGrxn values at specific environmental conditions using Equation 2.These ΔG°R = 8.314 J*mol–1*K–1, T = 298K and Q is the reaction quotient calculated based on the reaction in Equation 3 (where A and B are reactants and C and D are products) using Equation 4.where, This was done for a number of acetate consuming reactions, including iron reduction and methanogenesis, as listed in 36H2O into 3 L of deionized water. The pH of this solution was titrated periodically to pH 8 for about 2 h.Iron oxide minerals were prepared using methods previously described . The autAfter syntheses were complete, each mineral was rinsed to remove sorbed electrolytes. This was achieved through centrifugation at approximately 3000 RPM for 30 min, followed by decantation of the supernatant and replacement with deionized (>18 MΩ) water, and vigorous shaking to resuspend the mineral pellet. This process was repeated three to five times. The color of hematite and goethite, and the HCl-extractable Fe concentrations from the first time point of the incubation experiments  is shown in Experiments were inoculated with sediments obtained from Upper Mystic Lake, a eutrophic, dimictic, kettle hole lake in Arlington, MA, United States. This lake sediment was chosen because it has known methane ebullition in the sediment . MethaneSediments were collected from the southwest side of the lake on 26 July 2018, as shown in 4Cl, 1mM KH2PO4, 0.5 mM MgCl2, 0.5 mM CaCl2, 5mM NaHCO3, 10mM HEPES, 0.05% Bacto yeast extract, 64 μg/L Na2WO4, and 1mL/L of a trace element solution . Experiments were also supplemented with 10 mM sodium acetate as an electron donor and carbon source. Hematite, goethite, or ferrihydrite was added to experiments with the exception of the control bottles in amounts equivalent to 10 mM as Fe(III). The pH of each experimental setup was adjusted to 7.00 (±0.05) using hydrochloric acid. Total solution volumes in all experiments were 200 mL.Incubations were prepared in triplicate in 500 mL serum bottles (Wheaton) sealed with butyl septa and aluminum crimp seals, and bottles were covered with aluminum foil to keep experiments dark. All incubations contained 1mM NH2 and H2 [98.5:∼1.5 (v/v)], and H2 was purposely kept at the minimum concentration recommended by the manufacturer to limit hydrogen introduction into the incubation experiments. A visual representation of the sampling process discussed in the following sections is shown in Before inoculation, all bottles were sparged with nitrogen gas for 30 min. Incubations were inoculated with approximately 0.5 mL of wet sediment to serve as a natural methanogenic microbial community from Upper Mystic Lake (UML). This was done in an anaerobic glove bag using a needle and syringe into the sealed serum bottles. The bottles were then incubated with shaking at 27°C for about 30 days. Time series data on methane and iron speciation was collected throughout the 30 days. DNA was collected for 16S rRNA community analysis after 9, 18, and 29 days. All sampling was conducted in an anaerobic glove bag with an atmosphere of NAll gas measurements were made using a residual gas analyzer mass spectrometer/membrane inlet mass spectrometer (RGA-MS/MIMS)  in the RGA configuration. Measurements were made using the Faraday detector and the MASsoft software was used in multiple ion detection (MID) mode. Sample tubing connected to the RGA instrument was extended into the glove bag through a port, so serum vial headspace sampling could be performed within the glove bag. An 18-gauge needle was attached to the end of the sample tubing and pierced through the serum bottle septum for each sample measurement.m/z of 2 (H2), 15 (CH4), 16 (CH4/O2), 18 (H2O vapor), 28 (N2), 32 (O2), and 44 (CO2) were measured in each sample. Samples were measured for approximately 3 min, with the instrument producing values approximately every 15–20 s. After 3 min, the readings were typically stable for at least the last few measurements. Thus, the last five readouts were averaged to determine the m/z signal composition for each particular sample. All sensitivity factors were set to 1 and calibration was performed manually.To measure as much of the total gas in the sample as possible, 4 and CO2 relative to N2. CH4, CO2, and N2 made up 96% or more of the total observed signal in all samples. Ultra-high purity methane and food-grade carbon dioxide mix (10% CO2/90%N2) were diluted with industrial-grade N2 to produce standards. For CH4, m/z of 15 was used for all calibrations while m/z of 44 was used for CO2. Resulting linear calibration curves were used in place of the sensitivity factors provided by the instrument manufacturer and used to correct the measured ratios of CH4:N2 and CO2:N2 to actual ratios in each sample. The fractions of CH4 and CO2 in the headspace were then determined using the following equations:Calibrations were performed for CHCH4 is the fraction of CH4 in the headspace, FCO2 is the fraction of CO2 in the headspace, RCH4 is the measured calibration-corrected ratio of CH4:N2, and RCO2 is the measured calibration-corrected ratio of CO2:N2 These fractions were then converted into moles methane produced at each time point as follows. Because it could not be determined directly, it was assumed that the total pressure in the headspace was 1 atm (which is likely an underestimate due to production of CO2 and CH4). Thus, the fraction of methane was assumed to be the methane partial pressure in atm. The partial pressure was then converted into moles using the ideal gas law at 298 K. The headspace was assumed to be the initial headspace at T0 (300 mL) plus the additional headspace created by withdrawing ∼6 mL of liquid sample at each time point. This value is the moles of methane present in the sample at the time of sampling.where, F0), only the methane present at that time was used as total methane produced since no sample had been previously withdrawn.Because RGA sampling withdrew a substantial amount of gas from the serum vial headspace, a correction was calculated to account for methane removed during previous samplings. A sample volume withdrawal rate could not be measured directly but was estimated using a 1 mL syringe. The amount of time it took to sample an entire 1 mL syringe was determined to be 26 s. Thus, for a total sampling time of 3 min, approximately 7 mL of sample was consumed. For each measurement, the total moles of methane present were added to the moles of methane in 7 mL of the headspace as determined in the measurement, plus any methane removed in previous time points calculated in the same way. At the first time point  for HCl-extractable Fe analyses in centrifuge tubes. This sample was filtered after approximately 1 hour using 0.22 μm PES syringe filters into new centrifuge tubes. The remaining sample was filtered immediately at the time of sampling and 1.5 mL of this filtered sample was added to 1.5 mL of 4% nitric acid for aqueous Fe analysis. The remaining filtered sample was placed into a freezer to be preserved for acetate measurement.HCl-extractable Fe(II) and total HCl-extractable Fe concentrations were measured using the Ferrozine method  on a UV-The remaining filtered liquid sample (∼1.5 mL) was used for acetate analysis by ion chromatography . Samples were analyzed using AS11-HC-4m analytical and guard columns for anion analysis with potassium hydroxide as the eluent. Peak area determination was performed using the Chromeleon software. Calibration curves based on peak areas in standards were fit according the form in Samples for 16S rRNA analysis were taken at three time points. Six mL of each incubation replicate sample was set aside for immediate DNA extraction after sampling. Sampling was performed 9, 18, and 29 days after the start of the experiment. Three mL of wet sediment was used to determine the microbial composition of UML sediment. DNA extractions were performed using the PowerSoil (Qiagen) DNA extraction kit following the manufacturer’s instructions for “wet soil.” Accordingly, the samples were centrifuged at 10,000 g in 1.5 mL microcentrifuge tubes to remove excess water, adding 1.25 mL at a time and then discarding supernatant before adding another 1.25 mL of sample and centrifuging with discarding supernatant again until the entire sample (6 mL for incubations and 3 mL for UML sediment) was processed.Amplicon libraries were prepared using methods described in All statistical analyses on 16S rRNA ASV data were carried out in R (version 3.5.1) . Analysirxn (decreasing favorability) with increasing [Fe2+(aq)]. Acetoclastic methanogenesis also has an increasing ΔGrxn with increasing [CH4(aq)]. Changing acetate activities by orders of magnitude  has no effect on the relative favorabilities of each reaction ] increases.When considering acetate as the sole electron donor, thermodynamic calculations predict that ferrihydrite is the most energetically favorable iron oxide electron acceptor followed by goethite and hematite, as shown previously . The rea2+ higher than ∼10 uM, ferrihydrite reduction is always more favorable than methanogenesis, followed by goethite reduction and hematite reduction, with magnetite reduction being least favorable. An order of magnitude change in[(Fe2+(aq)] leads to a much greater change in ΔGrxn than an order of magnitude increase in acetate or decrease in methane concentrations. The ΔGrxn for acetotrophic iron reduction increases by +45.6 kJ/mol acetate oxidized with a one order of magnitude increase in iron, whereas the ΔGrxn only increases by +5.7 kJ/mol acetate oxidized with a one order of magnitude decrease in acetate activity. The ΔGrxn for methanogenesis increases by +5.7 kJ/mol acetate for an order of magnitude increase in [CH4(aq)] or an order of magnitude decrease in acetate activity.At activities of FeMethane was measured to determine the timing of the onset of methanogenesis and rates of methane production. In all experiments, the partial pressure of headspace methane increased throughout the length of the 30 days following a S-growth curve-like shape . Under aMaximum methane production rates were similar across all experiments at about 0.2 to 0.3 mmol per day ; howeverThe relative consumption rate of acetate was similar to that of methane production in all samples . AcetateIron concentrations and speciation were measured over time to understand iron reduction. HCl-extractable Fe(II) concentrations increased the most in ferrihydrite incubations, despite hematite and goethite experiments having stoichiometrically equivalent amounts of oxidized iron added at the onset of the experiments . HCl-extThe solid phase in the ferrihydrite incubations also changed over time . The aci16S rRNA amplicon sequencing was used to determine the composition of the microbial communities in response to the different iron oxide amendments within the incubations over time. DNA extractions were done at 9, 18, and 29 days into the experiments. These experimental samples were compared to samples collected from Upper Mystic Lake (UML) sediment, which was used as the inoculum for these experiments. After processing, there were 6,388,081 sequences in total in the 38 samples. These sequences were associated with 7720 ASVs. The UML sediment had a much higher diversity than the incubation samples; the UML samples had on average 4075 ASVs (± standard deviation of 133 ASVs) while the incubation samples contained 1060 ASVs on average (± standard deviation of 180 ASVs) , with liAt the phylum level , all incp-values of < 0.01. In contrast, all other pairwise group comparisons had p-values of > 0.01, and all except goethite-ferrihydrite had p-values > 0.05.Principle component analysis (PCoA) on the Bray-Curtis dissimilarity matrix of the experimental samples at the ASV level consisted of two distinct clusters . One cluGeobacter substantially increased above the initial UML sediment relative abundance in ferrihydrite samples. After a large initial increase in Geobacter abundance between initial UML sediment and ferrihydrite incubations at the day 9 time point, the relative abundance of Geobacter decreased in ferrihydrite incubations until day 29. In all incubations without ferrihydrite, the relative abundance of Geobacter was similar to the UML sediment.Relative abundances of genera within the phyla Proteobacteria and Euryarcheota were explored in more detail to provide insight into the microbes carrying out Fe(III) oxide reduction and methanogenesis, respectively. Methanosarcina after 18 days with an ingrowth of Methanoregula in all incubation samples except for the ferrihydrite incubations after 29 days. In contrast, the most prominent methanogens in the initial UML sediment were Methanomassillicoccus, Methanoregula, and Methanothrix, which all decreased in relative abundance from a total of about 7% in UML sediment to <1% by day 9 in every incubation sample.At day 9, ferrihydrite incubations had the lowest relative abundance of methanogens compared to hematite, goethite, and Fe oxide-free control samples. The relative abundance of Euryarcheota increased substantially across all samples from ≤ 3% at day 9 to over 10% on average by day 18. Interestingly, at day 18 and day 29, ferrihydrite incubations contained a higher relative abundance of Euryarchaeota than all other experimental samples. In all incubations, Euryarcheota consisted of predominantly DESeq2 was used to determine which ASVs were significantly differently abundant over time and across experimental conditions. First, all mineral and control incubations were analyzed based on which ASVs significantly changed over time in order to contextualize comparisons across experimental conditions. For each experimental condition, sequencing data from the two later time points (Day 18 and Day 29) were compared to the corresponding data at Day 9. Overall, ferrihydrite-amended communities were most stable across the three time points, with only 25 ASVs that significantly changed between day 9 and day 29, compared with ∼100 ASVs that changed significantly under all other experimental conditions. ASVs that were significantly different between Day 9 and later time points that belong to the phyla Proteobacteria and Euryarchaeota are shown in The changes in relative abundance between apparent iron reducers and methanogens occurred at different times throughout the course of the incubation experiments. There were no significantly different ASVs belonging to the family Geobacteraceae from days 18 and 29 compared to day 9, indicating that Geobacteraceae abundances stayed generally consistent across all three time points. In contrast, some of the most abundant Methanosarcinaceae ASVs showed significant differences between day 9 and days 18 and 29. For example, ASV00403 abundance significantly increased over time across all experimental conditions. However, ASV00405, which on average was the most abundant ASV of methanogen, only increased significantly after day 9 with the addition of ferrihydrite. Most methanogen ASVs with significant changes increased by day 29, except for two less abundant Methanosarcinaceae ASVs (ASV00398 and ASV00400) that decreased in relative abundance.Differential abundance data were also examined across conditions by comparing each mineral incubation to the control incubations at each time point in order to determine what was driving the differences between PCoA clusters . In accoProteobacteria and Euryarchaeota ASVs that were significantly differently abundant compared to control incubations are shown in Methanogen relative abundances generally only showed significant differences between ferrihydrite and control experiments, and these differences mostly appeared after Day 9. The number of methanogen ASVs that were significantly different from control experiments increased throughout the course of the incubation from two, both belonging to the family Methanobacteriacaeae and at lower relative abundance than controls, at day 9 up to a total of seventeen at day 29. Specifically, the presence of ferrihydrite specifically enriched for ASVs in the Methanosarcinaceae family, with one ASV (ASV00405) being especially abundant in ferrihydrite relative to all other experimental conditions, while all other methanogen families (including Methanobaceriaceae and Methanospirillaceae) were found in significantly lower abundance relative to control experiments. In contrast, three or fewer ASVs of methanogens were significantly different between goethite or hematite and control experiments at every time point.Methanosarcina ASV00405, and four ASVs in the Geobacteraceae family, including ASV03812.Heat map plots of the top 100 ASVs across all samples , using PWhile incubations with hematite, goethite, and ferrihydrite had equivalent amounts of Fe(III) added, only ferrihydrite was substantially reduced over the course of these experiments. Iron reduction proceeded readily in the presence of ferrihydrite using acetate as an electron donor, reaching mM concentrations of acid-extractable Fe(II) and aqueous Fe. However, acid-extractable Fe(II) concentrations remained about an order of magnitude lower in goethite and hematite incubations, indicating a much smaller extent of Fe(III) reduction than in ferrihydrite experiments. These trends in relative reduction amounts agree well with previous investigations of Fe(III) reduction in the presence of different Fe oxide minerals .Geobacter abundance relative to all other incubation conditions, with four ASVs being significantly higher in relative abundance compared to control incubations across all three time points. The relative abundance of Geobacter reached a maximum by day 9 of the incubation experiments, which is also approximately the point when iron reduction stopped. The relative abundance then decreased over time; however, this decrease was not statistically significant for any individual ASV of Geobacter.Iron reduction in ferrihydrite incubations corresponded with an increase in Thermodynamic predictions of iron reduction agree well with incubation experiments. Since these experiments were done in artificial freshwater , the concentrations of aqueous constituents are roughly equal to activities  activities are as high as 10–2, ferrihydrite reduction is always more favorable than methanogenesis. At very low Fe2+(aq) activities , hematite and goethite reduction are also more favorable than methanogenesis. However, due to reaction stoichiometry; 8 moles of Fe2+ are produced for every 1 mole of acetate consumed while only 1 mole of CH4 is produced per mole acetate consumed  activities. This results in methanogenesis being favorable over a wider range of methane activities compared to hematite and goethite reduction over a range of Fe2+(aq) activities. Therefore, methanogenesis is expected to dominate over iron reduction across a range of environmentally relevant conditions when hematite and goethite are the available electron acceptors, but ferrihydrite reduction remains more favorable than methanogenesis even at high Fe2+(aq) activities.Thermodynamic predictions also explain how microbial ferrihydrite reduction outcompetes methanogenesis over a wider range of environmental conditions than hematite and goethite reduction. For conditions where Feconsumed . Thus, tAcetate concentrations also varied over the course of the experiment. Initially, acetate concentrations increased to ∼15 mM. This may be due to fermentation of organic matter from the Upper Mystic Lake sediment used as an initial microbial community source. ASVs from genera of bacteria with members known to be capable of producing acetate as a fermentation product, including Macellibacteroides and Romboutsia , were hiHowever, despite thermodynamic predictions, ultimately, iron reduction in ferrihydrite incubations only influenced methane production in the beginning of the incubation timeframe until approximately day 9 . The tim2+(aq) concentrations, such as those seen in the ferrihyrite incubations in this study, both goethite and magnetite reduction are less favorable than methanogenesis  concentrations . These togenesis . Lepidocnesis  sorption onto iron-reducing bacteria may further inhibit their ability to carry out iron reduction. It is also possible that surface passivation by formation of Fe(II) precipitates contributed to the eventual outcompeting of Fe-reducers by methanogens in ferrihydrite incubations. Despite similar methane production rates and extents, there were marked differences in microbial community development in the presence of ferrihydrite compared to controls with no added iron that did not arise with the addition of hematite or goethite. The decreased diversity of the experimental samples relative to the microbial community in the original UML sediment indicates that the experimental conditions selected for specific microbes. Two distinct clusters  correspoMethanosarcina ASVs relative to control experiments by day 18 and these ASVs continued to be at high relative abundance at day 29  that was not as highly abundant in the control, goethite, and hematite samples. It is unclear if this ASV is simply more tolerant to high Fe(II) concentrations, or if some other unaccounted for process is occurring only in ferrihydrite samples. At the genus level, Methanosarcina includes both acetoclastic conductive minerals, and acetate. These include experiments with paddy soil with additions of both ferrihydrite and magnetite , that have been demonstrated to occur in the presence of hematite and magnetite, among other (semi)conductive materials . DIET inperiment  did not periment  and benzperiment ; additio2 formation per L of enrichment culture in Fe-AOM enrichment experiments in the presence of ferrihydrite, compared to net methane production rates of up to 1.5 mmol per day per L starting solution in this work.Moreover, methanogens have been shown to directly contribute to iron cycling. Some methanogens have even been able to reduce iron(III) in laboratory studies , and metAdditionally, more information about environmental conditions must be taken into account when determining applicability of our experiments at specific field conditions. For example, naturally formed iron oxide minerals, like goethite, can have a higher surface area than laboratory-synthesized iron oxides, which will influence their susceptibility to iron reduction . Other fVarying crystallinities of iron oxides have been invoked previously as an explanation for overlapping zones of sulfate and iron reduction , and thiThis work demonstrates the influence of iron oxide structure in determining the rate and extent of iron oxide reduction compared to methanogenesis systems with non-limiting concentrations of acetate. More well-crystalline iron oxides hematite and goethite had little effect on methane production or microbial community development throughout the course of the experiment. While the presence of ferrihydrite led to an initial surge of iron reduction throughout the first 9 days of the experiment, ferrihydrite likely transformed into more crystalline iron oxides like magnetite and goethite or underwent surface passivation by Fe(II) minerals over time. This eventually allowed methanogenesis to dominate, although the rate of methane production was slowed until after iron reduction stopped. By the end of the incubation experiments, there was no distinguishable difference in the maximum rate or total extent of methane production under any of the experimental conditions.2 to lower concentrations than sulfate reducers and methanogens in freshwater sediments, and that at high substrate concentrations all three processes could occur simultaneously  can be found below: HG and BK designed the study. HG carried out the experiments, performed the geochemical analyses and DNA extractions, and wrote the manuscript. JE and HG contributed to the metagenomic data analysis. BK and JE contributed to the writing of and revised the manuscript. All authors contributed to the creation of the manuscript.BK was employed by Exponent, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.