Heritable connective tissue disorders (HCTDs) consist of heterogeneous syndromes. The diagnosis of HCTDs is aided by genomic biotechnologies  facilitating the discovery of novel variants causing disease.Detailed clinical exam data and CLIA-approved genetic testing results from next generation sequencing of 74 genes known to play a role in HCTDs were manually reviewed and analyzed in one hundred consecutive, unrelated patients with phenotypic features indicative of a HCTD referred over a 3.5-year period (2016–2020) to a specialized academic genetics clinic. The prevalence of symptoms was evaluated in the context of genetic variants. We also determined if symptoms among different organ systems were related and performed latent class analysis to identify distinct groups of patients based on symptomatology.In the cohort of 100 consecutive, unrelated individuals there were four pathogenic, six likely pathogenic and 35 classified potentially pathogenic variants of unknown clinical significance. Patients with potentially pathogenic variants exhibited similar symptom profiles when compared to patients with pathogenic/likely pathogenic variants in the same genes. Although results did not meet a multiple testing corrected threshold, patients with connective tissue symptoms had suggestive evidence of increased odds of having skin  and eye symptoms  requiring further studies. The best performing latent class analysis results were identified when dividing the dataset into three distinct groups based on age, gender and presence or absence of symptoms in the skeletal, connective tissue, nervous, gastrointestinal and cardiovascular systems. These distinct classes of patients included individuals with: (1) minimal skeletal symptoms, (2) more skeletal but fewer connective tissue, nervous or gastrointestinal symptoms and (3) more nervous system symptoms.We used novel approaches to characterize phenotype-genotype relationships, including pinpointing potentially pathogenic variants, and detecting unique symptom profiles in patients with features of HCTDs. This study may guide future diagnosis and disease/organ system monitoring with continued improvement and surveillance by clinicians for patients and their families.The online version contains supplementary material available at 10.1186/s12920-022-01321-w. Heritable connective tissue disorders (HCTDs) are a diverse group of conditions that encompass symptoms indicative of defects in the organization and synthesis of extracellular matrix components involving many organs . FeatureThough the accuracy of diagnosing these disorders has increased dramatically using NGS and connective tissue gene panels, patients present with variable expressivity of clinical signs and symptoms that often overlap among genetically distinct HCTDs , 16. It FBN1 gene) found strong phenotypic correlations within a given organ system  but weak correlations between features belonging to different systems [FBN1 gene are not the only genetic factors contributing to expression of symptoms in Marfan syndrome [Furthermore, it is possible that phenotypic heterogeneity within syndromes with well-described pathogenic variants is influenced by variants of unknown significance in other genes. For example, a study that examined a large group of patients with genotypically confirmed Marfan syndrome , an ABMG board-certified clinical geneticist, and underwent genetic testing with NGS connective tissue disorder gene panels. This study summarizes clinical and genetic findings to yield a better understanding of the type and frequency of presenting features for HCTDs. In addition, we characterize genotype–phenotype relationships and interactions of clinical features with molecular genetics, including providing evidence for potentially important variants that are not currently considered pathogenic or likely pathogenic but should be further studied for possible pathogenicity. To determine if unique classes of symptoms were present among patients, a latent class analysis was used.https://www.fulgentgenetics.com/Connective-Tissue) as well as additional genes selected based on current evidence in the field and the expertise of the senior author  to the University of Kansas Medical Center Genetics Clinic with features of HCTDs  . Each pa6–2020 toClinical information was collected during the clinic visit along with buccal cells sent to the laboratory for DNA isolation and NGS connective tissue disorder testing. Data were de-identified, entered and stored with physical copies of the visits under lock and key with access restricted to approved study personnel only.Patient age, gender, chief complaint, review of systems, medical and family histories of HCTDs, inheritance patterns and physical examination. As a part of standard procedure during the detailed clinical evaluation, patients were also asked about symptoms relating to autonomic dysfunction, tachycardia, history of gastrointestinal issues , joint pain and hypermobility, etc. These responses were recorded and utilized for assessment and plan of care for delivery of patient services.Phenotypic features recorded were based on those known to be associated with clinically suspected heritable connective tissue disorders, clinical context and included Beighton hyperflexibility scores greater than or equal to 5 out of 9 and 33 identified clinical symptoms stratified in seven categories or systems as follows: skeletal, connective tissue, skin, eye, nervous, gastrointestinal and cardiovascular with list of features noted within each of the seven clinical categories seen in Table Results from NGS data analyses, or by Sanger sequencing to ensure 100% coverage of coding sequences, were manually extracted from Fulgent Genetics (fulgentgenetics.com) genetic testing reports and included gene name, inheritance modality, deletions/duplications confirmed by an orthogonal method (qPCR or MLPA), variant type  and position based on Hg19 build, amino acid substitution (if applicable), zygosity, and pathogenicity classification  following American College of Medical Genetics established guidelines. As this study aimed to describe variants of unknown clinical significance (VUS) that were potentially deleterious leading to pathogenicity, VUS were further interrogated and considered potentially pathogenic, if meeting at least three out of four criteria provided by Fulgent Genetics in genetic testing reports that were selected a priori based on the expertise of the practicing clinical geneticist and the clinical context of the patient history and presentation included: (1) maximum allele frequency ≤ 0.03% in the Broad Institute gnomAD database, 2) missense variant Grantham distance > 100 reflecting the evolutionary distance between the referent amino acid and the substituted amino acid (distances range from 5 to 250), (3) amino acid evolutionary conservation ≥ 90% across all mammals (including primates), and (4) in silico predictive computational scores with ≥ 50% deleterious changes identified in up to 10 programs used by the genetic testing laboratory over the 3.5-year period (2016–2020) during which time DNA was sent for testing. As such, not all variants identified were evaluated using all 10 programs as some reflected more recent predictors that may not have yet been available or were unable to provide a prediction due to limited information in the literature at the time of genetic testing. These ten programs were AGVGD, FATHMMMKL, LRT, SIFT, MUTATION ASSESSOR, MUTATION TASTER, FATHMM, METALR, METASVM and PROVEAN. Details on potentially pathogenic variants are provided in Additional file  missenseThe male-to-female ratio and averages/ranges of ages and Beighton scores were calculated for the dataset. Frequencies of specific symptoms categorized as present or absent and overall prevalence within a system were recorded. Presence of pathogenic, likely pathogenic, or potentially pathogenic VUSs were evaluated in relation to clinical features and the average number of these types of variants identified per patient was calculated.p < 0.10. As the p value represents the probability that a relationship between any two tested symptoms would occur by random chance, in this case that threshold was set at less than a 90% chance. For systems where symptoms were evidenced to be associated, we additionally evaluated the associations in logistic regression models while adjusting for age, gender and variant class—where variant class was categorized based on having at least one variant of interest  versus having other VUS not evidenced to be deleterious or no variants identified. False discovery rate was also controlled for using the Benjamini–Hochberg procedure and adjusted p values are reported to allow for comprehensive presentation of the results of these initial exploratory analyses.Considering patients presented to the genetics clinic due to suspicion of any HCTD and many genetic testing results were inconclusive, to better understand the relationships among symptoms and determine if we could identify more homogenous groups based on symptom profiles that may reflect distinct HCTDs , we performed latent class analysis (LCA). We used the polytomous variable Latent Class Analysis (poLCA) package in R v 1.4.1 . LCA assFour LCA models that included independent symptom variables were fit assessing the possibility of one to seven latent classes, with and without estimates of covariate effects  on underlying and measured variables. ‘Model 1’ included symptoms in the skeletal, connective tissue, nervous, gastrointestinal, and cardiovascular systems. ‘Model 2’ included these symptoms plus age and gender as covariates. ‘Model 3’ included symptoms in the skeletal, skin, eye, nervous, gastrointestinal, and cardiovascular systems. ‘Model 4’ included these symptoms plus covariates. Model fit and optimal number of latent classes were determined based on the best estimates of the Akaike information criterion (AIC), Bayesian information criterion (BIC), Pearson’s chi-square goodness of fit (Chisq), and likelihood ratio chi-square (Gsq) statistics that were calculated following 10 repetitions of each analysis. Considering all statistics calculated reflected different assumptions with different properties, the optimal LCA solution was determined to be the model where all four statistics were collectively minimized, while also maintaining reduced numbers of classes . Notablyp value < 0.05. Age was compared across all classes using one-way analysis of variance and between classes with t-tests. To determine if the latent symptom classes reflected genetic differences, logistic regression was used—while adjusting for age and gender—to test if having variants in any of the evaluated genes, or if specifically having genetic variants categorized as pathogenic, likely pathogenic or potentially pathogenic in any evaluated genes were associated with latent symptom classes.Fisher’s exact tests were used to compare the proportion of patients with symptoms in organ systems of interest across the final solution of latent classes. Considering the goals of these tests were to evaluate evidence for symptoms differences among patients assigned to latent classes , significance was based on a Benjamini–Hochberg adjusted Over an approximate 3.5-year period 2016–2020), there were 111 patients referred to the clinic for potential HCTDs presenting with concerns about a connective tissue disorder involving any or most of the seven clinical categories for connective tissue disorders as described in Table 6–2020, tThe average Beighton hyperflexibility score (± SD) was 5.9 ± 1.9 in 88 patients. Twelve patients were unable to be assessed due to joint pain, damage or surgery. A score of 9 out of 9 was observed in 9.1% of patients and a minimum score of zero observed in 1.1%. Within the skeletal system, the most common signs/symptoms were limb asymmetry—referring to different leg lengths and may be further impacted by flat feet, hip subluxation/dislocation and ankle instability (30%), followed by scoliosis (23%) and pes planus (14%). The most common connective tissue features within the patient cohort were joint hypermobility (79%), joint subluxation (51%), and dental defects (15%). Findings related to skin were quite common within the patient population: stretchable skin (69%), easy bruising/bleeding (59%), and poor wound healing/striae (37%). Ocular symptoms were observed with myopia at 29%. The two most common nervous system-related symptoms were migraines (28%) and chronic fatigue (19%). Gastrointestinal-associated problems included irritable bowel syndrome (17%) and food intolerance (16%). Cardiovascular findings included cardiac valve/septal defects reported in 17% of the patients. None of the patients reported a history of retinal detachment, spontaneous pneumothorax, or myocardial infarctions. The complete list of 30 clinical symptoms identified in the patient cohort categorized or grouped across seven organ systems can be seen in Table In total, there were 117 unique variants identified in 45 of the 74 genes included on commercially available connective tissue disorder testing panels  was heterozygous in one patient (ID #54) with symptoms related to skeletal, connective tissue, skin, eye and cardiovascular issues. One other patient (ID #109) had a VUS identified in COL1A1 that was potentially pathogenic (p.Arg796Cys). This patient also had skeletal and connective tissue symptoms, but no skin, eye or cardiovascular symptoms; however, they did have nervous system symptoms which were absent in the patient with the pathogenic variant in COL1A1. Notably, patient #54 with the pathogenic COL1A1 variant also had a likely pathogenic intronic ABCC6 variant (c.3883-6G > A). This likely pathogenic ABCC6 variant is reported to exhibit autosomal recessive inheritance making it unlikely this variant modifies the effects of the pathogenic variant. Regardless, there was one patient (ID #7) with a potentially pathogenic VUS in ABCC6 (p.Ala343Thr) exhibiting connective tissue, eye and cardiovascular symptoms; however, this patient also had a potentially pathogenic VUS in a different gene, FBN1 (p.Arg1428His).To better understand if any potentially pathogenic variants reported to be of unknown significance according to ACMG classifications may be clinically relevant, symptom profiles for patients with an ACMG-classified pathogenic or likely pathogenic variant were compared with those of patients who had a potentially pathogenic variant identified in the same gene. Specifically, a pathogenic variant in COL3A1 gene. This patient (ID #4) had symptoms related to connective tissue and skin. One other patient (ID #70) with a potentially pathogenic VUS identified in COL3A1 (p.Glu745Lys) had skeletal, connective tissue, skin, eye and cardiovascular symptoms.Other pathogenic variants of interest included a variant (c.1662 + 1G > A) near the FKBP14 gene with skeletal, connective tissue, skin, eye, nervous system, and cardiovascular symptoms. Another patient (ID #33) was heterozygous for a pathogenic duplication in PLOD1 (p.Glu326Lys585dup) and only exhibited skeletal symptoms. No other patients had FKBP14 or PLOD1 variants.Finally, there were two patients with pathogenic variants identified in genes reported to only have a recessive inheritance pattern. One patient (ID #99) was heterozygous for a pathogenic deletion of the entire B3GALT6 variant  and skeletal, connective tissue, skin and nervous system symptoms. In total, there were three patients with variants of potential interest in B3GALT6. One additional patient (ID #91) had four distinct potentially pathogenic VUSs identified including one in B3GALT6 (p.Trp198Leu), as well as potentially pathogenic variants in COL1A2 (p.Leu1019Phe), FBN2 (p.Ile998Asn) and TGFBR2 (p.Gly2Cys). The other patient (ID #104) had a potentially pathogenic B3GALT6 VUS (p.Ala114Glu) and no other variants of interest. The common features among all patients with B3GALT6 variants included skeletal, connective tissue, skin and nervous system symptoms although all patients were heterozygous. Variants in B3GALT6 have only previously been reported to be inherited in a recessive pattern. As such, it remains unclear if heterozygous variants in this gene influence symptom expression.Likely pathogenic variants were also identified in six patients. One patient (ID #80) had a likely pathogenic, heterozygous FLNA (p.Ile137Asn) was heterozygous in one female patient (ID #73) with connective tissue, skin, nervous system and cardiovascular symptoms. A likely pathogenic variant was identified in MITF (p.Glu318Lys) in a patient (ID #77) with skeletal, connective tissue, skin, nervous system and gastrointestinal symptoms. A likely pathogenic variant was identified in MYLK (p.Thr165Profs*72) in one patient (ID #108) with skeletal, connective tissue, skin and cardiovascular issues. Symptom comparisons between patients with likely pathogenic and potentially pathogenic variants could not be conducted as no other patients had variants of interest identified in FLNA, MITF, or MYLK.In addition, a likely pathogenic X-linked dominant variant in B3GALT6, COL1A1, COL1A2, COL3A1, FKBP14, and PLOD1. In addition, potentially pathogenic VUS were identified in COL12A1  and ZNF469 (currently reported recessive); however, no ACMG-classified pathogenic or likely pathogenic variants were identified in either COL12A1 or ZNF469 in this patient cohort and symptom profiles could not be compared.With regard to the specific set of 19 genes where variation is reported to cause EDS , there wp = 0.02) and eye symptoms ; no other relationships were identified based on an unadjusted p < 0.10 . Furthermore, having a variant classified as pathogenic, likely pathogenic, or potentially pathogenic VUS in any evaluated gene was not associated with the latent classes .There was no evidence that having any type of variant in any of the evaluated genes was associated with assignment to a latent symptom class . This indicates the need to identify additional genes of interest for testing and that research should focus on better functional characterization of VUS to improve future genetic testing and interpretation of results. Notably, there were 35 VUSs in 31 genes that upon further evaluation were potentially pathogenic in approximately half (56%) of the genes currently included on these panels. Symptom presentation appeared similar when comparing data from individuals with pathogenic/likely pathogenic variants to those with potentially pathogenic VUS. Given the limitations of in silico predictors that were used to define potentially pathogenic variants, additional follow-up studies are necessary to confirm an effect on the protein product. Regardless, this suggests that the prediction criteria used to further delineate VUS to detect those that were likely damaging to protein products may be useful to helping understand how to better treat patients for whom a pathogenic or likely pathogenic variant is not identified.p = 0.07) for reducing the odds of having comorbid eye and connective tissue symptoms. Furthermore, relationships between connective tissue and gastrointestinal symptoms were near significance (p = 0.10) and the next largest observed effect (OR 1.73).Chi-square tests were used to evaluate relationships among symptoms in seven organ systems. Although results should be considered in the context of these exploratory analyses and did not survive correction for multiple testing, given the presence of a connective tissue symptom , the odds of having a skin symptom  were 2.18 times greater than the odds in the absence of connective tissue symptoms. Similarly, the odds of having an eye symptom  were 1.89 times greater given connective tissue symptoms. Male gender was near significant , FLNA (N = 1 patient) and MYLK (N = 1 patient) causing classic genetic disorders such as Marfan syndrome , Melnick-Needles syndrome  and familial thoracic aortic aneurysm ; all patients had significant health concerns consistent with these disorders.Four patients, out of the 100 included in the dataset (4%), were heterozygous for variants classified as pathogenic. One patient had a pathogenic  type IV . In addiecessive , and is  type IV , indicat type II , it is uS type I . In addiFBN1 variant, the patient with the potentially pathogenic variant had features consistent with Marfan syndrome  [FBN1 variant that did not meet pathogenicity criteria exhibited some traits like Marfan syndrome but not the trademark tall stature. There were also two patients with variants in the COL3A1 gene where known pathogenic variants are autosomal dominant and causal for Ehlers Danlos syndrome type IV and rare type III [COL1A1. Pathogenic variants in COL1A1 are causal for Ehlers–Danlos syndrome (type I and VII), osteogenesis imperfecta (type I-IV), osteoporosis, and Caffey diseases (https://www.omim.org/entry/120150). All three patients had scoliosis; however, this was the only noted symptom for the patient with the VUS not meeting pathogenicity criteria. The two other patients had several skeletal and connective tissue issues.There were several variants identified that were currently classified as unknown significance and potentially pathogenic based on likelihood of being damaging to the gene product. These may be of particular interest for future evaluation and functional characterization. For example, similar to the patient with the likely pathogenic habitus) , 17. Onetype III . One patThese analyses offer insight into distinguishing characteristics among different patients with HCTD; however, particularly with regard to the identification of latent symptom classes, results should be considered preliminary and require replication in independent datasets to further confirm these findings. While the evaluated sample size is relatively small for LCA, we attempted to maximize performance by including high quality indicators that were confirmed via detailed clinical evaluations, as well as including covariate effects related to age and gender. This approach has been shown to help compensate for small sample size and be generally beneficial to improving performance based on evidence from Monte Carlo simulations that evaluated LCA for samples sizes ranging from 100 to 2000 . RegardlThe analytical methods used reflect novel approaches for characterizing clinical symptoms and relationships in those patients presenting with features of HCTDs and should stimulate additional research into genotype–phenotype relationships. Clinical presentation and gene findings could have consequences for diagnosis, surveillance and counseling for the patient, guidance for health care providers, organ system surveillance and for evaluating at risk family members.Additional file 1: Table S1. Connective Tissue Disorder Genes Included on NGS Sequencing Panels.Additional file 2: Table S2. Connective Tissue Disorder Gene Variants of Unknown Significance Meeting Potential Pathogenicity Criteria and Clinical Context.Additional file 3: Table S3. Genotypic and Phenotypic Profiles of 100 Patients Presenting for Suspicion of Heritable Connective Tissue Disorders.


Engineering membrane transporters to achieve desired functionality is reliant on availability of experimental data informing structure-function relationships and intelligent design. Plant aquaporin (AQP) isoforms are capable of transporting diverse substrates such as signaling molecules, nutrients, metalloids, and gases, as well as water. AQPs can act as multifunctional channels and their transport function is reliant on many factors, with few studies having assessed transport function of specific isoforms for multiple substrates.High-throughput yeast assays were developed to screen for transport function of plant AQPs, providing a platform for fast data generation and cataloguing of substrate transport profiles. We applied our high-throughput growth-based yeast assays to screen all 13 Arabidopsis PIPs (AtPIPs) for transport of water and several neutral solutes: hydrogen peroxide (H2O2), boric acid (BA), and urea. Sodium (Na+) transport was assessed using elemental analysis techniques.All AtPIPs facilitated water and H2O2 transport, although their growth phenotypes varied, and none were candidates for urea transport. For BA and Na+ transport, AtPIP2;2 and AtPIP2;7 were the top candidates, with yeast expressing these isoforms having the most pronounced toxicity response to BA exposure and accumulating the highest amounts of Na+. Linking putative AtPIP isoform substrate transport profiles with phylogenetics and gene expression data, enabled us to align possible substrate preferences with known and hypothesized biological roles of AtPIPs.This testing framework enables efficient cataloguing of putative transport functionality of diverse AQPs at a scale that can help accelerate our understanding of AQP biology through big data approaches (e.g. association studies). The principles of the individual assays could be further adapted to test additional substrates. Data generated from this framework could inform future testing of AQP physiological roles, and address knowledge gaps in structure-function relationships to improve engineering efforts. Deciphering the function of membrane transporters is essential for protein engineering and design, aiding development of designer proteins for a multitude of industries, such as medical, chemical, environmental and food . Improve2 and nitrogen (NH3/2O2) and nitric oxide (NO); O2 and lactic acid to cope with anoxic stress; and key nutrients such as potassium (Aquaporins (AQPs) constitute a major family of channel proteins with great potential to address a multitude of industry challenges  and inteotassium .The AQP gene family has expanded the most in plants, providing a rich source of sequence diversity to inform structure-function relationships. This diversity likely reflects greater duplication rates of plant genomes and the adaptive potential AQPs provide for a sessile lifestyle. Originating from only a few distinct isoforms in green algae, the genomes of Angiosperm species commonly harbour between 30-50 isoforms, with extremes of 84 and 121 in tobacco and canola, respectively . There a2 , which preclude their use for high-throughput applications. By contrast, yeast offer a simple and versatile host for the heterologous production of aquaporins , determining candidates for water, hydrogen peroxide, boric acid and urea transport. Sodium permeability for all AtPIPs was investigated though elemental analysis techniques.This approach can be used to efficiently catalogue the transport functions of many AQPs to help clarify their biological roles in plants and for use in associations studies to inform structure-function relationships towards improved protein engineering efforts.AtPIP and control gene coding sequences were commercially synthesised (Genscript) as gateway-enabled entry constructs and cloned into destination vectors from the Advanced Gateway® series of yeast expression plasmids . Subcellular localization in yeast cells was performed using confocal microscopy on a Zeiss LSM780 confocal laser-scanning microscope (Carl Zeiss) operated by Zen Black software. Quantification of AtPIP2;5 interactions with AtPIP1 proteins using the Y2H mbSUS was performed as per  in Nunc-96 400 µL flat bottom untreated 96-well plates (Thermo Scientific Cat#243656) with lid and 200μl culture volume per well. Default cycling conditions for yeast growth assays were: 250 cycles at 10 mins per cycle ; incubated at 30°C with a slightly warmer lid; shaking frequency of 400 rpm in double orbital shaking mode; 5 mins shaking per cycle prior to the OD reading, with the remaining time the plate sitting idle on the incubation plate; OD readings invoke orbital averaging at scan diameter of 4mm and 22 flashes per well, recording at 650nm. OD650 readings minus the blank were corrected for non-linearity using our pre-determined calibration function to generate a ‘true’OD650 at a 1cm path-length. The data was then converted into growth curves that were smoothed using several filters illustrated in Corr.OD650 values. These were finally log (LN) transformed using Corr.OD650 at time ‘t’ divided by the initial starting OD (Corr.ODi), details of these corrections are described in Corr.ODt/Corr.ODi) values’. Specifics of freeze-thaw, H2O2, boric acid, urea, and NaCl treatments are detailed in Detailed material and methods are provided as plasmids  to creatd as per . Yeast sWe established a high-throughput yeast micro-cultivation (200 µl) method using 96-well plates. The micro-cultures were incubated in a plate reader with versatile control over temperature, shaking, and OD reading modes. We optimized these parameters to find conditions that generated repeatable growth curves  described the relationship between ‘recorded’ and ‘true’ OD datasets that was valid for all S. cerevisiae strains used in this study , generating uncompressed growth curves with improved resolution of key derived growth characteristics: initial lag phase (λ), maximum growth rate (μ), and final carrying capacity or biomass yield (κ) , or the culture with the highest supplementation of essential nutrient when examining growth requiring agents (e.g. urea). AUCs for all cultures were calculated from the start of cultivation until ф (treated/AUCcontrol providing relative differences in growth (ΔAUC). In our routine conditions, all control cultures reached and remained in stationary phase for an extended period of time. As such, ф can be shifted (ф+t) in order to capture additional data from treated cultures that grow very slowly; with an understanding that ΔAUC will be underestimated because the control culture plateaued earlier  as a single all-encompassing parameter capturing changes in λ, μ and κ , indicating high AtPIP protein production  achieved using the strong GPD promoter relative to the less active TPI1 promoter  in order to evaluate AQP-facilitated substrate transport into the cell. Sub-cellular localization of the AtPIPs was evaluated using confocal microscopy of AtPIP-GFP lines and compared against cytosolic (GFP only) and endoplasmic reticulum  markers Figure 2lacZ reporter ≥ 4-fold above background levels , enabling further enhancements in sensitivity due to the co-expressed AtPIP1.aquaporin null mutant background (aqy1 aqy2), which is compromised in tolerance to rapid freeze-thaw events   , dramatiaqy1 aqy2 yeast expressing one of the 13 AtPIP genes or an empty vector, differentially affected the growth curves , can impair yeast growth (decreasing μ and κ) and trigger cell death (prolonging λ) when internalized levels exceed the protective mechanisms of the cell  (The reactive oxygen species (ROS) hydrogen peroxide  especially prominent at 1mM compared to their evaluation in the aqy1 aqy2 background  than empty vector control, with growth reductions sitting between the most and least sensitive AtPIP2;2 and AtPIP2;6 respectively , suggesting that expression of AtPIP1;2 alone in skn7 somehow protects against H2O2 treatment  with hindered monomeric channel activity but retained hetero-oligomerization capacity. The histidine at position 207 in AtPIP1;4 represents a strongly conserved residue located in a cytosolic loop of PIP proteins that is involved in gating of the monomeric pore  concentrations were tested on aqy1 aqy2 empty vector yeast to determine treatment doses. Consistent with reports, moderate BA treatments mainly reduced the rate of growth (μ) with little impact on lag-phase (λ)  it acts as a fungistatic agent, slowing down proliferation by disrupting cell wall synthesis, but not killing the cell . A rangeAtPIP1;1 expressing yeast were by far the most sensitive to BA exposure, with dramatic growth reductions even at 20mM BA  of AtPIP-expressing yeast in response to 20mM and 30mM BA treatment were consistent with the empty vector control exposed to increasing BA concentrations C. Five  control . AtPIP1;Δ2-47AtPIP1;2, Δ2-47AtPIP1;4 and Δ2-48AtPIP1;5). The truncated versions had similar sensitivity to BA as their full-length counterparts  urea uptake deficient mutant was enhanced by increasing concentrations of urea; specifically through increased maximum growth rate (μ) and carrying capacity (κ)  . These aAtpip1;2, Atpip2;1 and Atpip2;6 each show a ~20% reduction in rosette hydraulic conductivity, which worsens to ~39% in the triple mutant  led to enhanced toxicity phenotypes indicative of H2O2 transport in yeast  that regulate seed dormancy versus germination  . This data suggests involvement of other, yet to be determined, domains influencing AQP substrate permeability, that should be revealed with the expanded application of this phenotyping framework and generation of the transport profile catalogue.AQPs are membrane proteins with wide-ranging transport capabilities, for which deciphering functional determinants will be essential for their successful deployment in industrial and crop biotechnological applications. Building a substantial catalogue of transport profiles for AQPs is needed for association studies to identify residues of functional relevance, and for the development of an AQP core differential set that would enable effective screening of new and novel substrate permeabilities. The testing framework described in this study enables efficient cataloguing of putative transport functionality of diverse AQPs. We applied this framework to produce comparative substrate transport profiles for water, hydrogen peroxide, boric acid and urea across the entire AtPIP subfamily. NaThe original contributions presented in the study are included in the article/MG conceived the original screening, framework, future applications, and research plans, and made the yeast expressing the AtPIP constructs. MG and AD developed the micro-cultivation methodology, established optimal treatment concentrations and wrote the manuscript. MG developed data processing methodology, MG performed the AtPIP yeast screening experiments and analysis. MG and WC performed AtPIP interaction and yeast spheroplast analysis. JQ and SM developed and performed the sodium uptake assay with supervision by CB. MG, JE, CB, AD and SM analyzed the data. All authors critically reviewed and edited the manuscript. MG and AD agree to serve as the authors responsible for contact and ensure communication. All authors contributed to the article and approved the submitted version.


Methylation of cytosines is a prototypic epigenetic modification of the DNA. It has been implicated in various regulatory mechanisms across the animal kingdom and particularly in vertebrates. We mapped DNA methylation in 580 animal species , resulting in 2443 genome-scale DNA methylation profiles of multiple organs. Bioinformatic analysis of this large dataset quantified the association of DNA methylation with the underlying genomic DNA sequence throughout vertebrate evolution. We observed a broadly conserved link with two major transitions—once in the first vertebrates and again with the emergence of reptiles. Cross-species comparisons focusing on individual organs supported a deeply conserved association of DNA methylation with tissue type, and cross-mapping analysis of DNA methylation at gene promoters revealed evolutionary changes for orthologous genes. In summary, this study establishes a large resource of vertebrate and invertebrate DNA methylomes, it showcases the power of reference-free epigenome analysis in species for which no reference genomes are available, and it contributes an epigenetic perspective to the study of vertebrate evolution. DNA methylation is involved in regulatory processes throughout the animal kingdom. Here, the authors map DNA methylation in 535 vertebrates and 45 invertebrates, establishing a reference dataset for cross-species analysis and exploring epigenetic variation across vertebrate evolution. Moreover, it is involved in the regulation of many biological processes associated with multicellular life2, including development3, cell differentiation4, and maintenance of cellular identity6. DNA methylation has been studied extensively in the context of diseases such as cancer8, metabolic diseases9, autoimmune disorders11, and for its role in aging12. From an evolutionary perspective, DNA methylation and its associated enzymes (most notably the DNA methyltransferases that “write” DNA methylation) are present throughout the animal kingdom, although they have been lost in certain species including the model organism Caenorhabditis elegans13. DNA methylation has also been implicated in speciation14 and in the response to environmental influences16. The study of DNA methylation in a broad range of species is expected to contribute to our understanding of evolution.DNA methylation at the fifth carbon position of cytosines (5-methyl-cytosine) provides an epigenetic layer of genome regulation that does not involve changes in the DNA sequence. In vertebrates, DNA methylation occurs preferentially at palindromic CpG dinucleotides, where it marks both strands symmetrically. It is essential for genome integrity and contributes to the silencing of transposable elements18, which revealed major differences between vertebrates and invertebrates19. Moreover, considerable variability was observed among vertebrates23. While these initial studies relied on methylation-specific restriction enzymes or on chromatography-based methods, more recent investigations used next-generation sequencing to determine DNA methylation patterns in 17 eukaryotic species (which included two vertebrates)24, in 13 animal species (five invertebrate and seven vertebrate species)25, in seven vertebrate species26, and in eight mammalian species27. High-resolution DNA methylation maps enabled initial analyses of the evolutionary relationship between DNA methylation and the underlying DNA sequence30. These previous studies were however limited to a small number of species, while an ideal study would cover many species across all branches of vertebrate evolution—such that each species becomes a complex data point in a truly integrative analysis of DNA methylation in its evolutionary context.Genome-wide DNA methylation patterns vary widely across species. Pioneering research in the early 1980s compared global levels of DNA methylation across several animal species32. CpG-rich genomic regions (including many promoters and enhancers) tend to be unmethylated, except where they overlap with evolutionarily recent transposable elements33 or are subject to mechanisms of regulatory repression that involve DNA methylation34. In contrast, CpG-poor genomic regions tend to be highly methylated, except where they overlap with active transcription factor binding sites35 or in areas of large-scale DNA methylation erosion as observed in cancer and ageing37. The genome-wide correlation between DNA methylation and DNA sequence has enabled the prediction of locus-specific DNA methylation levels based on the underlying DNA sequence, focusing on CpG islands and gene promoters39 and on individual CpG dinucleotides41. Consistent with this genetic basis of DNA methylation, differences in the genomic DNA sequence between individuals have been linked to differences in DNA methylation43. Nevertheless, human primary samples tend to cluster according to tissue type rather than according to the sample donor44, indicating that DNA methylation differences between human individuals are generally less pronounced than tissue-specific differences.In humans, where DNA methylation has been studied in most detail, a strong correlation exists between the patterns of genomic DNA sequence and local DNA methylationTo investigate DNA methylation beyond the human genome and in the broad context of vertebrate evolution, we established genome-scale DNA methylation profiles at single-base resolution across a wide range of vertebrate and invertebrate species, covering all vertebrate classes and several proximal invertebrate classes. Primary tissue or DNA samples were obtained from biobanks and other sources comprising mainly wildlife and zoo animals. We included heart and liver samples wherever possible, to allow for tissue-matched comparisons across species. In addition, other tissues such as lung, gills, fin, spleen, brain, lymph node, muscle, kidney, and skin were included in a species-specific manner. Samples were selected to prioritize healthy adults and to balance the male-to-female ratio, aiming for two to four individuals per species. This sampling strategy allowed us to cover a large number of species, consistent with our study’s focus on analyzing trends that hold across multiple species, rather than on the in-depth investigation of DNA methylation regulation in individual species.47. Our assay enriches for CpG-rich regulatory regions but also covers many other parts of the genome, including exons, introns, intergenic regions, and repetitive elements; and it measures DNA methylation both at CpG sites and non-CpG sites in the genome. We analyzed our RRBS dataset using reference-genome independent bioinformatic methods45, allowing us to include many species that do not currently have a published reference genome and to avoid biases due to the different quality of available reference genomes. We previously validated this approach in a head-to-head comparison of reference-free and reference-based analysis in three species45. Moreover, as part of this study we confirmed that RRBS is indeed suitable for cross-species analysis, using in silico simulations of RRBS coverage based on existing reference genomes, comparison to whole genome bisulfite sequencing (WGBS) data, and reference-based analysis in a subset of samples.DNA methylation profiling was performed using an optimized version of the reduced representation bisulfite sequencing (RRBS) assayOur full dataset comprises 2443 DNA methylation profiles covering 580 animal species (535 vertebrates and 45 invertebrates). Based on this dataset, we identified a quantitative, predictive association of DNA methylation and the underlying genomic DNA sequence that was shared between vertebrate and invertebrate species. We observed two major transitions along the evolutionary axis: one between vertebrates and invertebrates and one between amphibians and reptiles. We also investigated tissue-specific and inter-individual differences in DNA methylation. For fish, birds, and mammals, tissue-specific differences were more pronounced than inter-individual differences, but for invertebrates, reptiles, and amphibians, both factors explained a similar share of the observed variance in DNA methylation. By analyzing transcription factor binding sites in differentially methylated regions between heart and liver tissue throughout vertebrate evolution, we identified a deeply conserved association of DNA methylation with tissue identity. Finally, cross-mapping to existing reference genomes identified characteristic evolutionary trends in DNA methylation at gene promoters.48, and provide a starting point for untangling how the complex interplay of DNA sequence patterns and DNA methylation has contributed to the evolution of vertebrate genomes.In summary, this study contributes an epigenetic perspective to the investigation of vertebrate evolution, and establishes a major resource for dissecting the role of DNA methylation in vertebrates and invertebrates. Moreover, our results emphasize the feasibility and value of including epigenome profiling in ongoing initiatives to map all vertebrate genomesLethenteron camtschaticum), 32 cartilaginous fish (chondrichthyes), 565 bony fish (actinopteri), 74 amphibians (amphibia), 280 reptiles (reptilia), 607 birds (aves), 70 metatherian mammals / marsupials , and 658 eutherian mammals . Wherever possible, we included multiple tissues (most notably heart and liver for comparison across species) and multiple individuals, with a balanced sex ratio and a focus on young adult animals.To investigate the evolutionary dynamics of DNA methylation in vertebrates, we performed genome-scale DNA methylation profiling for 580 species and a total of 2443 primary samples Fig. . Our sam49. In RRBS, restriction enzymes cut the DNA at CCGG (MspI) and TCGA (TaqI) sites independent of CpG methylation, followed by DNA methylation profiling of the size-selected fragments using bisulfite sequencing. RRBS selects for DNA fragments that each contain at least one CpG, making the assay cost-effective and quantitative even for large genomes with variable CpG density. RRBS covers around four million out of 28 million CpGs in the human genome and two million out of 20 million CpGs in the mouse genome — including CpG-rich promoter and enhancer regions as well as a broad sampling of regions with modest CpG density52.DNA methylation profiling was performed using reduced representation bisulfite sequencing (RRBS). The RRBS assay provides single-nucleotide, single-allele resolution for a defined set of genomic regions determined by sequence-specific DNA fragmentation and size selection. This focus on DNA fragments with defined start and end sequences facilitates DNA methylation analysis across species and without reference genomes . RRBS leverages the concept of reduced representation sequencing , a widely used method for genotyping in species that lack high-quality reference genomes49. To provide additional support for the validity of using RRBS for cross-species analysis, we simulated RRBS coverage across a wide range of species and analyzed the expected RRBS coverage for genomic elements such as CpG islands, transcripts, promoters, and repetitive elements. Across 76 species for which reference genomes were available , we simulated the restriction digest and size selection in RRBS53, and we determined the expected coverage for the different types of genomic elements , we also sequenced one RRBS library without bisulfite conversion for each species and included these data in the identification of genomic cytosines. RRBS quality metrics  indicated high data quality for most samples and allowed us to flag potentially problematic samples , which had consistently lower DNA methylation levels . Among the invertebrates—by far the most heterogeneous group in our analysis—we observed a wide range of DNA methylation levels from 2% in Penaeus (prawns) to 80% in the clam worm Alitta succinea. The majority of these invertebrates had DNA methylation levels in the range of 20–40%, similar to the levels observed in birds and marsupials. Finally, the Japanese lamprey, which is an early jawless vertebrate, showed high DNA methylation levels close to 60%, which is similar to the levels observed in reptiles and mammals  compared to other eutherian mammals . The different groups of reptiles  showed similar levels of DNA methylation—with the exception of 67. In contrast, CpG island frequency alone explained only around 23% of the observed variance, and CG composition  explained around 50%. These results show that CpG density is a key contributor but clearly not the only factor explaining the close association between genome-wide DNA methylation and genome sequence composition across species.To quantify and compare the relationship between genome-wide DNA methylation levels and the corresponding genomes, we constructed linear models based on features that describe each species’ DNA sequence composition . Strikingly, 3-mer frequencies explained >80% of the observed variance in genome-wide DNA methylation across vertebrate evolution Fig. . Four ofSimilarities in the frequency of short DNA motifs (illustrated here by 3-mers and 6-mers) in the consensus reference fragments of two species closely reflected their phylogenetic distance Fig. . This ob69. We can quantify DNA methylation erosion based on our RRBS data using the “proportion of discordant reads” (PDR) metric68. This metric exploits the observation that most genomic loci exhibit a bimodal distribution of DNA methylation , and it interprets deviations from this pattern as evidence of DNA methylation erosion. The PDR metric was first established for cancer, where it was associated with clinical features including tumor aggressiveness72. We calculated species-specific PDR values in analogy with the species-specific DNA methylation levels by averaging across consensus reference fragments, tissues, and individuals  but detectable in most samples. We did not observe strong differences in non-CpG methylation across taxonomic groups—with one exception: Brain samples of birds and mammals had elevated levels of non-CpG methylation  and low methylation (for the bulk of the genome)78—for example by suppressing repetitive DNA elements that threaten genome integrity or by constraining developmental plasticity in differentiated cells. We observed a positive correlation between genome-wide DNA methylation levels and the theoretical, unmitigated cancer risk of the investigated species (which we estimated based on each species’ body weight and longevity79). This positive association was most pronounced in birds (r = 0.53) and remained statistically significant after correcting for phylogeny (p = 0.01)  between the DNA sequence of a given genomic region and its DNA methylation level. Previous studies in human and mouse have uncovered associations between DNA methylation and the underlying DNA sequence for genomic regions such as CpG islands and gene promotersTo decipher the relationship between DNA methylation and the underlying DNA sequence throughout vertebrate evolution, we trained machine learning classifiers that predict locus-specific DNA methylation levels based on the DNA sequence of the corresponding genomic regions Fig. . Specifi81. Overall, our results support the existence of a “genomic code” that links locus-specific DNA methylation levels to the underlying DNA sequence in vertebrate and invertebrate species.We consistently observed greater-than-random prediction performance across all taxonomic groups Fig. , with hiTo dissect this predictive relationship, we compared the cross-validated prediction performance of classifiers trained on 1-mer frequencies , 2-mer frequencies, etc. up to 10-mer frequencies. In this analysis, 3-mer frequencies were generally the most informative, followed by 2-mer and 4-mer frequencies  to predict DNA methylation levels in another species. For each pair of species, we then determined ROC-AUC values as measures of cross-species predictability, with high values indicating good transferability of the trained model from one species to the other Fig. .Fig. 3ThThe prediction performance was generally high between related species and even across taxonomy groups Fig. , often r75. These observations suggest that the predictive relationship between locus-specific 3-mer frequencies and their associated DNA methylation levels  is deeply conserved across taxonomic groups. However, the fact that predictability of DNA methylation differed widely across target species suggests that some species deviate much more strongly from the genetically encoded “default” DNA methylation profile than other species, perhaps due to tissue-specific regulation, environmental influences, and stochastic effects.We found that models trained in species with lower prediction performance generally performed well in species with higher prediction performance, but not vice versa Fig. . Even prCuriously, a few species of invertebrates, fish, amphibians, and reptiles showed an apparent inversion of the genomic code for DNA methylation, such that DNA sequence patterns normally associated with low DNA methylation levels were instead linked to high DNA methylation levels, and vice versa. The presence of such inverted species in our dataset was evident from ROC-AUC values for cross-species prediction that were substantially worse than expected by random chance , a bony fish (actinopteri) species with pronounced inversion . For each of these species we inferred the percentage of variance explained by the tissue and by the individual  appears to occur on top of a broadly conserved “genomic code” of DNA methylation. We would expect that the same applies to tissue-specific as well as inter-individual differences in DNA methylation. To investigate the relative contributions of the tissue and the individual to the observed DNA methylation variation in our dataset, we focused on those species for which we have multiple tissues and individuals  may be repressed by high activity of FOXO4 in liver, while being activated by KLF5 and EGR1 in heart, which may contribute to the higher tolerance toward hypoxic conditions in the liver90. These observations are in line with our GO analysis .Given the breadth of the presented dataset and analysis, detailed follow-up studies in selected species will be needed to corroborate and extend these observations; and we provide our dataset as a comprehensive resource and starting point for such investigations  showed DNA methylation profiles that are similar to those of vertebrates, with a prominent dip at gene promoters and high gene-body methylation , rather than in taxonomic groups with intermediate levels  as it would be expected mathematically72, and loss of epigenetic control appears to causally contribute to cancer development97. In this context, the observed variability of DNA methylation erosion across species raises interesting questions regarding a potential role of DNA methylation as a cancer-protective mechanism, especially in large and long-lived vertebrates. While our dataset cannot functionally address these questions, we found that birds, which have a low incidence of tumors98, were characterized by particularly low levels of DNA methylation erosion and showed a positive correlation between theoretical cancer risk and DNA methylation levels. We envision that our optimized RRBS assay and reference-free analysis will facilitate DNA methylation profiling of tumors from wild and zoo animals for the wide range of vertebrate species encountered in veterinary pathology. This will in time contribute to a better understanding of the potential roles of DNA methylation in solving the lack of correlation between body size and cancer risk (Peto’s paradox)100, which stands out as a remarkable feat of vertebrate evolution.Erosion of DNA methylation patterns has been observed in human cancers25. Fourth, this study relies on our reference-free analysis method (RefFreeDMA)45, which enables us to work without reference genomes but lacks the regional context that is provided by a high-quality reference genome. We addressed this limitation by focusing on transcription factor binding sites, whose DNA methylation levels tend to reflect the activity of the corresponding transcription factors. Moreover, we devised a cross-mapping strategy that leverages gene annotations from existing reference genomes and integrates data from different species in a human ortholog gene space. Fifth, despite the large number of samples and species covered by this study, several interesting clades especially among amphibians and reptiles are not well represented in our dataset. Finally, the different species do not provide fully independent data points but are connected through evolution. We thus used statistical methods that correct for phylogenetic relationships, and we evaluated our classifiers both within and across species.Potential limitations of this study arise from the experimental choices that allowed us to process 2443 primary tissue samples from 580 species. First, RRBS uses restriction enzymes to pre-enrich a “reduced representation” of the genome prior to bisulfite conversion and sequencing. Compared to WGBS, RRBS covers fewer CpGs (mean: 2.5 million CpGs per sample), is cheaper, and more scalable. It also provides consistent start and end points for the DNA fragments defined by the restriction sites, which facilitates the comparison across tissues and across individuals (mean: 1.7 million shared CpGs between two samples of the same species). Second, analyzing any subset of CpGs in the genome bears the risk of introducing species-specific biases. While we performed extensive validations and designed our analyses to ameliorate this risk, it is a relevant consideration for all analyses of the presented dataset. Third, we focused our initial analysis of this large dataset primarily on DNA methylation at CpG dinucleotides, given its well-established biological roles. Nevertheless, the RRBS assay also covers DNA methylation at non-CpG sites , and we observed expectedly low levels of non-CpG methylation in our dataset . We also detected significantly higher non-CpG methylation levels in brain compared to other tissues in mammals and birds, consistent with a recent report focusing on much fewer speciesIn conclusion, this study provides an initial account of the DNA methylation landscape associated with vertebrate evolution, both by establishing a dataset of unprecedented scale and by deriving insights into conserved and divergent aspects of DNA methylation across a wide range of animal species. Most notably, we found that DNA sequence and DNA methylation exhibit widespread associations in both vertebrate and invertebrate species that gradually changed over the course of vertebrate evolution. The presented data and analyses also provide an evolutionary context for investigating the epigenetic heterogeneity that is observed in human and animal populations and in a broad range of diseases.Research Institute of Wildlife Ecology of the University of Veterinary Medicine Vienna (1611 samples): Tissue samples were collected during routine pathological examination of deceased wild, pet, and zoo animals. They were fresh-frozen and stored at −80 °C. Pathological conditions and sample preservation  were recorded (Supplementary Data Camelus bactrianus) and llama (Lama glama) were collected as part of routine veterinary examinations. Blood cell types were isolated using forward/side scatter FACS45.Ocean Genome Legacy Center (OGL) at the Northeastern University Marine Science Center (600 samples): Specimens were collected and deposited to the OGL collections by numerous collaborating researchers and were stored at −80 °C prior to dissection. DNA was isolated using the Qiagen DNeasy Blood & Tissue kit according to the manufacturer’s protocol and stored at −80 °C prior to shipment on dry ice.Commercial fish farm (Biofisch Wien) (73 samples): Innards of fish killed for food were immediately dissected, transported on dry ice, and stored at −80 °C until DNA extraction using the Qiagen DNeasy Blood & Tissue kit.Commercial fish retailer (Naschmarkt Wien) (67 samples): Whole specimens of sea food were purchased, transported on ice, dissected, and stored at −80 °C until DNA extraction using the Qiagen DNeasy Blood & Tissue kit.Department of Medical Biochemistry of the Medical University of Vienna (21 samples): Tissue samples of chicken  were collected and stored at −80 °C until DNA extraction using the Qiagen DNeasy Blood & Tissue kit.Max Planck Institute for Evolutionary Biology (16 samples): Tissue samples of Eurasian blackcaps (Sylvia atricapilla) were obtained from birds caught at the Pape Ornithological Station in Latvia  between end of August and beginning of September 2011, then transported to the University of Ferrara in Italy, where they were held in aviaries until sample collection. Experimental procedures unrelated to this work were carried out during the autumn migratory season 2011, and birds were stored at −80 °C until organs were dissected in 2016 at the MPI for Evolutionary Biology. DNA was isolated using a standard phenol-chloroform extraction protocol and stored in ddH2O at −80 °C.Department of Biology of the University of Kentucky (16 samples): Tissue samples of Mexican axolotls (Ambystoma mexicanum) were collected and stored at −80 °C until DNA extraction using a standard phenol-chloroform extraction protocol.Department for Pathobiology of the University of Veterinary Medicine Vienna (15 samples): Tissue samples of flying snakes (Chrysopelea) were collected during routine pathological examination of deceased animals and stored at −20 °C until DNA extraction using the Qiagen DNeasy Blood & Tissue kit.CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences (12 tissue samples): Healthy tissue samples of Tasmanian devils (Sarcophilus harrisii) were collected and processed as part of a previously published study that investigated Tasmanian devil transmissible tumors101.St. Anna Children’s Cancer Research Institute (12 samples): Leukocytes and erythrocytes of zebrafish (Danio rerio) were collected from kidneys and blood of adult animals. Cells were dispersed in PBS supplemented with 3% FCS and 2 mM EDTA and sorted by FACS following an established protocol for blood cell populations in zebrafish102. Sorted cells were lysed, and DNA was isolated using the Qiagen DNeasy Blood & Tissue kit.The selected samples represent all vertebrate classes and many marine invertebrate species. To obtain this breadth of coverage, samples were obtained from several sources  to create a taxonomic tree for the analyzed species, and we visualized the resulting phylogenetic relationships and associated information using the iTOL software103. The resulting annotated species tree is provided for interactive viewing and browsing under the following URL: https://itol.embl.de/tree/841339292169571630660457.All samples were annotated with a scientific (Latin) name and a common (English) name based on the information provided by the sample source. Occasionally, the available information did not support the assignment of the exact species; these samples were assigned genus names rather than individual species names. Moreover, the sequencing data for each species were compared with public reference databases (as described in more detail below), and potential errors or ambiguities were flagged or corrected based on manual inspection. Detailed taxonomic annotations for all included species were obtained from the NCBI database using the 3) were placed in collection tubes, covered with proteinase-K containing digestion buffer, and shaken overnight at 56 °C. When the tissue samples were completely dissolved, the DNA was bound to spin columns and washed, followed by elution in 50–200 µl nuclease free water, depending on the expected amount of DNA. The DNA concentration was then quantified using a Qubit fluorometer. DNA from blood cells was isolated as previously described45, using the Allprep DNA/RNA Mini kit (Qiagen). Between 50,000 and two million cells were lysed in 600 μl Buffer RLT Plus supplemented with 1% β-mercaptoethanol and vortexed thoroughly for at least 5 min. The isolation of DNA and RNA was performed according to the manufacturer’s protocol. DNA was stored at −20 °C.DNA from tissue samples was extracted using the Qiagen DNeasy Blood & Tissue kit according to the manufacturer’s protocol. Briefly, small pieces of tissue . After confirming adequate fragment size distributions with Bioanalyzer High Sensitivity DNA chips (Agilent), libraries were sequenced on Illumina HiSeq 3000/4000 machines using the 50 or 60 bp single-read setup.Reduced representation bisulfite sequencing (RRBS) was performed as described previouslyCt values for each pool, the number of PCR enrichment cycles was determined as the Ct minus two, which ranged from 5 to 11 cycles. PCR and qPCR cycler programs were the same as in the RRBS protocol. The enriched libraries were subjected to a 1.0× bead clean up. Library size distributions were assessed on Bioanalyzer High Sensitivity DNA chips (Agilent) and ranged from 260 to 300 bp (mostly 280 bp). Libraries were sequenced on Illumina HiSeq 3000/4000 machines using the 50 or 60 bp single-read setup.To distinguish with confidence between genomic thymines und constitutively unmethylated cytosines (which are read as thymines in bisulfite sequencing), we sequenced one RRBS library for each species without the bisulfite conversion step. Libraries were multiplexed in pools of up to 20 samples, and the pools were subjected to size selection with a 0.6× reverse bead clean up and eluted in 20 µl EB. The amount of effective library in the size-selected pools was determined by qPCR using 1 µl size selected library as input. Based on qPCR 45, which is available on Github (https://github.com/jklughammer/RefFreeDMA). For each species, RefFreeDMA clustered the sequencing data into read stacks corresponding to specific positions in the genome, inferred the genomic DNA sequence as a weighted consensus for each read stack (including both converted and unconverted RRBS libraries), and performed DNA methylation calling for each sample against these consensus reference fragments. Two improvements were introduced in the process of generating the consensus references: (i) Detection and removal of contaminating microbial sequences by mapping all reads to a “decoy” genome consisting of the NCBI BLAST dataset of representative bacterial/archeal genomes and keeping only unmapped reads; (ii) incorporation of unconverted RRBS libraries to enhance detection of consistently unmethylated genomic cytosines. For analyses that focused on the genomic sequence , only those consensus reference fragments that were covered by the corresponding uncovered RRBS library were considered, in order to minimize bias. Finally, summary statistics and quality metrics  were calculated for each sample  using the findOverlaps function in the R package GenomicRanges105. Finally, we calculated the fraction of CpGs within each of the assessed genomic elements that are covered by the in silico RRBS libraries. For each genome, the coordinates of the genomic elements were downloaded from the UCSC Genome Browser website (goldenpath/<genome>/bigZips) using the R package rtracklayer106. Promoters were defined as the regions 1000 bp upstream to 500 bp downstream of the transcription start sites. The genome sequences were obtained from the corresponding genome assemblies provided by the UCSC Genome Browser.To assess the genomic coverage of RRBS across a wide range of species, we simulated the restriction digest and size selection in RRBS for all annotated vertebrate genomes that were available from the UCSC Genome Browser, and we determined the expected RRBS coverage for CpG islands, transcripts, promoters, and repeats in each species. To create in silico RRBS libraries, we first mapped all MspI and TaqI restriction sites in these genomes using the The following species and genome assemblies were included in the analysis: Vase tunicate (ci3), African clawed frog (xenLae2), armadillo (dasNov3), elephant shark , Tibetan frog (nanPar1), green anole (anoCar2), medaka (oryLat2), fugu (fr3), tetraodon (tetNig2), Nile tilapia (oreNil2), kangaroo rat (dipOrd1), stickleback (gasAcu1), Atlantic cod (gadMor1), sloth (choHof1), zebrafish (danRer11), manatee (triMan1), microbat (myoLuc2), mouse (mm39), Garter snake (thaSir1), naked mole-rat (hetGla2), squirrel (speTri2), zebra finch (taeGut2), golden eagle (aquChr2), Chinese hamster (criGri1), Guinea pig (cavPor3), purple sea urchin (strPur2), brown kiwi (aptMan1), mouse lemur (micMur2), Hawaiian monk seal (neoSch1), chicken , budgerigar (melUnd1), American alligator , African elephant (loxAfr3), Japanese lamprey (petMar3), turkey , painted turtle (chrPic1), cow (bosTau9), ferret (musFur1), rabbit (oryCun2), tree shrew (tupBel1), hedgehog (eriEur2), white rhinoceros (cerSim1), wallaby (macEug2), marmoset , sheep (oviAri4), megabat (pteVam1), squirrel monkey (saiBol1), cat (felCat9), Tasmanian devil (sarHar1), golden snub-nosed monkey (rhiRox1), pig (susScr11), rhesus macaque (rheMac10), baboon (papAnu4), orangutan (ponAbe3), alpaca (vicPac2), horse (equCab3), green monkey (chlSab2), dog (canFam5), rat (rn7).Because many reference genomes had an incomplete assembly status and consisted of many scaffold sequences , we concatenated individual scaffolds into 20 arbitrary chromosomes, separating the sequences by stretches of 100 Ns. This improved software runtimes and avoided out-of-memory issues. After processing, genomic coordinates based on these artificial chromosomes were ported back to the original coordinate space to match the genome annotations.To assess biological and technical effects on our RRBS libraries and on the derived consensus references, each consensus reference fragment was evaluated based on its read coverage across all samples for a given species . For each species, the reference genome with the highest mapping rate was determined and used for further analysis. Mapping was performed using the cross-mapping function of RefFreeDMA with an allowed mismatch rate of up to 0.2 . The genomes used for cross-mapping and their preparation are described in the RRBS coverage simulation section. DNA methylation profiles across annotated genes were created by averaging DNA methylation calls within 5000 bp upstream or downstream of the gene body in bins of 100 bp, and in bins of 200 bp within the gene body itself. For sample-wise analyses the samples were kept separate, whereas all samples of a given species were combined for species-wise analyses.To validate our consensus references and to enable gene-centric analyses for a subset of species, we devised a cross-mapping workflow that connects the RefFreeDMA-derived consensus reference fragments to putative orthologous regions in available reference genomes. We identified an empirical “best fit” by aligning all consensus reference fragments of a given species to all reference genomes of species in the same class in the UCSC Genome Browser (as determined by the Bos taurus57 (GSE147087), Mus musculus56 (GSE42836), Phascolarctos cinereus58 (GSE149600), Gallus gallus66 (GSE146620), Parus major65 (SRR2070790), Xenopus laevis63 , Danio rerio61 , Callorhinchus milii25 (GSE141609), Branchiostoma lanceolatum59 , Crassostrea gigas55 (GSE40302), and Octopus bimaculoides25 (GSE141609). In addition, we obtained WGBS data for Chelydra serpentina from the authors of the corresponding paper64. Supplementary files containing CpG-wise coverage and methylation information were obtained using the R package GEOquery107 and converted into a common format containing CpG-wise read coverage and DNA methylation ratio. For Danio rerio  and Parus major (SRR2070790) no suitable files were available; hence we reprocessed the WGBS data for these species starting from the raw sequencing data using the gemBS pipeline108 with the danRer11 and the Parus_major1.1 genome assembly, respectively.To validate our RRBS-based, reference-free DNA methylation analysis against WGBS data, we identified the species in our dataset for which WGBS data were publicly available from GEO. This included: https://github.com/jklughammer/bisulfiteBlast). Where both of the two best matching species differed by more than the level of “class” from the annotated species (this was assessed using the lowest_common function in the R package taxize), samples were manually inspected and flagged as unreliable if the discrepancies could not be explained . In total, 30 samples were flagged as unreliable  and mapped 1000 random reads per sample using the NCBI BLAST command line tool with the following parameters: -max_target_seqs 100 -num_threads 4 -word_size 15 -evalue 0.00000001 -outfmt “6 qseqid sseqid sscinames scomnames qlen slen sstart send pident length evalue bitscore qseq” , and the absolute number of covered CpG sites. CpG island frequencies were calculated by determining the percentage of consensus reference fragments that fulfilled the Gardiner-Garden or Takai-Jones criteria for CpG islands111, requiring a GC content (combined C and G frequencies) of at least 50% (Gardiner-Garden) or 55% (Takai-Jones) and a CpG observed versus expected ratio of at least 0.6 (Gardiner-Garden) or 0.65 (Takai-Jones), over stretches of 50 bp.To investigate the association between genome-wide DNA methylation levels and the genomic DNA sequence composition, we calculated mean DNA methylation levels across all CpGs and across all samples for each species, and we correlated these values with three sets of features derived from the corresponding consensus reference: (i) k-mer frequencies; (ii) CG composition; (iii) CpG island frequencies. K-mer frequencies for k ranging from 1 to 3 were calculated using the MEME suite’s phylolm112 for the integration of taxonomy tree structure. The variance explained (R2) by each of the models was calculated using the R2.pred function in the R package rr2113. The R2 values were further adjusted using the Wherry formula to account for the number of variables in each of the models114. All models were additionally evaluated by the Akaike information criterion (AIC), using the AIC function. To evaluate the predictive power of the 3-mers, we used stepwise feature selection115, iteratively adding and removing the features  in the linear model. Models were compared based on the AIC using the stepAIC function in the R package MASS. Each 3-mer was assigned a stability score calculated as the percentage of bootstrap experiments in which the feature was selected for the final model.We evaluated the explanatory power of these feature groups for the observed variation in genome-wide DNA methylation levels across species, using a standard linear model as well as linear models that included the phylogenetic group annotation or the taxonomy tree as additional information. Linear models were implemented in R, using the  package dist function in R package stats116. We then performed hierarchical clustering of this distance matrix using the hclust function from the same package with default parameters, and we visualized the result as a dendrogram using the R package dendextend117.We also assessed how well 3-mer frequencies recapitulate phylogenetic distance between the analyzed species. To that end, we derived a pairwise distance matrix across species based on the global 3-mer frequencies in the consensus reference of each species, using the Finally, in the analysis of publicly available WGBS data, genome-wide DNA methylation values were calculated for each sample by averaging across the DNA methylation levels of all CpGs with coverage exceeding five reads.p-values were used for interpretation. The phylogenetic models were built with the compare.gee function in the R package ape118 assuming Gaussian distributions and using the taxonomic tree as depicted in Fig. glm function in R. For the 3-mer analysis, the p-values obtained from both models were adjusted for multiple testing using the Bonferroni method.To test for statistically significant associations between genome-wide DNA methylation levels and 3-mer frequencies , we used generalized linear models that explicitly account and control for phylogenetic relationships. Models were built individually for each factor, either with and without taking phylogeny into account, and the corresponding coefficients and associated 68. A custom Python script (which is now part of RefFreeDMA) was used to determine the number of concordantly or discordantly methylated reads with at least four valid CpG measurements for each CpG within each sample. For each CpG, the PDR was then calculated as the ratio of discordant reads compared to all valid reads covering that CpG. CpGs at the end of a read were disregarded as potentially unreliable. Finally, sample-wise PDR values were calculated by averaging across their CpG-wise values.As a measure of DNA methylation erosion, the proportion of discordant reads (PDR) was calculated as described in the original publicationkebabs119 to predict the discretized DNA methylation states of consensus reference fragments  based on the DNA sequence composition of the consensus reference fragment. From the set of sequences assigned to high and low DNA methylation state, we randomly selected class-balanced training and test sets comprising 2000 sequences each. In those species where one class contained fewer than 2000 sequences, the number of sequences for the other class was reduced accordingly, in order to avoid class imbalance.To investigate the association between locus-specific DNA methylation and the underlying DNA sequence, we trained machine learning classifiers to predict the discretized mean DNA methylation levels of individual genomic regions (averaged across samples and/or tissues in a given species) based on their genomic DNA sequence. Specifically, we trained support vector machines (SVMs) with a spectrum kernel from the R package kebabs119.For each species, training and test set sequences were transformed into feature matrices comprising all k-mer frequencies with a fixed length k. Based on the training set, the optimal regularization value (C) as well as the optimal k-mer length (k) were selected by grid search across C values of 0.01, 0.1, 1, and 10, and across k values from 1 to 10, using 10-fold cross-validation . For each taxonomic group, the top-10 k-mers with the most significant differential feature weights were reported , Mus musculus56 , Gallus gallus66 , Xenopus laevis63 , Phascolarctos cinereus58 , Danio rerio60 , Branchiostoma lanceolatum59 , and Chelydra serpentina64 . All genome assemblies were processed using the R package Biostrings104 and split in 50 bp tiles, mimicking the consensus reference fragments. Each tile was annotated with its mean DNA methylation level calculated as the coverage-weighted mean of DNA methylation values for each CpG in the tile. As in the RRBS-based analysis, only sequences with a mean coverage of at least 10 reads across all samples were retained. Sequences with DNA methylation levels >80% in all samples were labeled “highly methylated”, whereas those with DNA methylation levels <20% in all samples were labeled “lowly methylated”. The support vector machine was trained and tested with cross-validation on a balanced subset of 2000 randomly chosen sequences, while ensuring that test and training sequences did not belong to the same chromosome. This procedure was repeated three times in each species, in order to assess the stability of the results.For validation by reference-based analysis of WGBS data, we selected those species for which a suitable reference genome and WGBS data with at least two biological replicates were publicly available. The following species and datasets were included: To assess the generalizability of locus-specific prediction across species, models were trained in one species and tested (without re-training) in a different species. Model performance in each scenario was quantified by receiver operating characteristic area under curve (ROC-AUC) values in unseen test sets of the target species.actinopteri) was investigated further. To that end, we identified those 3-mers whose feature weights deviated most strongly in the inverted species, as compared to all other species in this taxonomic group. To test the hypothesis that the observed inversion in the relationship between DNA methylation and DNA sequence may be due to recent expansion of heavily methylated repeats in the inverted species, we used the identified 3-mers for repeat identification, calculating the frequencies of 3-mer derived 9-mer repeats  across all consensus reference fragments with high (>80%) and low (<20%) average DNA methylation levels.These cross-species predictions unexpectedly resulted in a few cases (13 out of 580 species) in which the observed cross-species prediction performance was systematically lower than expected by chance. We refer to those outliers as inverted species, given that the relationship between locus-specific DNA methylation and the underlying DNA sequence appears to be inverted compared to most other vertebrate and invertebrate species. We denoted species as inverted if they had average ROC-AUC values below 0.45 compared to all other species. The taxonomic group with most inverted species (2) for the mean DNA methylation levels of the consensus reference fragments across samples. The Pearson correlation was calculated using the cor function in R. The significance of the difference between the variance explained by tissue and by individual between taxonomic groups was calculated using a two-sided paired Wilcoxon test (wilcox.test function in R). Word clouds representing the relative frequency of tissues contributing to the analysis were produced using the wordcloud function in the R package wordcloud.To assess the prevalence of tissue-specific versus inter-individual differences in DNA methylation, we focused on species with samples for at least two individuals, at least two tissues, and one common tissue that was shared between individuals. We further excluded species that had <50% average CpG overlap between samples or were flagged in the validation of species annotations. For each of the selected species, we calculated the variance explained by the tissue type and by the individual as the average squared Pearson correlation  were determined by RefFreeDMA as described previously123 using the AME tool from the MEME package with default parameters124. We scored each motif for enrichment among the top-500 hypermethylated fragments relative to the top-500 hypomethylated fragments, and vice versa. Motifs with multiple-testing adjusted p-values below 0.05 were considered significantly enriched. Transcription factors were additionally annotated based on their gene expression levels in human tissues, using the consensus transcript expression levels from the Human Protein Atlas (https://www.proteinatlas.org/about/download). Only transcription factors that have normalized RNA expression values greater than one in heart or liver samples were included in the analysis. Moreover, to explore the tissue specificity of transcription factor binding, we clustered the corresponding transcription factors based on their motif enrichment in heart and liver using the default hierarchical clustering of the pheatmap function in the R package pheatmap. GO term annotations of the selected transcription factors were obtained using the GOnet125 web tool (https://tools.dice-database.org/GOnet) with a custom set of relevant GO terms . The resulting network was downloaded as a JSON file and visualized using Cytoscape126. For better visualization, connections between GO terms were cut and redundant annotations were removed. Five transcription factors were removed from the network because they were not annotated with any “Biological Processes” GO term  or only with the broad term “signal transduction” (KLF16).To identify enriched transcription factor binding motifs among the differentially methylated consensus reference fragments, we tested the binding position-weight matrixes (PWMs) from the 2020 version of the JASPAR database86. Transcription factors annotated as preferring binding to unmethylated sites whose binding sites were hypomethylated in liver (when compared to heart) were classified as “active in liver”. Similarly, transcription factors annotated as preferring binding to methylated sites whose binding sites were hypermethylated in liver (when compared to heart) were also classified as “active in liver”. Transcription factors showing the opposite characteristics were labeled “active in heart”. Using a manually curated database of human gene-regulatory networks87, we identified the potential targets of these transcription factors and visualized the resulting network with Cytoscape126.Transcription factors were further annotated with their binding preferences (methylated or unmethylated binding site) based on HT-SELEX experiments for their human homologsgene2refseq dictionary. Second, NCBI IDs were matched to their human orthologs using the NCBI gene_orthologs dictionary. Both dictionaries were obtained from https://ftp.ncbi.nlm.nih.gov/gene/DATA.Using the cross-mapping of RRBS consensus reference fragments to putative orthologous regions in existing reference genomes (as described above) as well as the gene annotations of these reference genomes, we calculated promoter methylation for each covered gene as the mean DNA methylation levels across 50 bins spanning 2500 bp upstream of the transcription start site. Gene identifiers across all genomes were then linked to their human homologs. First, the original RefSeq IDs were converted to NCBI IDs using the NCBI cor function in R. Uniform Manifold Approximation and Projection (UMAP) was then applied to these correlation matrixes using the umap function in the R package uwot with the following parameters: n_neighbors = 20, min_dist = 2, spread = 1  or n_neighbors = 15, min_dist = 0.05, spread = 1.5 . Leiden clustering for the gene-wise UMAP representation was performed using the cluster_leiden function in the R package igraph with a resolution of 0.06. Gene enrichment analysis on the genes in each Leiden cluster was performed with the gost function in the R package gprofiler2 using GO Biological Processes, GO Molecular Function, and GO Cellular Compartment as gene set databases and an FDR-corrected p-value of 0.05 as the significance threshold.For an integrated visualization of our dataset, we produced a common dimensionality-reduced representation of genes and samples, combining data across species into a shared human homolog gene space based on mean promoter methylation levels. For the sample-wise analysis, genes were included if they were covered in >100 samples, and samples were included if they had coverage for >400 genes. For the gene-wise analysis, genes were included if they were covered in >400 samples, and samples were included if they had coverage for >50 genes. This filtering strategy was optimized to produce missing-value-free sample-by-sample and gene-by-gene correlation matrices using Person’s correlation with pairwise complete observations as calculated using the rpart function in the R package rpart. Genes were included if they were covered in at least 60% of the samples, and samples were included if they had coverage for at least 12% of the genes in the assessed subset of the data. We focused our analysis on mammals and birds as the most highly represented taxonomic groups, and on liver and heart as the most highly represented tissue types. We predicted tissue type (heart versus liver) and evolutionary class  separately for mammal and bird species and for heart and liver tissues, respectively. 80 (tissue prediction) and 100 (evolutionary class prediction) species were randomly selected for model training, and the remaining species were used for model testing. We split by species to make sure that training and test data did not contain samples of the same species, thereby focusing the analyses on cross-species prediction. The procedure was repeated 100 times, and for each iteration we recorded the prediction performance  and feature weights. The prediction performance was assessed for each classification task, and average feature weights were calculated across all 100 iterations. As a control analysis, the same procedure was applied to scrambled data derived from the actual data by randomly distributing the non-missing values across the non-missing value positions. This analysis maintained the missing-value structure to ensure that this did not contribute to the observed predictions.In the shared human homolog gene space, we assessed the ability to predict sample properties  based on promoter methylation levels. We used a random forest classifier that can handle missing values, as implemented by the Supplementary InformationEditorial Assessment ReportDescription of Additional Supplementary FilesSupplementary Data


High-fat diet (HFD)-induced obesity is a risk factor for colon cancer. Our previous data show that compared to an AIN-93 diet (AIN), a HFD promotes azoxymethane (AOM)-induced colonic aberrant crypt foci (ACF) formation and microbial dysbiosis in C57BL/6 mice. To explore the underlying metabolic basis, we hypothesize that AOM treatment triggers a different fecal metabolomic profile in C57BL/6 mice fed the HFD or the AIN. We found that 65 of 196 identified metabolites were significantly different among the four groups of mice . A sparse partial least squares discriminant analysis (sPLSDA) showed that concentrations of nine fecal lipid metabolites were increased in the HFD + AOM compared to the HFD, which played a key role in overall metabolome group separation. These nine fecal lipid metabolite concentrations were positively associated with the number of colonic ACF, the cell proliferation of Ki67 proteins, and the abundance of dysbiotic bacteria. These data suggest that the process of AOM-induced ACF formation may increase selective fecal lipid concentrations in mice fed with a HFD but not an AIN. Collectively, the accumulation of these critical fecal lipid species may alter the overall metabolome during tumorigenesis in the colon. Obesity is one of the most prevalent causes of non-communicable diseases across the globe , and theMetabolic status can be potentially tracked through metabolomics as small-molecule metabolites represent a dynamic situation in response to physiological and pathological changes . UntargeTo determine the impact of the AOM treatment on the fecal metabolome in the colon, we compared the fecal metabolome of the four groups of mice fed with two different diets with and without AOM  .Daily fecal samples were collected and stored at −80 °C during the last week of the study (week 14) [To further determine the effect of AOM treatment on the overall metabolome, sparse partial least squares discriminant analysis (sPLSDA) was used to visualize the individual sample variability and overall metabolite separation between experimental groups A. There The top 10 metabolites determining component 1 separation were (1) the increased concentrations of arachidic acid , linoleic acid , cis-gondoic acid , octadecylglycerol (≥142%), palmitic acid , oleic acid (18:1n-9); ≥213%), hexadecylglycerol (≥166%), stearic acid , and palmitoleic acid  in the HFD + AOM group relative to other treatments; and (2) the increased concentration of maltose (≥44%) in the AIN group compared to other groups . Conversely, the top 10 metabolites in the HFD group for component 2 separation, relative to the HFD + AOM group, were the increased concentrations of 6-hydroxynicotinic acid (≥105%), 4-aminobutyric acid (≥63%), deoxycholic acid (≥144%), glycerol-3-galactoside (≥18%), 3-epicholic acid (≥120%), pimelic acid (≥35%), glycolic acid (≥48%), citramalic acid (≥9%), triethanolamine (≥28%), and biphenyl (≥37%) .These 65 altered metabolites  were sigAlthough the overall pathway enrichment analysis  demonstrTo further explore the potential roles of these 65 differential metabolites in human disease aspects, we conducted a metabolite set enrichment analysis. These 65 metabolites were significantly involved in 27 of the 44 reported metabolite sets in human feces . The topAs shown in As shown in Correlation heatmap analysis  revealedIn the present report, 65 of the 196 identified metabolites were significantly different among the four treatment groups . ImportaThe effects of AOM-induced ACF formation on these metabolite concentrations  suggest Metabolites of component 1 were lipid metabolites and their concentrations were greatly increased in the HFD + AOM group compared to the other three groups B. In FigThird, except glycolic acid in the AIN group, all 10 endogenous metabolites, such as bile acid byproducts of component 2, were decreased by AOM for both diets C. There To determine the biological impact of these 65 differential metabolites on the metabolism, pathway enrichment analysis demonstrated that these 65 altered metabolites  were sigIn agreement with the pathway enrichment analysis, metabolite set enrichment analysis also revealed that 65 differential metabolites were statistically matched with 27 metabolite sets . Five ofWhile the underlying mechanism of fecal lipid accumulation B remainsTo explore the functional connection, we determined the interrelation of fecal (or plasma) lipid metabolites and potential oncogenic signatures in the colon. The correlation analysis demonstrated that nine fecal lipid metabolites of component 1 B were poTM ventilated cages within a pathogen-free facility that maintained a 12 h light–dark cycle and a temperature of 22 ± 1 °C. Mice were given free access to food and deionized water. This study was approved by the Animal Care and Use Committee of the Grand Forks Human Nutrition Research Center (protocol code HZ13M2), and animals were maintained in accordance with NIH guidelines for the care and use of laboratory animals. Briefly, C57BL/6 mice were randomly assigned to either an AIN or HFD group (n = 25/group) for the entire experimental period (14 weeks). On week 3, within a given diet group, mice received either weekly intraperitoneal injections of the colon carcinogen, AOM  (n = 15/group) at a concentration of 8 mg/kg body weight [n = 10/group) for 4 weeks. At the termination of the experiment, mice were fasted for 6 h and then euthanized with a mixture of ketamine and xylazine (100 mg/kg body weight). At the end of the study (week 14), (A) fecal and plasma samples were collected and stored at −80 °C for metabolomic analysis [The study design, diet composition, and preparation have been previously reported . Three- y weight , or phosMetabolomic analysis was performed at the West Coast Metabolomics Center  ,32. FecaTo avoid a skewed distribution when performing bioinformatic analysis, the obtained (peak-intensity) data were normalized by Log10 transformation with an auto-scaling method which is highly recommended for most peak intensity data using MetaboAnalyst software  ,35. The Taken together, our results demonstrate that the process of AOM-induced ACF formation is associated with an increase in fecal fatty acids in male mice fed with a HFD but not an AIN. The accumulation of fecal fatty acids also correlates with changes in overall metabolome compositions and may be directly related to tumorigenesis in the colon in a male mouse model.