We implement several GLOS-libraries in Escherichia coli and show that GLOS-libraries indeed retain diversity during genome editing and that such libraries can be used in complex genome editing operations such as concomitant deletions. We argue that this approach allows for stable and efficient fine tuning of chromosomal functions with minimal effort.Multiplexed gene expression optimization via modulation of gene translation efficiency through ribosome binding site (RBS) engineering is a valuable approach for optimizing artificial properties in bacteria, ranging from genetic circuits to production pathways. Established algorithms design smart RBS-libraries based on a single partially-degenerate sequence that efficiently samples the entire space of translation initiation rates. However, the sequence space that is accessible when integrating the library by CRISPR/Cas9-based genome editing is severely restricted by DNA mismatch repair (MMR) systems. MMR efficiency depends on the type and length of the mismatch and thus effectively removes potential library members from the pool. Rather than working in MMR-deficient strains, which accumulate off-target mutations, or depending on temporary MMR inactivation, which requires additional steps, we eliminate this limitation by developing a pre-selection rule of  Small changes in as few as 6 to 8 bp in a spatially well-defined region result in the up- or down-regulation of translation3. Since the translation initiation rate (TIR) of any RBS can be approximately predicted solely from its nucleotide sequence3, RBS engineering is a useful tool for rationally optimizing a broad range of artificial functions, such as the signalling characteristics of genetic circuits4 or pathway fluxes for production of valuable products5. Still, in many cases the required level of translation is not known nor is the prediction of the RBS strength exact. In addition, confounding factors such as metabolic burden, essentiality of involved genes, side product formation and toxicity may apply6, and predicted expression levels may not be met due to a lack of availability of free ribosomes7. Therefore, a library approach is often needed to optimize the TIR for each of the involved genes. As full randomization of even a short part of the RBS produces a set of sequences that is too large to evaluate for a single gene, let alone for gene combinations, and in addition is heavily biased towards non-functional or weak RBSs8, a number of tools exist that use the mentioned approximate prediction of RBS strengths to design smaller libraries with a high level of functional sequences whose predicted TIRs still span the entire accessible range. They include the “RBS library calculator”9, the “MAGE Oligo Design Tool” (MODEST)10, the “Empiric Model and Oligos for Protein Expression Changes” (EMOPEC)11, and “Reduced Libraries” (RedLibs)8.Modifying ribosome binding site (RBS) strength is a popular and efficient approach to tune gene expression levels in prokaryotic systems13, the relevant functions that need tuning are located on the chromosome, presenting a considerable practical obstacle to efficient engineering14. However, the development of multiplex automated genome engineering (MAGE), by which small changes in the prokaryotic genome can be efficiently introduced using the lambda red-supported exploitation of single stranded DNA oligonucleotides as fake Okazaki fragments15, has allowed extending RBS engineering to chromosomal genes, including large scale multiplexing17. However, the mechanism of action of this targeted mutagenesis method inherently leads to effects that depend on the sequence of the mutagenic oligonucleotide: the target cell counters mutagenesis by removing mismatches during replication using its MMR enzyme MutS, and the efficiency of this process depends on the length and nature of the mismatch20. Consequently, the members of libraries of mutagenic oligonucleotides will meet with quite different fates upon transformation of the target cell. The effect can be avoided in MMR-deficient (MMR−) strains, but this results in an increased overall mutation rate  on an (at least) 6 bp-long mismatch should eliminate sequence bias. Practically speaking, this rule, which we term the GLOS rule (for genome library optimized sequences) can be easily implemented as an additional requirement for available oligonucleotide selection algorithms, in our case for RBS engineering using the RedLibs algorithm8 , which has been shown to deliver excellent allelic replacement (AR) efficiencies . The GLOS-strategy fits well to this approach, as by design all oligonucleotides will have the same mismatch length and thus AR efficiency and gRNA/target binding should be similar, avoiding additional sources of biases . Moreover, it was previously shown that lacZ can be modified with high AR efficiency28. This was important to exclude variations in AR efficiency due to the position on the genome29.While it was shown previously that both the length and nature of mismatches affect the frequency of repair in MMRE. coli’s lacZ open reading frame (ORF) with fully degenerate bases (“N6-library”) and obtained the predicted TIRs for each RBS sequence from an RBS calculator30. We used this dataset as input for the RedLibs algorithm to design an oligonucleotide encoding a smart 18-member lacZ RBS library with a TIR distribution as uniform as possible  and successful mutagenesis was selected for using CRMAGE , which left us with three nucleotides instead of four at each position . This GLOS library was then further reduced using RedLibs to generate a single oligonucleotide encoding an 18-member library with TIRs again distributed as uniformly as possible. The resulting smart library was integrated into the genome of MMR+ EcNR1 by CRMAGE again in two independent experiments. We observed an improvement of the AR efficiencies to over 98%, with 16 and 18 of the 18 library members incorporated  were not integrated as efficiently (0 to 5% abundance in the library) as oligonucleotides with higher folding energy  . Similarly, the sequences that were recovered most frequently  were 79% and 45% overrepresented in the pool  data of the synthesized oligonucleotides to detect bias before genome editing. Even though we found up to a 3-fold difference in the oligonucleotide distribution after NGS, there is no correlation with ∆G , the entire functional space can be covered with reasonable certainty. Careful analysis suggests that deviations from an ideal distribution of sequences after genome editing can be linked to some extent to external factors (such as oligonucleotide selection and/or folding energy and synthesis).In conclusion, by including GLOS into the design of a smart RBS library AR efficiency after CRISPR/Cas9-counterselection can be improved from 48% to 98% in an MMRE. coli 32 is not included on the plasmid. Indeed, overexpression of ribA, ribB, or ribC led to 2-, 3- and 2-fold increased levels of secreted riboflavin, respectively. We chose ribA and ribB for further experiments.To test the efficiency of the GLOS-RedLibs strategy on a pathway rather a single gene, we chose riboflavin production in ribA, targeting positions −11 to −16 relative to the start of the ribA ORF, and integrated it into an MMR+ strain , we were able to achieve the expression level observed with plasmid-based overexpression  in front of NR1 Fig. . We sequNR1 Fig. . SimilarScreening for riboflavin production levels among the recovered strains, we found clones producing up to 2.6-fold more riboflavin than the wild type, indicating that the library nearly covered the riboflavin production range (1- to ~3-fold wild type) expected from the initial plasmid-based overexpression experiments. Production levels of the best producer (AGGACA) were verified in triplicate Fig. . We concribA and ribB are both on the negative strand on replichore 1 and 2, respectively, their RBSs cannot be simultaneously mutated with high efficiency. Therefore, to further improve riboflavin production we combined the RBSs in front of ribA and ribB found in the respective best producer strains. We also constructed a strain that contained in front of ribA and ribB the two RBSs that were predicted to show the highest TIRs after GLOS-RedLibs library construction (GAGGGA for ribA and AGGAAC for ribB). We found that the strain containing the optimized RBSs showed a 9.3-fold increase in riboflavin production over wild type, while the strain containing the RBSs with the highest predicted TIRs showed only a 6.7-fold increase in production . The MMR− strain had undergone in addition two transformations to inactivate mutS, which is a necessary prerequisite to inactivate the MMR system. Therefore, analysing the genomic sequence of the two strains for off-target mutations should accurately reflect the impact of engineering in an MMR− background. We performed Illumina-based genome sequencing on both strains and compared the sequences to the parental EcNR1 strain. Indeed, the MMR+ strain exhibited only 4 off-target mutations, while the MMR− strain exhibited 57 off-target mutations  and used according to the manufacturer’s instructions. Chemicals were purchased in the highest purity available from Sigma-Aldrich  or BD Bioscience . Low salt Difco Luria broth base, Miller (LB Miller) was used for all CRMAGE experiments, where required supplemented with chloramphenicol at 20 µg mL−1, kanamycin at 50 µg mL−1, streptomycin 50 µg mL−1, or carbenicillin at 50 µg mL−1 for antibiotic selection. Sucrose was added to a final concentration of 5 g L−1 for selection of loss of plasmid pSEVA431_SacB. Medium M9 GCA contained 1× M9 salts36, 10 mg L−1 thiamine, 2 mg L−1 biotin, 0.5 g L−1 glucose and 1 g L−1 casamino acids. Desalted oligonucleotides were purchased from Sigma-Aldrich  or Microsynth . MAGE oligonucleotides contained four phosphorothioated bases at the 5′-end.Restriction enzymes, 37). They were inserted into the plasmid pCRISPR according to the Addgene protocol38. Shortly, pCRISPR_X plasmids were constructed by digestion of pCRISPR with BsaI followed by insertion of a double stranded linker assembled from oligonucleotide pairs recruited from primers 11–14. Plasmid pSEVA431_SacB was constructed by restriction digest-based cloning. The sacB gene including a constitutive promoter was amplified from plasmid pKO3 and HindIII and EcoRI restriction sites were included by PCR , and the rib RBS sequence (bold) (TCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATAAGCTT). Between the two genes, an intergenic region (TAATAAGCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATAAGCTT) containing the mkate2 RBS sequence (bold) was integrated. After the mkate2 gene, a 3’ region (TAGTAAGCTAGCTCGAATTC) containing a cloning site (underlined) was inserted. To express the operons, we constructed vector pSEVA261_Ptet, which allows induction of gene expression via the tet-system in response to anhydrotetracycline (aTc). It was constructed by isolation of the Ptet promoter together with tetR from plasmid pAB92 by restriction digest with SpeI and EcoRI and ligation into plasmid pSEVA261, linearized with the same enzymes. Integration of the operons as ribX-mKate2 fragments into pSEVA261_Ptet produced plasmids pSEVA261_ribX, where X = A, B, C, D, or E.Strains and plasmids used in this study are shown in Supplementary Table 6) for the N6-libraries. For the GLOS-libraries a partially degenerated sequence was identified so that each sequence in the library is a 6-bp mismatch to the wild-type sequence. Effectively, this means at each position we allowed all bases except for the original one – e.g., if the original sequence was an A the randomization would include a T, C and G at this positon. Appling this rule at 6 consecutive positions ensured that a 6 bp mismatch was created for each library member. The fully degenerate sequence, and the partially degenerate sequence that was designed according to GLOS rules (see above), were submitted to the RBS Calculator version 1.0 in the “Predict: RBS Library” mode5  in order to calculate TIR values. For each gene, this data set was then used as input for the RedLibs algorithm version 1.1 as described in the documentation provided with the algorithm’s script (Supplementary file github.com/dgerngross/RedLibs). The RedLibs algorithm was set to generate reduced libraries of 18 sequences whose predicted TIRs distribute as uniformly as possible between the minimal and maximal value of the input library  were grown in 3 mL of LB Miller supplemented with chloramphenicol at 32 °C until an OD600 of approximately 0.6 was reached. Cells were heat-shocked for 15 min at 42 °C to induce production of the Red Beta protein. The induced cells were made electrocompetent by washing 3 times with 1 mL of ice cold water. Then, each oligonucleotide was added to the cells to a final concentration in the cuvette of 2 µM and the cells were electroporated . Cells were recovered by addition of 3 mL of fresh LB Miller medium supplemented with chloramphenicol for one further CRISPR/Cas9 assisted MAGE cycle. For the second cycle cells were electroporated with 2 µM oligonucleotide and 50 to 100 ng of the pCRISPR plasmid. Cells were incubated for recovery at 32 °C, after 1 h kanamycin was added for selection of pCRISPR, and the incubation was continued overnight. On the next day, cells were plated on selective medium plates with kanamycin and chloramphenicol. Clones were analysed by colony PCR . AR efficiency is defined by the ratio of number of mutants and number of total clones. PCR primers (15–20) are listed in Supplementary Table Taq DNA polymerase . Whole genome data were analysed with the software breseq version 0.26.040. In order to investigate the composition of the oligonucleotide libraries, ssDNA was filled up by a primer extension reaction with one primer  for 24 h at 30 °C in M9 GCA medium. Riboflavin was quantified in the cell-free supernatant using an Infinite M1000 PRO microplate reader  measuring fluorescence (excitation wavelength 440 nm and emission wavelength 535 nm). Additionally, the ODGrowth curves were analysed in 1 mL LB medium at 30 °C and shaking at 800 rpm using a BioLector  device in a 48-well FlowerPlate .github.com/dgerngross/RedLibs.All data generated or analysed during this study are included in this published article (and its Supplementary Information files), the algorithm’s script used in this published article is provided on Supplementary Figures and TablesSupplementary file S1


Classical swine fever virus  to tolerate a deletion of the core protein if a compensatory mutation is present in the NS3-helicase-domain (Vp447∆c). In contrast to the parental virus (Vp447), which causes a hemorrhagic-fever-like disease in pigs, Vp447∆c is avirulent in vivo. In comparison to Vp447, growth of Vp447∆c in primary porcine cells and IFN-treated porcine cell lines was reduced >20-fold. Also, primary porcine endothelial cells and IFN-pretreated porcine cell lines were 8–24 times less susceptible to Vp447∆c. This reduction of susceptibility could be partially reversed by loading Vp447∆c particles with different levels of core protein. In contrast, expression of core protein in the recipient cell did not have any beneficial effect. Therefore, a protective effect of core protein in the incoming virus particle against the products of IFN-stimulated genes could be demonstrated.A multitude of viral factors - either inhibiting the induction of the IFN-system or its effectors – have been described to date. However, little is known about the role of structural components of the incoming virus particle in protecting against IFN-induced antiviral factors during or immediately after entry. In this study, we take advantage of the previously reported property of  The IFN-system is a powerful cellular tool to counteract virus infection at different levels  and Bovine viral diarrhea virus (BVDV) can be responsible for substantial economic losses and are notifiable in several countries. All pestivirus species can be transmitted vertically, and depending on the time point of infection of the fetuses, the results are fetal death, malformations or – if infection takes place before the development of the active immune response - the birth of persistently infected, often phenotypically normal offspring, which is immunotolerant to the virusrns – are known inhibitors of induction of the innate immune response. Npro, the N-terminal autoprotease78rns, a surface glycoprotein with RNase activity101213Two proteins unique to pestiviruses – Npro and EHowever, high IFN-α levels can be detected in CSFV infected pigs181920rns, E1 and E2 are located within the viral envelope and interact with the target cell. Inside the virus particle, core protein is associated with the viral genome, supposedly forming a nucleocapsid structure. However, high resolution cryo electron microscopy reconstructions of virus particles from several members of the Flaviviridae – including Dengue and Zika virus25Flaviviridae - with no apparent core coding region in several members∆c) were a ± 10-fold drop in virus titer in vitro and avirulence in the natural host. The aim of this study was to uncover additional functions of core protein by focusing on the potential reason(s) for its attenuation in vivo.Pestivirus particles contain four known structural proteinaceous components   and primary porcine macrophages  with the parental Vp447, on average 2.7 × 105 (ppEndo) and 6.3 × 105 ffu/ml (ppMacro), respectively, could be detected in the supernatant (∆c resulted in an average titer of 1.8 × 102 (ppEndo) and 2.2 × 102 ffu/ml (ppMacro) after 48 h .The observed properties of Vp447n mutant . This ren mutant . The und∆c. The relative susceptibility of ppEndo for Vp447 was 1.6% and 0.07% for Vp447∆c. This represents a 22-fold reduction of the susceptibility of ppEndo for Vp447∆c in comparison to Vp447 . In an attempt to reduce the affected stages to entry and (early) replication, we determined the number of infected ppEndo and the number of infected SK6-cells for the same amount of input virus. These data were then used to calculate the relative susceptibility of ppEndo (in %) when compared to SK6-cells as a measure for the increased resistance of ppEndo to infection by Vp447 and Vp447to Vp447 .∆c ranged from 3.9% (16 h before infection) to 77% (4 h after infection), demonstrating an increased sensitivity of Vp447∆c to IFN-pretreatment of host cells. Similar results were obtained when STE-cells were treated with IFN-α 16 h before infection, with average 16.4% susceptibility for Vp447 and 0.69% for Vp447∆c (n = 3). Collectively, these data suggest that the ability of Vp447∆c to infect cells with an activated IFN-system is markedly reduced in comparison to Vp447.The same assay was used to assess the effect of IFN-α treatment at different time points  on the susceptibility of PK15-cells . Here, r∆c can be taken as evidence for a protective role of core protein during either entry or replication. To elucidate whether the replication of Vp447 and Vp447∆c genomes was differently affected by IFN-treatment, IFN-treated PK15-cells as well untreated SK6- and PK15-cells were transfected with standardized amounts of Vp447 and Vp447∆c genomic RNA. The average specific infectivity per μg of genome of Vp447 was 2.4 × 105 SK6-cells and 1.5 × 105 PK15-cells, respectively. Comparable values were observed for Vp447∆c genomes, with 3.5 × 105 antigen positive cells/μg for SK6-cells and 2.8 × 105 for PK15-cells. Transfection of IFN-pretreated cells led to a 13- and 22-fold drop of specific infectivity for Vp447 and Vp447∆c genomes, respectively. This translates into a less than 2-fold reduced specific infectivity of Vp447∆c genomes when compared to Vp447, which makes it unlikely that newly produced core protein is a major factor underlying the observed loss of infectivity of Vp447∆c when the cellular IFN-system is activated.The more pronounced effect of IFN-treatment on the susceptibility of Vp447∆c particles with core protein in trans could have a more pronounced effect regarding the ability to establish an infection in cells with an activated IFN-system. To this end a cell line expressing core protein from a tetracycline-inducible promoter was established. Overexpressed core protein is not efficiently incorporated into Vp447∆c particles due to an antagonistic effect of the rescue mutation Tyr2177 in the helicase domain of NS3∆c particles with amounts of core protein comparable to the parental virus, Vp447∆c lacking the N2177Y rescue mutation (Vp447∆cN2177) was also included in the experiments. Vp447∆cN2177 can be rescued if core protein is provided in trans. Core protein was readily detectable by Western blot analysis of concentrated Vp447 and Vp447∆cN2177 particles . Hence, infectivity of core-transcomplemented Vp447∆cN2177 also nearly reached levels observed for Vp447 (16%).Subsequently, Vp447, Vp447al virus . For STE∆c and Vp447∆cN2177 upon core-transcomplementation was assessed employing ppEndo. In this model, infectivity of core-transcomplemented viruses increased from 0.33% to 1.1 for Vp447∆c and reached 5.5% for Vp447∆cN2177. The infection rate observed for the core-loaded Vp447∆cN2177 again approached levels observed for Vp447 (6.2%). From these data, it can be concluded that core protein antagonizes IFN-induced effectors that prevent infection of IFN-treated cells.To examine the above described effect also in primary porcine cells, the change of infectivity of Vp447∆c, no beneficial effect compared to the controls was observed when cells were treated with IFN-α 16 h before infection, as susceptibility ranged between 1.7–2.7%  and YFP as a control were generated using lentiviral transduction. Different core protein C-termini were chosen to account for its potential effects on subcellular localization/processing of core protein. Upon infection of these cell lines with Vp4471.7–2.7% . This warns are well defined regarding their abilities to prevent activation of the IFN-system. Yet, IFN- levels are often high in CSFV infected pigs or persistently BVDV infected animals161719202132The ability of pestiviruses to efficiently modify the host innate immune system has been the center of intense research efforts during the past decades. By now, the functions of Npro and Ein vitro and in vivo. The previously reported animal experiment demonstrated the need for core protein in the natural hostin vivo could either be due to a specific effect of core protein, or just be invoked by the less efficient virus production of Vp447∆c observed in vitro.The construction of a CSFV that tolerated a nearly complete deletion of the core protein allowed for the first time to examine the effect of a missing structural virus component on its growth ∆c for primary cells and IFN-activated cell lines indicated that core protein is fulfilling (an) essential role(s) during the viral life cycle. When looking at the far better characterized HCV core protein, multiple interactions with cellular components have been described, including modification of the innate immune response by negatively regulating the JAK-STAT pathway . This is also supported by the finding that the infectivity of Vp447 grown in core-expressing cells always exceeded the one of Vp447 grown in control cells. This might also provide evidence that the pestiviral nucleocapsid is not a strictly organized icosahedral structure consisting of a defined number of capsid proteins, but rather a polymorphous aggregate of viral genome and core protein. This is supported by single particle cryo electron microscopic reconstructions on other members of the Flaviviridae2526The possibility to partially overcome the IFN-induced antiviral state in cells by the incorporation of core protein into virus particles of Vp447PegivirusCapsid proteins are considered one of the most essential components of virus particles395) needed to transfect a single cell. Yet, this might also be at least partially due to defects/degradation of the in vitro generated transcripts and low transfection efficiency.After entry into the host cell the nucleocapsid supposedly deteriorates, facilitating the early steps of replication. In terms of a plus-stranded RNA virus the initial step of replication is translation at a ribosome which requires an uncondensed and accessible RNA molecule. Devoid of its usual protein cover, it is easy to imagine the hostile environment a single RNA-genome faces when getting released into a cell with all its RNases and RNA-binding proteins. Upon activation of the IFN-system, the situation gets even more precarious. Therefore, the likelihood of a ‘naked’ viral RNA genome to find a ribosome and to stay intact long enough to allow minus-strand synthesis seems low. This is also supported by the high amounts of viral genomes , which subsequently was transfected in 5 × 106 SK6-cells by electroporation (Bio-Rad Gene Pulser). Transfection efficiency was assessed 14 h after transfection by immunohistochemistry specifically staining CSFV E2 with the mouse monoclonal antibody A18. Virus containing supernatant for infection experiments was harvested 36 h after electroporation, clarified by centrifugation for 5 min at 3000 × g and stored in aliquots at −70 °C.All cells employed in this study, SK6-, PK15- and STE-cells, as wells as primary porcine cells were grown in Dulbecco’s modified Eagle’s medium supplemented with 10% BVDV-free FCS at 37 °C and 5%COTiter was determined in ffu/ml on SK6-cells employing 10-fold dilution steps. 14 h after infection, cells were stained by immunohistochemistry as described above and foci of infected cells were counted using a Nikon Eclipse TS100 microscope.PpEndo were harvested from aortas of ~6 months old feeder pigs obtained at the local slaughter house. Therefore, aortas were freed of all adjacent tissue, washed several times with PBS and the intimal layer of the aorta was then scraped off with a scalpel blade. Cells were immediately seeded on tissue culture plates and used for experiments as soon as cells had grown to confluency.6 cells were seeded in teflon bags to allow for the differentiation into promacrophages. After 7d, cells were seeded on tissue culture plates and employed for infection experiments.PpMacro were isolated from buffy coats derived from EDTA blood of ~6 months old feeder pigs. Peripheral blood mononuclear cells were separated from other blood components by gradient centrifugation employing Ficoll Paque  (1200 g 45 min). After 2 additional wash steps with PBS, 5 × 105 SK6-, PK15-, STE-cells, ppEndo or ppMacro were seeded in 24 well plates. If applicable, cells were treated with 300 IU human IFN-α2A  16 h prior to or 4 h after infection. Cells were infected with a MOI of 1 for 4 h. Thereafter, medium was exchanged and virus titer was determined 24 h and 48 h after infection as described above.1 × 105 SK6- or PK15-cells or ppEndo were seeded in 24 well plates. Porcine cell lines were routinely treated with 300 IU human IFNα2A  16 h prior to infection (if not indicated otherwise). Ten-fold dilutions of the viruses to be tested were used for infection. 4 h after infection, the medium was exchanged to a medium containing 0.5% methylcellulose. 24 h after infection, cells were stained by immunohistochemistry and infected foci were counted. The permissiveness was calculated in percent employing the following formula:1 × 10−2, 10−3, 10−4, 10−5) of 2.5 μg in vitro transcribed viral genome were transfected in SK-6 or PK15-cells resuspended in PBS by electroporation . IFN-treatment was performed 16 h prior to transfection. After transfection, cells were seeded on 6 well plates and the medium was exchanged to medium containing 0.5% methylcellulose 4 h after transfection. 24 h after transfection, cells were stained by immunohistochemistry and the number of infected foci per genome dilution was determined.Equal dilutions .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The degree to which debris-covered glaciers record past environmental conditions is debated. Here we describe a novel palaeoclimate archive derived from the surface morphology and internal debris within cold-based debris-covered glaciers in Antarctica. Results show that subtle changes in mass balance impart major changes in the concentration of englacial debris and corresponding surface topography, and that over the past ∼220 ka, at least, the changes are related to obliquity-paced solar radiation, manifest as variations in total summer energy. Our findings emphasize solar radiation as a significant driver of mass balance changes in high-latitude mountain systems, and demonstrate that debris-covered glaciers are among the most sensitive recorders of obliquity-paced climate variability in interior Antarctica, in contrast to most other Antarctic archives that favour eccentricity-paced forcing over the same time period. Furthermore, our results open the possibility that similar-appearing debris-covered glaciers on Mars may likewise hold clues to environmental change. The degree to which debris-covered glaciers record past environmental conditions is debated. Here, the authors show that obliquity-paced variations in solar radiation over the past ∼220 ka are expressed in Mullins glacier as repetitive changes in englacial debris and corresponding surface topography. Glacial cycles during the mid-Pleistocene transition 700–1,250 ka) underwent a major reorganization in periodicity, from ∼41 to ∼100 ka, followed later by a second transition at ∼400 ka, when the amplitude of interglacial thermal extremes increased (the mid-Brunhes event),250 ka u234585–106 a. Using a numerical model of glacial mass balance, we explore the sensitivity of these glaciers to the range of climate forcing experienced in the region over the past ∼250 ka. These results are combined with morphological observations, chronological constraints and a geomorphic model for debris entrainment to detect tractable links between past climate change and preserved glacier structure.To address this question, we analysed the surface and internal characteristics of two small (<6.5 km long and ∼125 m thick), north-facing debris-covered alpine glaciers10111113−1  mass wasting and snow accumulation at headwall cliffs and (2) exceedingly slow rates of ice loss beneath protective supraglacial debris1920−1. Down glacier, the rate of ice loss decreases rapidly and ranges from a maximum of ∼0.05 cm a−1 (at the onset of continuous surface debris cover <5 cm thick) to ∼6.6 × 10−7 cm a−1 beneath debris ∼50 cm in thickness1922At present, exposed ice in the critical zone of both Mullins and Friedman glaciers ablates at ∼7 cm aTwenty-four kilometres of overlapping and multiple-frequency ground-penetrating radar (GPR) transects, 21 shallow ice cores, and >40 near-surface ice excavations on Mullins and Friedman glaciers were collected and analysed by Mackay, MarchantFollowing121925What process might drive similar changes in mass balance in the glacier critical zones of both Mullins and Friedman glaciers? To address this question, we use a numerical energy balance model (EBM) for point mass balance (accumulation – ablation) as input to our conceptual model for the formation of rocky surface lags in critical zones of debris-covered glaciers .We next reconstruct broad changes in mass balance—and associated IDL formation periods—in the critical zones of Mullins and Friedman glaciers during late Pleistocene time. To accomplish this, we repeat our modelling exercise but this time use proxy data (inferred precipitation and temperature) from nearby Taylor Dome (<40 km distant)31A chronology for Mullins Glacier, based on cosmogenic-nuclide exposure ages of surface clasts and the integration of modern, horizontal ice-surface flow velocities for both Mullins and Friedman glaciers13These results highlight the sensitivity of cold-based debris-covered alpine glaciers to obliquity forcing in a region where ∼100 ka forcing dominates most other Pleistocene climate archives2345More broadly, these results highlight the importance of critical zone ice dynamics in the development of cold-based, debris-covered glaciers. Similar-appearing ASDs on other debris-covered glaciers in Antarctica343519Finally, our conclusion that highlights the importance of integrated summer energy in high-latitude glacial dynamics may also have implications for the dynamics of nearby blue ice areas in Antarctica2244454748We investigate mass balance within the critical zone of Mullins and Friedman glaciers  with an To accomplish this, high-temporal resolution (minimum ∼1 h) meteorological data are required to model small  changes in point mass balance. This is particularly true for glacial systems that are strongly impacted by diurnal meteorological forcing−1  is located immediately adjacent to the primary meteorological station (AWS01), and is ∼100 m down valley from, and 15 m lower, than the modern equilibrium line. L01 is used to validate our model under modern conditions; it is also used to test the relative magnitude of accumulation/ablation forcing and its resulting impact on mass balance in the critical zone for past climate configurations. When modelled over conditions representative of the past 230 ka, the mass balance at L01 shows cyclical fluctuations in magnitude, but remains negative at each time step. We modelled a second location (L02), situated ∼400 m up-valley and ∼100 m higher than L01  and adjust L02's meteorological parameters from those measured at L01 by including (1) a constant air temperature decrease of 1 °C according to an average lapse rate of 9.8 °C km−1 ref. , and (2)Starting at 230 ka BP and moving forward in time at 0.5 ka time steps, we couple the mass balance results for L02 to a geomorphic model for the development of supraglacial debris/IDL under conditions of negative mass balance (see below). The geomorphic model computes the accumulated thickness of supraglacial debris over time. When the mass balance at L02 returns to a positive value, we assume burial of the supraglacial debris layer .The following sections provide details on the above model framework, and include additional data for site conditions, meteorological records, meteorological transfer functions, numerical models used and model validation.−2 and a resolution of <1 W m−2), (7) upwelling short-wave solar radiation , (8) downwelling longwave radiation , (9) upwelling longwave radiation , and (10) internal CNR4 housing temperature (Pt-100 thermistor). For this study, we use data from AWS01 during the period from 01/01/2011 to 12/31/2012  at four locations/31/2012 . This tw/31/2012 —indicateTo assess aerodynamic roughness near AWS01, we installed a vertical array of four Onset Computer Corporation cup-style anemometers spaced at roughly logarithmic heights of 0.28, 0.66, 1.34 and 2.2 m, ∼3 m from AWS01; this array sampled data every 30 s, stored at 5  min intervals, for average wind velocity, maximum gust velocity and wind direction (top anemometer only) for the period 11/29/2010 to 12/09/2010. Wind data were processed with a 15-min running average to reduce noise.−3) of fresh snowfall collected on site during the 2011 and 2012 field seasons.Snowfall in Mullins and Friedman valleys is sourced from a combination of (1) direct atmospheric precipitation, typically associated with coastal storm tracks, and (2) wind-transported snow sourced from the East Antarctic Ice Sheet (EAIS)−1 (σ=1.86) and 6.86 cm a−1 (σ=2.1), respectively, with the stake located closest to AWS01 averaging a slightly higher ablation rate of 7.1 cm a−1. Horizontal displacement down valley for all stakes averaged 9.2 cm a−1 for Mullins and 10.1 cm a −1 for Friedman glaciers.We measured ablation directly by installing an array of 1.25-inch PVC ablation stakes in the uppermost ablation areas of both Mullins  and Friedman glaciers z0a) at AWS01  of ln(z-d) verses u(z) where z is the measurement height, d is the displacement height (∼0 m), and u(z) is the wind speed at height z. This resulted in a computed mean aerodynamic roughness of z0a=0.0065, m over the sampling period. This value is higher than values for z0a typically reported for ablating glacier ice and snow areas  during our measurement period.We used our vertical wind-profile measurements to derive an estimate for the local mean aerodynamic roughness  is not constant, but rather depends on surface cover  and zenith angle56K ↑) to global downwelling shortwave (K ↓) radiation K↓. We disregard the data following fresh snowfall events in order to include in the mean of only those measurements made over bare ice. We do not model the zenith angle dependency of albedo. We address the modelled albedo change through time as a function of changing surface type below.Albedo  as reported by ref. We set the albedos of fresh snowfall and firn to the mean of published values  (45 km distant), referenced to the st9810 timescale418O record is converted to temperature deviation from modern conditions according to the calibrations of Steig, Morse10Be, using relationships between the measured concentration and fluxTo derive representative paleoatmospheric temperatures and past ice-accumulation rates for Mullins and Friedman glaciers, we use the δχ is the parameter of interest and Ψ is a paleo proxy. The subscripts index time such that χydh represents the value of χ at year y before present (BP), day of year d, and hour h. The notation χdh0 represents the modern (at year zero BP) measured values for the parameter of interest.In order to execute the EBM under estimated paleoclimate conditions, we require a set of transfer functions for each input parameter  at our input time step (1 h) from the available proxy data. In the following sections, we develop equations of the form θydh) by assuming a simple linear relationship between the variation in temperature at AWS01 on Mullins Glacier and the average annual atmospheric temperature deviation at Taylor Dome. We apply a constant adjustment to each measurement point such thatWe estimate past mast height temperatures  in a format suitable to our EBM approach (Pydh), we assume that the total annual precipitation (including atmospheric precipitation and wind-blown snow) at the headwall of Mullins and Friedman glaciers is linearly related to the average annual net ice accumulation rate at TD . Implicit in this approximation are the assumptions that (1) net accumulation at TD is directly proportional to precipitation at TD , and (2) that fluctuations in precipitation at TD co-vary with precipitation (including wind-blown snow) at our nearby study site. We preserve the timing and duration of our measured snowfall events and alter only their magnitude  such that the precipitation is adjusted with the function.To estimate past snowfall  of measurement hours where non-zero precipitation occurs.where L↓) is a function of the integrated temperature of the atmospheric column and its effective emissivity. We estimate paleo values of L↓ydh for our model by directly adjusting our modern measured L↓0dh based on changes in temperature, θydh. To do this, we adopt the BrutsaertL↓ between modern conditions and a prescribed paleo atmospheric condition, and then adjust our measured L↓dh0 by this amount to arrive at L↓ydh. This procedure is represented with the equationsDownwelling longwave radiation (Andσ is the Stefan-Boltzmann constant (5.67 × 10−8), and e is the actual vapour pressure at screen height, and θydh is determined from equation θ) and the dewpoint using the approximation of ref. L↓ydh is the assumption that RH under previous climate conditions was similar modern values.where K↓) at ground level, we first compute the difference between the modern and past daily average top of atmosphere (TOA) solar insolation, denoted To estimate past intensity and seasonality of downwelling shortwave radiation   , we compdewalls) . Using tB (0.02) is an amplitude factorwhere We implemented a surface energy balance model (EBM) using source code derived from the open-source DEBAM of Hock and HolmgrenWithin the EBM, all components of the radiation balance and heat fluxes are estimated at each time step to address the governing energy balance equation:QM is the energy available for melt, Rn is the net radiation, QH is the sensible heat flux, QL is the latent heat flux, QG is the ground heat flux, and QR is the sensible heat flux supplied by rain. We disregard QR due to climate conditions that prohibit rainfall at our study locationQM ≈ 0 for our system, but do not enforce this constraint.where K↓ is the downwelling shortwave solar radiation, L↓ is the downwelling longwave radiation, and L↑ is the upwelling longwave radiation from the snow/ice surface. K↓ and L↓ are from prescribed meteorological inputs. L↑ is equal to Ts is the modelled surface skin layer temperature determined from the ice/snow temperature and density model66The net radiation is defined as QH and QL are computed following a bulk aerodynamic approachz0θ) and vapour pressure (z0e) are computed from the measured value for aerodynamic roughness length (z0a) based on the procedure of ref. The turbulent heat fluxes Ts) and ground flux components are calculated from the multi-layer ice/snow temperature and density model of ref. The surface temperature  characterizes the energy exchanges through convection, conduction, radiation and vapour diffusion, and is computed according to ref. Ts) is determined from a linear interpolation of temperatures of the uppermost two layers. To maintain stability within the overall EBM, the multi-layer ice/snow temperature and density model is executed at a time step of 1/100 of the main execution time step as determined by the temporal resolution of the meteorological input data (1 h). Vertical layers are added to the model as snow accumulates via precipitation and are removed as a function of modelled ablation.where Ts), the latent heat term (QL) and the energy available for melt (if any) (QM), represented by the residual in equation TS is <0 °C, then ablation (A)  is entirely accommodated through sublimation/resublimation as λS is the latent heat of sublimation in units of J kg−1, step is the model time step and the direction of latent heat flux (the sign of QL) determines if mass is lost or gained in the system. If A) is determined byThe model includes both positive (resublimation) and negative (melt and sublimation) mass fluxes to compute the total ablation. At each time step, these are determined from the surface temperature  is then simply the accumulation combined with the ablation in units of water equivalent.where θs) as 262°K from L↑ is the measured longwave upwelling at time t=0, σ is the Stafan–Boltzman constant and ɛi is the emissivity of ice (0.99). For model runs under synthetic paleo conditions, we set the ice-surface temperature to be equal to the air temperature at hour zero (θy00). We initialize the subsurface temperature profile using model results of Mackay et al.The surface condition at the start of each model run is assigned as bare glacier ice. For model validation under current conditions (see below), we initialize the ice-surface temperature  that will be used in the model scenario.−1) agrees well with the collocated ablation stake at L01 (7.1 cm a−1) and within margin of the measured mean of all ablation stakes in the critical zone of Mullins Glacier (5.56 cm a−1). Finally, when large (>2 cm snow depth) snowfall events occurred over bare ice, we use the time-lapse camera record to confirm the modelled dynamics of snowpack evolution and ablation. Although limited in temporal resolution, this exercise demonstrates excellent agreement between the observed and modelled ephemeral snow pack ablation rate.When executed using modern meteorological conditions and tuned to measured initial conditions , the EBMFollowing Mackay and Marchanth(t) is the thickness of the supraglacial debris layer at time t, c is the englacial debris concentration (by volume), Aeff is the effective ice down-wasting rate, ɛd is the bulk erosion rate of the supraglacial debris, ϕ is the average bulk porosity of the resulting supraglacial debris and Γ is the average rockfall accumulation rate.where ϕ and ɛd as constants equal to 0.3  is spatially variable and depends on the distance from the headwallVS is the volume of supraglacial debris (3.4–3.9 × 105  m3) estimate by integrating the measured supraglacial debris thicknessVE is the volume of englacial debris (2.8–8.4 × 105  m3) estimate by multiplying the measured glacial ice volumeArf is the estimated area of the rockfall accumulation zone (5.4 × 105 m2) determined from GIS and the model results of Mackay et al.TIDL6 is the estimated surface age at IDL 6 (215 ka)−3  cm a−1.The rockfall accumulation rate (Aeff) in equation h). As mass balance initially turns negative, incipient supraglacial debris (<∼1 cm) is likely sparse and would only impact ablation dynamics through localized changes in albedo. Under this scenario, we set the effective ice down-wasting rate equal to the modelled mass balance for the clean ice case . Once the debris layer evolves to become a continuous sheet, it is no longer appropriate to include mass gains from snowfall , and we equate the effective ablation rate to the modelled ablation (A) . For continuous supraglacial debris thicker than a few cm, the ablation rate becomes vapour-diffusion-limitedFor the case where ice is essentially bare, without debris cover, the downwasting rate is set equal to the EBM mass balance model results (which may include a positive ice growth contribution from precipitation).Hp1≈1 cm, the downwasting rate is set to the bare-ice EBM mass balance results, except that accumulation from snowfall is ignored .For the special case where debris thickness=Hp2 ∼3 cm, the downwasting rate is determined by the (parameterized) vapour diffusion model.Finally, for ice covered with debris thickness ≥The effective ice down-wasting rate (This is represented as:−1), h is the debris thickness (cm), A is the EBM modelled ablation rate (cm a−1), Hp1 is the first supraglacial debris thickness threshold (1 cm), S(H) is the ablation rate according to the exponential ablation model of Mackay and Marchanth, Hp2 is the second supraglacial debris thickness threshold (3 cm).where MB is the EBM modelled clean-ice point mass balance  is the approximate debris thickness at which a ‘continuous' debris cover is observed in the field. While fresh snowfall can persist and accumulate on bare ice/perennial snow banks, direct observations and time-lapse camera data (see ‘Methods' section) show that snowfall rapidly ablates off continuous debris covers. The value of threshold #2  corresponds to the minimum approximate debris thickness at which the ablation of polar debris-covered ice can be reasonably represented by a vapour diffusion model. The exact value for this parameter is currently not well constrained. However, 3–7 cm is broadly indicated by the measurements/modelling of ref. The value of threshold #1 (h≤Hp1 and Hp1<h<Hp2), we apply a simple linear ramp between each case in equation In order to model the down-wasting rate for debris thicknesses between these defined debris thickness thresholds  is the ablation rate according to the model of Mackay and MarchantAy is the EBM ablation rate at year y BP, and S(ht-1) is the ablation rate according to the model of Mackay and Marchantwhere Finally, we generate estimates for the thickness of supraglacial debris by simultaneously solving equations Chronological control is based on cosmogenic-nuclide exposure ages of surface clasts on Mullins Glacier, the integration of modern, horizontal ice-surface flow velocities for both Mullins and Friedman glaciers13As discussed in detail in Mackay and MarchantAs an independent approach to refine this chronology, we use measured horizontal surface flow velocitiesThe large range in the above estimates is due to computing the surface age based on either (1) dividing the measured horizontal distance by the mean velocity of the entire region of interest or (2) integrating the measured surface ice-flow velocities at specific points along the glacier (smoothed with a moving average filter of 50 m) see ref. . Both aphttp://people.bu.edu/marchant/databases/index.html.The data that support the findings of this study are available from the corresponding author upon reasonable request. A subset of these data are available at How to cite this article: Mackay, S. L. & Marchant, D. R. Obliquity-paced climate change recorded in Antarctic debris-covered glaciers. Nat. Commun.8, 14194 doi: 10.1038/ncomms14194 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures and Supplementary References.


DNMT1 and DNMT3a 3′UTR. Results indicated a significant decrease in global methylation in both liver and muscle, with an associated decrease in DNA methyltransferase activity and DNMT3a transcript abundance. There was a significant increase in one specific candidate miRNA (miR29a) that was predicted to bind to DNMT3a. Taking a comparative genomics approach, the binding sites of miR29a to the DNMT3a 3′UTR was compared across species, spanning fish to mammals, and revealed a highly conserved binding motif that has been maintained since the vertebrate ancestor, approximately 500 million years ago. This research establishes that miRNA act as an essential mediator between the environment and DNA methylation patterns via DNMTs, which is further confirmed by a genomic regulatory mechanism that has been deeply conserved throughout evolution.The objective of this study was to examine the regulation of DNA methylation following acute (24 h) and prolonged (14 d) exposure to low (1 ng/L) and high (10 ng/L) benzo[a]pyrene. However, with the recent release of the rainbow trout genome, we were able to conduct a more detailed analysis regarding the regulation of the enzymes involved in DNA methylation; DNA methyltransferases (DNMTs). Bioinformatic approaches were used to identify candidate microRNA (miRNA) that potentially bind to the  However, it is accepted that alterations in the functionality of DNMTs can lead to global instability in transcription rates and contribute to numerous pathological conditions including cancer6. Methylation patterns are established through several different DNMTs, two of which are of interest to this study: DNMT1 and DNMT3a. DNMT1 is termed the maintenance DNMT as it interacts with hemi-methylated DNA7. During DNA replication, DNMT1 is localized to the replication fork where it copies the methylation patterns to the newly synthesized strand, thus maintaining the methylation profile8. Additionally, DNMT1 repairs DNA methylation on strands that have lost their methyl group9. In contrast to DNMT1, DNMT3a is termed a de novo DNMT7, and functions by methylating CpG sites in DNA, which ultimately leads to gene silencing10. While DNMTs are essential in regulating methylation patterns in DNA, recent studies have suggested that other epigenetic mechanisms play an essential, tandem role in DNA methylation.DNA methylation is a well-established epigenetic process wherein DNA methyl transferases (DNMTs) transfer a methyl group to cytosine residues adjacent to guanines (CpG sites) in the promoter region of specific genes. By inhibiting the binding of regulatory regions by transcription factors, DNA methylation is typically associated with gene silencing11, breast cancer12, bronchopulmonary dysplasia13, and liver fibrosis14, which accentuate the pivotal role of miRNA in disease progression and pathology. MicroRNA are short (~20–22nt), non-coding RNA that exert a pleiotropic effect on multiple targeted transcripts through both normal and pathological conditions within the cell15. The evolution and conservation of miRNA are well established in the vertebrate lineage, and studies have suggested that the expansion in the number of miRNA families are strongly associated with the evolution of organismal diversity and complexity19. However, while conservation in miRNA sequence persists in vertebrates, miRNA target site conservation in the 3′UTR is poorly understood. Several studies suggest that the 3′UTR are under selective pressures to maintain complementarity to the corresponding miRNA21, while others have demonstrated a rapid evolution in target sites22. More recently, Xu et al.23 demonstrated that while target sites are evolutionarily conserved in a given species, this conservation is progressively lost in a step-wise manner through expanding taxonomic groups. This suggests that similar phenotypic responses in species to environmental perturbation have evolved both distinct and comparative mechanism of transcriptional regulation, and caution needs to be taken when comparing miRNA and target regulation in mammalian literature versus more distantly related species, such as teleosts.Many recent studies have suggested that a variety of microRNAs (miRNA) specifically target DNMTs in leukemia25, and attribute these changes to the repression in several DNMT expression patterns. B[a]P is a polycyclic aromatic hydrocarbon (PAH) formed by the incomplete combustion of organic material and is found ubiquitously in the aquatic environment26. It is an established carcinogen and has been linked to multigenerational effects through alterations in DNA methylation patterns28. Further, there are numerous studies confirming a variety of miRNA that respond to B[a]P exposure, both in mammalian cell studies29 and C. elegans30. It is therefore ideal to understand the molecular mechanisms by which B[a]P exposure leads to global changes in the methylation, and the potential regulatory mechanisms of methylation patterns through miRNA.Recent evidence demonstrates that Benzo(a)pyrene (B[a]P) plays a significant role in the hypo-methylated state of DNA, both global and specific, in mammalian and teleost modelsin silico bioinformatic analysis to investigate the interplay between DNMTs, miRNA, and their conserved binding sites in rainbow trout exposed to B[a]P. First, using rainbow trout (Oncorhynchus mykiss), we examined the impact of acute and chronic B[a]P exposure on the global methylation pattern and DNMT activity in the liver, as this is the major site of B[a]P detoxification26. Second, mining the recently published rainbow trout genome31 and miRNA transcriptome32, we predicted miRNAs targeting the 3′UTRs of DNMT1 and 3a, which were then experimentally quantified using quantitative PCR. Lastly, given the fundamental importance of DNMT involvement in vertebrate epigenetic regulation during stress and disease34, we examined the degree to which the predicted miRNA binding are conserved throughout vertebrate genomes. We demonstrate that a specific miRNA (miR-29) is elevated upon B[a]P exposure, and identify ultra conserved miR-29 binding sites in the DNMT3A 3′UTR. Our work reveals a conserved regulatory mechanism connecting environmental stress and DNA methylation via DNMTs.In this study, we used a multidisciplinary approach involving enzymatic analysis, quantitative PCR, and DNMT3a was a primary driver of the decrease in methylation activity and patterning in the liver of rainbow trout following B[a]P exposure.Global DNA methylation was significantly down regulated in the liver after exposure to 10 ng/L B[a]P, independent of exposure time Fig. . This de4 (1.17 ± 0.06 mg/L), NH3+ (0.04 ± 0.01 mg/L), and dissolved oxygen (10.34 ± 0.1 mg /L) were consistent with healthy housing conditions, and there were no detectable differences across tank replicates or treatments. Therefore, we conclude that the observed differences in global methylation, DNMT activity and transcript abundance were associated with the B[a]P treatment alone.Liver somatic index (1.32 ± 0.04) and condition factor (1.04 ± 0.01) calculated on samples fish fell within the expected range of standard animal health, and there were no detectable differences across tanks or treatments. Further, measurements of pH (8.22 ± 0.03), NH38, we hypothesized that miR-29 may also be regulating O. mykiss DNMT3A in response to B[a]P treatment. Supporting this, we identified several miR-29 binding sites in the 3′UTR of DNMT3A . To examine differential abundance of these miRNAs following B[a]P exposure, we performed quantitative PCR on the relative abundance of miR-29 and also several miRNAs predicted to target the 3′UTR of DNMT1, including omy-Let-7e, omy-miR-18c, omy-miR-219a, and omy-miR-458. MiRNA predicted to target DNMT1 demonstrated no significant patterning associated with B[a]P exposure, although there was some fluctuations in omy-miR-18c and omy-miR-458 during the initial 24 h exposure to 1 ng/L B[a]P , demonstrated a significant increase in relative abundance that strongly correlated with the decrease in target abundance . Further guided by comparison with the rainbow trout cDNA from the NCBI EST database , we identified a longer putative 3′UTR extending to 3,385 in length. The 3′UTR was followed by a poly-A tail as expected and is more consistent with the 3′UTR length  of the reference zebrafish Dnmt3ab gene from the Ensembl database (accession # ENSDARG00000015566.1).In order to investigate the molecular basis for miR-29 mediated regulation of DNMT3A following B[a]P exposure, we examined the 3′UTR of DNMT3a 3′UTR in both zebrafish and humans P plays a significant role in altering the epigenome of rainbow trout. Acute and chronic B(a)P exposures resulted in the dysregulation of miR29a. Ultimately, these exposures led to potentially detrimental hypomethylation of genomic DNA, a hallmark of genomic instability and cancer, through a miR29a-DNMT3a regulatory pathway that is conserved across 500 million years of evolution. Moreover, bioinformatics analysis provides further evidence that miR29a is a major regulator of epigenetic methylation enzymes and its regulation is an interface between the environment and the genome.Our research strongly substantiates the importance of miRNA regulation, especially miR29a, on the epigenome. MiRNAs act as the interface between environment and the expression of genes, regulating changes in order to combat exposures to environmental stressors. Numerous environmental studies examining changes on the miRNA transcriptome have demonstrated a clear association between environmental factors, such as diet, drugs, and pollutants, with changes in miRNA abundanceish Fig. . Previou40. During development, genomic methylation reprogramming occurs, causing the loss of certain parental methylation patterns. After various developmental time points, specific methylation patterns are restored, which has broad developmental potential during differentiation41. It has been effectively demonstrated in several fish species numerous environmental and developmental factors affect methylation patterning, such sex reversal in fish42, metamorphosis in lamprey43, adaptive responses to salinity in brown trout44, and contaminant exposure in eels45. Common amongst these studies is the disruption of the methylation patterning driven by changes DNMT activity. While DNMT function is critical in early development, its dysregulation has been studied in relation to numerous pathologies, including a variety of cancers46. In cancer, global DNA hypomethylation, a hallmark of genomic instability, is the most common epigenetic phenotype that is observed47. As a result, it is not surprising that inactivation or loss of DNMT protein expression plays a causal role in promoting global DNA hypomethylation and subsequent tumorigenesis47, evidence that supports the hypomethylation pattern we have demonstrated in trout exposed to B[a]P P exposure. One exception between results herein is that DNMT1 levels did not show a decrease in response to change in miR-29a, however protein quantification would be required to confirm this discrepancy. The research by Fabbri et al.53 also stresses the importance of protein quantification of DNMT1 and DNMT3 by further confirming relationships between candidate miRNA expression changes and their downstream DNMT targets. Furthermore, this research compliments recent literature by demonstrating that other miRNA exhibit effects on the genome and that environmental stressors trigger expression changes in these miRNA. In toxicological studies in trout embryos exposed to B(a)P, morphology and developmental defects were observed54. Depletion of yolk sac, kyphosis, lack of cellular pigmentation and defects or lack of eyes were observed in trout exposed to B(a)P54. From our results, some of these observed abnormalities could be explained by epigenetic changes through DNMTs, however further research would be required. This research establishes that miRNA act as a mediator between the environment and epigenetics through DNMTs, which is further observed by an evolutionarily conserved regulatory relationship between miR-29a and DNMTs in mammals and teleosts.Additionally, the exposure to B(a)P caused an increase in miR-29a and miR-202, which is mirrored in a variety of cancersO. mykiss; immature, mixed sex, 25.2 ± 0.4 g) were obtained from Silver Creek Aquaculture  and acclimated for at least 2 weeks in 200 L aerated tanks with constant water flow at 13 °C and a 12 h light: 12 h dark photoperiod prior to experimentation. Although there were mortalities that occurred during the course of the experiment, these were likely related to the general condition of the animal upon arrival, as mortalities were noted in all tanks . Fish were then moved into separated aquariums and allowed to acclimate for 1 week prior to the start of the exposure. For each of the exposures, there were 9 fish placed in individual tanks, and tanks were replicated 3 times for each exposure regime . All tanks were dosed continuously in a flow through manner for the duration of the experiment. Animals were monitored throughout the duration of the treatment. After 24 h and 14 d, fish were terminally anesthetized with an overdose of buffered tricaine methanesulfonate , weighed, whole livers were removed which were immediately freeze-clamped in liquid nitrogen, and stored at −80 °C. All liver tissue was first ground to a fine powder under liquid nitrogen, weighed into aliquots, and stored at −80 °C until processed to reduce potential issues with thawing. For this experiment, only 3 fish from each tank replicate were used, with the remaining fish repurposed for a separate experiment. Therefore, the total number of fish sampled per treatment was 9. All experimental procedures followed strict ethical guidelines and were approved by the University of Waterloo animal care committee (AUPP #14–15).Rainbow trout  following the manufacturers instructions. DNA was quantified on a SpectraMax 190 spectrophotometer (Molecular Devices) using a SpectraDrop microvolume microplate (Molecular Devices). Once extracted, DNA was stored at −20 °C until required. 100 ng of DNA was used to assess the global methylation in the liver of rainbow trout. Global methylation was determined via MethylFlash™ methylated DNA colorimetric quantification kit (Epigentek) following the manufacturers explicit instructions. Global methylation is expressed as a percentage of cytosine methylation as determined by the provided standard curve. Total DNMT activity was determined using the EpiQuik™ DNMT activity/inhibition assay ultra kit (Epigentek) following the manufacturer’s protocol. Briefly, nuclear proteins, including DNMTs, were extracted from 20 mg of frozen liver tissue using the EpiQuik™ nuclear extraction kit (Epigentek). 10 μg of nuclear extract was used for each liver sample and assessed using the DNMT activity kit. Total DNMT activity is presented as relative activity compared to the control.31, we retrieved and analyzed raw genomic contigs from the Genoscope Resource (http://www.genoscope.cns.fr/trout/). TBLASTN searches using zebrafish Dnmt6 (uniprot identifier B3DGZ8) yielded the gene identifier GSONMT00080191001 as the top-scoring hit . BLAST searches against the NCBI nr database with GSONMT00080191001 as the query yielded Salmon Dnmt3a with 99% identity , confirming GSONMT00080191001 as Dnmt3a from rainbow trout. TBLASTN searches using zebrafish (Dnmt1) (uniprot identifiers B3DKL6 and B3DKL7) yielded the gene identifier GSONMT00021648001 as the top-scoring hit . BLAST searches against the NCBI nr database with GSONMT00021648001 as the query yielded Salmon Dnmt1 isoforms 1–4 as the top queries with 98% identity .To identify the Dnmt3a gene and associated 3′UTR within the recently published rainbow trout genome55. Primer information is listed in Table 32. Total RNA, including microRNA, was extracted from 25 mg frozen liver tissue (n = 9) using the Qiagen miRNeasy kit (Qiagen). Total RNA and microRNA concentrations and purity were validated spectrophotmetrically using the SpectraDrop microvolume microplate on a Spectramax 190 (Molecular Devices). Additionally, a small aliquot of each sample was run electrophoresed on an agrose gel to validate RNA integrity. First-strand cDNA for quantification of DNMT1 and DNMT3a transcripts was synthesized using the QuantiTect Reverse Transcription kit (Qiagen) starting with 1 µg of RNA. Relative quantification of DNMT1 and DNMT3ab, using the QuantiTect SYBR green kit (BioRad), was assayed on the CFX96 real-time system (BioRad). Each reaction contained 5 uL SYBR mix, 1 µL of each forward and reverse primer (0.3 μM), 1 µL of 5x diluted cDNA template, and 2 µL RNase/DNase H2O. Cycling conditions were: 15 min initial activation at 95 °C followed by 40 cycles of 95 °C for 15 s, 60 °C for 30 s, and 72 °C for 30 s. To account for differences in amplification efficiencies between different cDNA, standard curves were constructed for each target transcript using serial dilutions of pooled cDNA. To account for differences in cDNA production and loading, all samples were normalized to the abundance of the house-keeping gene elongation factor-1a, which did not change over the experimental treatments and is a common housekeeping normalization transcript for mRNA. Relative abundance data was calculated using the 2ΔΔ−CT method56, as all standard curves had an efficiency of 100%. Both RNase/DNase-free H2O and non-reverse transcribed RNA were assayed on each plate to ensure that no contaminating DNA was present in the reagents used. Further, melt-curve analysis was performed on all samples to ensure only one product was formed.3′UTRs of DNMT1 and DNMT3a were then run through mirbase.org. Candidates were chosen and scored based on greatest base pair matching and the greatest number of binding sites. Candidates were also chosen using targetscanfish.com, which analyzes the DNMT1 and DNMT3a 3′UTR in related zebrafish transcripts to determine miRNA regulatory candidates. DNMT1 candidates were omy-let-7e, omy-miR-18C, omy-miR-219a, and omy-miR-458, while the ideal candidates for DNMT3a were omy-miR-29a and omy-miR-202. Additionally, small nucleolar RNA U6 (snoU6) sequence was extracted from the trout genome database to serve as an endogenous control gene, to normalize miRNA quantification data, as this has been identified as a suitable reference RNA target for normalization of miRNA using quantitative RT-PCRin silico predicted miRNA was performed using the Qiagen miScript SYBR green kit following the manufactures instructions to a final volume of 25 µl per reaction. The reaction conditions were as follows: 15 min initial incubation at 95 °C followed by 40 cycles of 95 °C for 15 s, 55 °C for 30 s, and 72 °C for 30 s. This was followed by a melt-curve analysis to ensure only one product was formed. Standard curves were formed for all targeted miRNA, and all validation steps were carried out as above to ensure proper efficiency and no contamination was detected in each sample. Relative abundance was calculated using the 2ΔΔ−CT method56. For relative abundance calculations, the house-keeping small RNA U6 was used as this is common for normalization of miRNA in quantitative RT-PCR, which did not change over the experimental treatments.For first-strand synthesis of miRNA, the miScript II RT kit (Qiagen) was used, with a starting amount of 250 ng RNA for each sample. Relative quantification of all Dnmt3a 3′UTRs from fish, BLASTN searches were performed against the Euteleostomi subset of the NCBI database with zebrafish Dnmt6 3′UTR as the query. Homologous sequences from fish were selected from the BLAST hits, all with E-value < 0.01, and verified as 3′UTRs from Dnmt3a orthologs. Sequences included Dnmt3a 3′UTRs from the following species and genome identifiers: Common carp genome (GCA_000951615.1); Danio rerio: danRer7; Salmon genome: ICSASG_v2v (GCF_000233375.1); Haplochromis burtoni: AstBur1.0 (GCF_000239415.1); Nothobranchius furzeri: GRZ genome assembly (GCF_001465895.1); O. mykiss genomic scaffold, scaffold_64; Homo sapiens: Hg19; Mus musculus: mm10; Gallus gallus: galGal4; Danio rerio: danRer7 (for UCSC MAF). These sequences were subsequently aligned using MUSCLE version 3.557 with default parameters.To construct an alignment of Dnmt3a 3′UTR alignment was then merged with orthologous sequences from additional non-fish vertebrate species including mammals. Sections of the Multiz-100way alignment were taken from UCSC table browser (hg19) and converted using ‘MAF to FASTA’ from Galaxy58; human, mouse, chicken and zebrafish sequences were extracted for alignment. The zebrafish sequence was used to align the fish 3′UTR MSA to the subset Multiz alignment via Seaview, with sequence conservation and logos generated using Jalview (coloured with the ‘Zappo’ colouring scheme at an 85% identity threshold). PhyloP scores from the UCSC vertebrate Multiz 100-way alignment were also retrieved and mapped onto the alignment. TargetScanFish v. 6.2 and TargetScanHuman v. 7.1 were then used to predict microRNA binding sites in zebrafish Dnmt6 (Dnmt3ab) and human Dnmt3a, respectively. Additional exact matches to mir29 6-mers (GGTGCT) were also identified in the human 3′UTR sequence regardless of their position , and evolutionarily conserved binding sites identified by visual inspection. Sequence logos were generated with Jalview version 2 .Using the Multiz-100 way alignment from the UCSC browser, the fish p < 0.05. All data passed test of variance and normality, and the analysis was performed on the raw data.All data are presented as a mean ± SEM. Graphical and statistical analysis was carried out using Prism 6 (GraphPad Software). Statistical differences were determined between all treatments within a given time period using a one-way ANOVA followed by a Tukey’s multiple comparison test, as the goal was to examine the acute dose response and chronic dose response, but not the difference between time. Hence, we did not use 2-way ANOVA. However for simplicity, we combined the 24 h and 14 d data on the same graphs. Correlation analysis for the inverse relationship between relative abundance of miRNA and DNMTs was performed using a Pearson’s correlation. The level of significance for all tests was set a The datasets generated during and/or analyzed during the current study are available from the corresponding author on request.


To compare the more complex technique, functional principal component analysis (FPCA), to simpler methods of estimating values of sparse and irregularly spaced continuous variables at given time points in longitudinal data using a diabetic patient cohort from UK primary care.The setting for this study is the Clinical Practice Research Datalink (CPRD), a UK general practice research database. For 16,034 diabetic patients identified in CPRD, with at least 2 measures in a 30‐month period, HbA1c was estimated after temporarily omitting (i) the final and (ii) middle known values using linear interpolation, simple linear regression, arithmetic mean, random effects, and FPCA. Performance of each method was assessed using mean prediction error. The influence on predictive accuracy of (1) more homogeneous populations and (2) number and range of known HbA1c values was explored.When estimating the last observation, the predictive accuracy of FPCA was highest with over half of predicted values within 0.4 units, equivalent to laboratory measurement error. Predictive accuracy improved when estimating the middle observation with almost 60% predicted values within 0.4 units for FPCA. These results were marginally better than that achieved by simpler approaches, such as last‐occurrence‐carried‐forward linear interpolation. This pattern persisted with more homogeneous populations as well as when variability in HbA1c measures coupled with frequency of data points were considered.When estimating change from baseline to prespecified time points in electronic medical records data, a marginal benefit to using the more complex modelling approach of FPCA exists over more traditional methods. In clinical trials, patients are invited for assessment at prespecified intervals. In reality, patients visit their doctor at any time. It thus becomes impossible to measure change in glycosylated haemoglobin (HbA1c) at any given week unless the patient has visited their doctor then. Yet if we want to use observational research to assess effectiveness of oral hypoglycaemics (OHG), we should compare changes in HbA1c over an agreed time interval. Options for estimating values of continuous variables at given time points exist such as selecting the closest temporal measurement as a surrogate,It is unclear whether more complex techniques perform better when dealing with sparse and irregularly spaced data. Hypothetically, simpler models may work well when variability of measurements is limited, but more complex models may work better in certain situations. The study's aim was to compare FPCA methodology to other simpler methods in a cohort of diabetic patients from primary care. Specific objectives included (1) removing known HbA1c observations and calculating prediction error by comparing estimated to known values at specific time points in the whole cohort as well as in treatment, gender, and age strata and (2) exploring whether methods perform differently in certain circumstances, such as when there are changes in number of medications, length of time between consecutive measurements, and data sparseness levels within stable and unstable disease groupings.Measuring outcomes in observational studies at prespecified time intervals is difficult when data are sparse and irregularly spaced.Traditional methods of estimation include techniques, such as last‐observation‐carried‐forward linear interpolation, which allow crude estimation of change in continuous variables at specified time points .More complex techniques exist, such as FPCA, to model sparse longitudinal data.In patients with diabetes, this study demonstrates that in the setting of sparse and irregularly spaced data, using the more complex method, FPCA, has a marginal benefit.22.1The setting was the Clinical Practice Research Datalink (CPRD), a UK database of anonymised primary care EHRs covering an active population of over 8 million people.Adult patients with type 2 diabetes defined by Read codes (code list available from the authors) or who were prescribed OHG medication between 1987 and July 2011 were identified from CPRD. Practices were excluded if their last collection date preceded the study end date or the practice did not meet minimum data quality standards, as assessed by CPRD, throughout the study period. We restricted analysis to new users of OHG medication in the period 1 July 2007 to 31 December 2008 (defined as first ever use of an OHG) in order to generate a more homogeneous cohort. Patients were then required to have at least 2 HbA1c measurements in the study period 1 January 2009 to 30 June 2011  and (2) squared prediction error. This was developed to allow estimation of prediction error at times when outcomes for some patients may not have been measured and could not contribute to the estimation. The procedure of removing a random 25% of final data points was done 6 times to reflect the variability that would occur depending on which data points were sampled. Results across the 6 data sets were pooled with mean and SD values for each measure of predictive accuracy calculated. Coefficients of variation (CV), defined as the ratio of SD to the mean, were also generated as a measure of precision between the 6 replicated data sets.A primary analysis used all available HbA1c observations within the study period after temporarily excluding the final data point for 1‐in‐4 randomly selected patients Figure a. The teLinear interpolation methods were used in the primary analysis when estimating the final observation for each individual, as described by Genolini,As an alternative to linear interpolation methods, the arithmetic mean (AM) method was used, which involved simply calculating the average of the nonmissing HbA1c measures for that individual Figure e.Another computationally simple method considered was fitting a regression line in an SLR using each individual's set of nonmissing HbA1c measures as the outcome variable and time as the predictor variable where each individual's missing last value was subsequently estimated from this fitted line Figure f.One other estimation approach of temporarily excluded values was the use of RE modelling using random intercepts and constant slope. Individuals once again were not assumed to be measured at the same number of time points, but rather at different time points. This model estimates the individual's values across time on the basis of whatever data that individual has, enhanced by the time trend that is estimated for the sample as a whole but with the added bonus of taking into account the effects of covariates, age and gender, in the model.A final approach was to use FPCA methodology in developing patient‐specific estimated trajectories, using all data from the whole population, which would then allow estimation of a continuous variable, such as HbA1c, for each individual, not only at the last data point but also at any time point of interest throughout the study period Figure g. It is All interpolation methods, including AM and SLR, estimated temporarily excluded values using just that individual's set of data, whereas with the model‐based approaches of RE and FPCA, it was necessary to use data available on all subjects in the study cohort when making estimations at specific time points for particular individuals.A secondary analysis sought to examine whether prediction errors improved for any estimation method by removing the middle data point for the same 6 sets of 1‐in‐4 randomly selected patients Figure b. This aPredictive accuracy of each method was assessed using mean absolute prediction error (MAPE), defined as the average absolute prediction error across the quarter of patients where either their last data points in the primary analysis or their middle data points in the secondary analysis were omitted from the estimation. The distribution of squared prediction errors was examined and mean squared absolute prediction error (MSAPE) was used in method comparisons.To appreciate the importance of the difference between estimated and true HbA1c, we calculated the proportion of individual absolute prediction errors that were (i) below measurement error and (ii) below a clinically meaningful difference. HbA1c measurement error is considered to be around 0.4% assuming an average HbA1c value of 8%.The lowest values for MAPE and MSAPE and the highest proportion of absolute prediction errors (i) within a clinically meaningful difference and (ii) within measurement error, indicated the optimal estimation method.2.3Stratum Ametformin‐only new usersStratum Bfemale new usersStratum Cfemale metformin‐only new usersStratum Dfemale metformin‐only new users aged (i) ≤58, (ii) 59–70, or (iii) ≥71 yearsStratum Enew users with medication changes (i) single and (ii) ≥2Stratum Fnew users whose distance in time between missing HbA1c measure and its closest neighbour (i) >50th percentile (ii) >75th percentile and (iii) >90th percentileStratum Gnew users with 2–3, 4–5, 6–8, or >8 HbA1c measures in the study window and for whom their range of recorded values is <2 or ≥2 HbA1c units We anticipated that prediction error would be affected by many factors, such as medication, gender, age, switching medication, distance in time between missing HbA1c measure and its nearest neighbour, and density of HbA1c measures coupled with disease stability. Therefore, after first applying all estimation methods to the whole new user cohort, we performed separate analyses based on the following subgroups:These sensitivity analyses were done only for a single replicated data set. With each stratum, we created a homogeneous population and as such we expected modelling methods to perform better than other approaches that do not take into account population behaviour. The exception to this is likely to be stratum G, where we restricted the population to patients with highly variable HbA1c and as such the expectation was that all approaches may underperform.http://www.stata.com) and R V.3.1.3 (http://www.R-project.org).The analysis was conducted using Stata V.12.1 . The median period between measurements was 163 days .Table The best performance was achieved by FPCA where a mean of 54% of subjects had prediction errors less than measurement error compared to a mean of 29% with bisector linear interpolation, the least performing method. FPCA was only marginally better than last‐occurrence‐carried‐forward (LOCF), RE, and AM approaches  and erratic data (large HbA1c range), more flexible modelling methods do not convincingly produce better results. In fact, by looking at levels of prediction error, when we get into sparse and erratic data, we cannot trust any method in that all methods do quite badly. Evidence of this can be seen in the subgroup of new users with 2–3 measures and unstable disease where the proportion of subjects with prediction errors less than the clinical important difference of 0.5 is at best 20% with LOCF.This study exhibits a couple of important strengths. Approximately 16,000 subjects in the original population provide for a very large study cohort, facilitating the estimation of temporarily excluded HbA1c values using a whole range of techniques, including linear interpolation approaches as well as more complex modelling approaches. Also, knowing what the true measurements are for the ones we are trying to predict allows us to effectively calibrate results.There are, of course, some limitations. First, we have 1 very particular continuous outcome variable in HbA1c and 1 very particular population in diabetic patients. So our findings may not be generalisable to different populations where there may be different variability through time and different associations with other variables. Second, HbA1c has its own within‐person variability. So it may very well be that for other outcomes, such as weight, which varies more slowly, these estimation approaches may perform differently. Third, it is very likely that HbA1c measurements in this population are missing not at random and unfortunately there is no method of testing available to address this problem. Although we are predicting observed values from observed values, it is possible that missing data would have enabled the more complex modelling methods to work better. Fourth, LOCF, NOCB, and AM all assume the outcome does not change with time, whereas RE and SLR both assume it changes linearly, and FPCA allows for nonlinear changes. When deciding therefore which estimation method is most appropriate, consideration should be given to whether the data violate any underlying assumptions. Fifth, these methods do not account for uncertainty associated with missing values and so using them will produce estimates that are too precise and will lead to potential bias in subsequent analyses. Finally, underrepeated sampling, using the whole cohort only, was justified since the vast majority of CVs were below 3%. However, it is an assumption that as a consequence there would be no change in the order of each method's performance for strata A‐G.In summary, we have shown that there is a marginal benefit to using the more complex FPCA model when estimating missing HbA1c values in a cohort of patients with diabetes, as results for this model are generally better than all other approaches. However, FPCA is a significantly more complex technique to implement, although worth considering due to the problems attached to using more simple approaches. Caution is needed in extrapolating these findings to other settings as the most appropriate method when estimating values at given time points will likely depend on the variable of interest, the population in which it is measured, where the missing data actually exist, how homogeneous the population is to start with, and the behaviour of patients and clinicians.This study was supported by the Canadian Institutes of Health Research (CIHR) operating grant no. 201009MOP, the Medical Research Council (MRC) Clinician Scientist Fellowship grant no. G0902272, and MRC grant no. MR/K006665/1.Study approval was granted by the Independent Scientific Advisory Committee of CPRD (ref 11_154A).There are no conflicts of interest to disclose.Table S1. Mean absolute prediction errors (MAPE), proportions of subjects with absolute prediction errors (APE) within clinical acceptability and measurement error, and mean squared absolute prediction errors (MSAPE), for each of eight strata. Reporting on subjects whose last HbA1c value excluded from analysis.Figure S1a. Mean absolute prediction errors for strata (A) new users on metformin only medication, (B) female new users, (C) female metformin‐only new users, (D) female metformin‐only new users aged a) ≤ 58, b) 59–70, c) ≥ 71 years, (E) new users with a) single medication change and b) two or more changes in medication, (F) new users whose distance in time between missing HbA1c measure and its closest neighbour was a) > 50th percentile b) > 75th percentile and c) > 90th percentile, where prediction error is absolute difference between predicted and actual values of last HbA1c observation and n is number of subjects being reported on, whose last value was excluded.Figure S1b. Proportion (%) with absolute prediction errors < 0.5  for strata (A) new users on metformin only medication, (B) female new users, (C) female metformin‐only new users, (D) female metformin‐only new users aged a) ≤ 58, b) 59–70, c) ≥ 71 years, (E) new users with a) single medication change and b) two or more changes in medication, (F) new users whose distance in time between missing HbA1c measure and its closest neighbour was a) > 50th percentile b) > 75th percentile and c) > 90th percentile, where prediction error is absolute difference between predicted and actual values of last HbA1c observation and n is number of subjects being reported on, whose last value was excluded.Figure S1c. Proportion (%) with absolute prediction errors < 0.4 (measurement error) for strata (A) new users on metformin only medication, (B) female new users, (C) female metformin‐only new users, (D) female metformin‐only new users aged a) ≤ 58, b) 59–70, c) ≥ 71 years, (E) new users with a) single medication change and b) two or more changes in medication, (F) new users whose distance in time between missing HbA1c measure and its closest neighbour was a) > 50th percentile b) > 75th percentile and c) > 90th percentile, where prediction error is absolute difference between predicted and actual values of last HbA1c observation and n is number of subjects being reported on, whose last value was excluded.Figure S1d. Mean squared absolute prediction errors for strata (A) new users on metformin only medication, (B) female new users, (C) female metformin‐only new users, (D) female metformin‐only new users aged a) ≤ 58, b) 59–70, c) ≥ 71 years, (E) new users with a) single medication change and b) two or more changes in medication, (F) new users whose distance in time between missing HbA1c measure and its closest neighbour was a) > 50th percentile b) > 75th percentile and c) > 90th percentile, where prediction error is absolute difference between predicted and actual values of last HbA1c observation and n is number of subjects being reported on, whose last value was excluded.Figure S2a. Mean absolute prediction errors for subgroups (from stratum G) of new users with 2–3, 4–5, 6–8 or >8 HbA1c measures for both stable (< 2 HbA1c units) and unstable disease (≥ 2 HbA1c units), where prediction error is absolute difference between predicted and actual values of last HbA1c observation and n is number of subjects being reported on, whose last value was excluded.Figure S2b. Proportion (%) with absolute prediction errors < 0.5  for subgroups (from stratum G) of new users with 2–3, 4–5, 6–8 or >8 HbA1c measures for both stable (< 2 HbA1c units) and unstable disease (≥ 2 HbA1c units), where prediction error is absolute difference between predicted and actual values of last HbA1c observation and n is number of subjects being reported on, whose last value was excluded.Figure S2c. Proportion (%) with absolute prediction errors < 0.4 (measurement error) for subgroups (from stratum G) of new users with 2–3, 4–5, 6–8 or >8 HbA1c measures for both stable (< 2 HbA1c units) and unstable disease (≥ 2 HbA1c units), where prediction error is absolute difference between predicted and actual values of last HbA1c observation and n is number of subjects being reported on, whose last value was excluded.Figure S2d. Mean squared absolute prediction errors for subgroups (from stratum G) of new users with 2–3, 4–5, 6–8 or >8 HbA1c measures for both stable (< 2 HbA1c units) and unstable disease (≥ 2 HbA1c units), where prediction error is difference between absolute predicted and actual values of last HbA1c observation and n is number of subjects being reported on, whose last value was excluded.Table S2. Mean absolute prediction errors (MAPE), proportions of subjects with absolute prediction errors (APE) within clinical acceptability and measurement error, and mean squared absolute prediction errors (MSAPE), for each of eight strata. Reporting on subjects whose middle HbA1c value excluded from analysis.Figure S3a. Mean absolute prediction errors for strata (A) new users on metformin only medication, (B) female new users, (C) female metformin‐only new users, (D) female metformin‐only new users aged a) ≤ 58, b) 59–70, c) ≥ 71 years, (E) new users with a) single medication change and b) two or more changes in medication, (F) new users whose distance in time between missing HbA1c measure and its closest neighbour was a) >50th percentile b) >75th percentile and c) >90th percentile, where prediction error is absolute difference between predicted and actual values of middle HbA1c observation and n is number of subjects being reported on, whose middle value was excluded.Figure S3b. Proportion (%) with absolute prediction errors < 0.5  for strata (A) new users on metformin only medication, (B) female new users, (C) female metformin‐only new users, (D) female metformin‐only new users aged a) ≤ 58, b) 59–70, c) ≥ 71 years, (E) new users with a) single medication change and b) two or more changes in medication, (F) new users whose distance in time between missing HbA1c measure and its closest neighbour was a) > 50th percentile b) > 75th percentile and c) > 90th percentile, where prediction error is absolute difference between predicted and actual values of middle HbA1c observation and n is number of subjects being reported on, whose middle value was excluded.Figure S3c. Proportion (%) with absolute prediction errors < 0.4 (measurement error) for strata (A) new users on metformin only medication, (B) female new users, (C) female metformin‐only new users, (D) female metformin‐only new users aged a) ≤ 58, b) 59–70, c) ≥ 71 years, (E) new users with a) single medication change and b) two or more changes in medication, (F) new users whose distance in time between missing HbA1c measure and its closest neighbour was a) > 50th percentile b) > 75th percentile and c) > 90th percentile, where prediction error is absolute difference between predicted and actual values of middle HbA1c observation and n is number of subjects being reported on, whose middle value was excluded.Figure S3d. Mean squared absolute prediction errors for strata (A) new users on metformin only medication, (B) female new users, (C) female metformin‐only new users, (D) female metformin‐only new users aged a) ≤ 58, b) 59–70, c) ≥ 71 years, (E) new users with a) single medication change and b) two or more changes in medication, (F) new users whose distance in time between missing HbA1c measure and its closest neighbour was a) > 50th percentile b) > 75th percentile and c) > 90th percentile, where prediction error is absolute difference between predicted and actual values of middle HbA1c observation and n is number of subjects being reported on, with middle value excluded.Figure S4a. Mean absolute prediction errors for subgroups (from stratum G) of new users with 2–3, 4–5, 6–8 or >8 HbA1c measures for both stable (< 2 HbA1c units) and unstable disease (≥ 2 HbA1c units), where prediction error is absolute difference between predicted and actual values of middle HbA1c observation and n is number of subjects being reported on, whose middle value was excluded.Figure S4b. Proportion (%) with absolute prediction errors < 0.5  for subgroups (from stratum G) of new users with 2–3, 4–5, 6–8 or >8 HbA1c measures for both stable (< 2 HbA1c units) and unstable disease (≥ 2 HbA1c units), where prediction error is absolute difference between predicted and actual values of middle HbA1c observation and n is number of subjects being reported on, whose middle value was excluded.Figure S4c. Proportion (%) with absolute prediction errors < 0.4 (measurement error) for subgroups (from stratum G) of new users with 2–3, 4–5, 6–8 or >8 HbA1c measures for both stable (< 2 HbA1c units) and unstable disease (≥ 2 HbA1c units), where prediction error is absolute difference between predicted and actual values of middle HbA1c observation and n is number of subjects being reported on, whose middle value was excluded.Figure S4d. Mean squared absolute prediction errors for subgroups (from stratum G) of new users with 2–3, 4–5, 6–8 or >8 HbA1c measures for both stable (< 2 HbA1c units) and unstable disease (≥ 2 HbA1c units), where prediction error is absolute difference between predicted and actual values of middle HbA1c observation and n is number of subjects being reported on, whose middle value was excluded.Click here for additional data file.