The robustness of this assay required a multicenter validation. The flow cytometry assay designed to quantify peripheral blood monocyte subsets was implemented by multiple diagnosis laboratories in France. A nationwide survey was performed to evaluate its performance. All the 48 French laboratories answered the questionnaire, revealing that 63% use this assay routinely. Central blind reanalysis of 329 cytometry files collected from five laboratories demonstrated an excellent correlation in classical monocyte fraction measurement . The cutoff value of 94% classical monocytes being the critical readout for diagnosis, we then compared 115 patients with classical monocytes ≥ 94% and 214 patients with a fraction < 94% between initial analysis and reanalysis. An agreement was obtained in 311 files. Finally, an overt diagnosis, available for 86 files, confirmed a good sensitivity (93.6%) and specificity (89.7%). This survey demonstrates the robustness of the flow assay with limited variability of classical monocyte percentage between centers, validates the 94% cutoff value, and confirms its sensitivity and specificity.Peripheral blood monocytes include three subsets defined by CD14 and CD16 surface markers. An increase in the CD14 Such a monocytosis can also be seen in various diseases, including chronic or acute infections, chronic inflammatory processes, and hematopoietic malignancies, more specifically myeloproliferative neoplasms4. CMML is characterized by a clonal hematopoiesis with abnormal myeloid differentiation, which is either dysplastic, leading to cytopenias, or exacerbated, leading to myeloproliferation, or both6. Dysplasia of one or more myeloid lineages might be observed, yet is not mandatory. If myelodysplasia is absent or minimal, an acquired clonal cytogenetic or molecular genetic abnormality should be detected for diagnosis statement2. Indeed, most CMMLs have somatic mutations, especially of TET2, SRSF2, ASXL1 genes, and genes of the Ras pathway, although none of them are specific to the disease7. For all these reasons, diagnosis of CMML has hitherto remained difficult.The updated World Health Organization (WHO) classification for chronic myelomonocytic leukemia (CMML) diagnosis requires both the presence of persistent peripheral monocytosis  CD14++CD16+ and nonclassical (ncMo) CD14−/lowCD16+, can be a powerful tool to diagnose CMML, regardless of mutational background, subtype, or dysplastic versus proliferative features. We showed that a relative accumulation of cMo ≥ 94% of total peripheral blood monocytes distinguishes CMML from any type of reactive monocytosis with high specificity (94.1%) and sensitivity (91.9%)8. This 94% threshold was subsequently validated in two other independent studies10, the latter highlighting the efficacy of this test to distinguish CMML from myeloproliferative neoplasms with associated monocytosis10. Moreover, the sensitivity of cMo accumulation was recently demonstrated to increase with the subtype, reaching 100% for CMML type 211.We found that accumulation of classical monocytes (cMo) CD1413. Since this assay has been largely adopted by diagnosis laboratories in France, we sought to assess its use throughout a nationwide survey and its performance through a multicenter evaluation.The flow cytometry-based monocyte subset analysis, hereafter referred to as “monocyte assay”, is now proposed as an additional diagnostic modality in CMMLA short questionnaire was sent by personalized emails between July and September 2016 to 48 French laboratories  performing routine flow cytometry diagnosis of myeloid neoplasms. Surveyed flow cytometrists were first asked whether they had implemented the “monocyte assay” in their daily routine practice. If not, they were queried if they would be willing to set it up within a short time, later, or most likely never. If they were using the “monocyte assay”, they were requested to provide several information, including the date and conditions in which the “monocyte assay” had been implemented, the antibody panel used, and the number of tests performed every month. Eventually, surveyed cytometrists were asked if they considered the “monocyte assay” useful for CMML diagnosis.++CD16−) percentage evaluation in order to perform a centralized reanalysis. Briefly, whole-blood samples were stained with the following antibodies  as previously described8 and analyzed with a Navios Cytometer (Beckman-Coulter): CD14-PE or CD14-ECD or CD14-PC5.5 or CD14-APC or CD14-AA750 (clone RMO52); CD16-ECD or CD16-AA750 or CD16-PB (clone 3G8); CD2-FITC or CD2-PE or CD2-PC7 or CD2-AA700 (clone 39C1.5); CD56-PC5.5 or CD56-APC (clone N901); CD24-PE or CD24-PC5.5 (clone ALB9); CD45-KO (clone J33) for four centers; one center used CD7-AA700 (clone 8H8.1) instead of CD2 and CD33-PC5.5 (clone D3HL60.251) instead of CD24.Five centers selected among those commonly using the assay were asked to send their raw data files as well as the corresponding cMo (CD14® Software (Beckman-Coulter®) by two different operators, a skilled one (OWB) and a trainee one (ST) through a protocol adapted to each center and/or antibody panel used. Briefly, monocytes were roughly selected as CD45high/SSCint cells among living cells and singlets , NK cells as CD56+/SSClow/int cells (which overlap with ncMo subset on CD14/CD16 dot plot), B cells as CD24+/SSClow cells, both immature and mature granulocytes as CD24+/SSCint to high cells (or immature cells as CD33+/SSCint to high cells), residual granulocytes expressing high levels of CD16, and the remaining CD14−CD16− cells corresponding mainly to basophils and to NK cells which would have not previously been excluded , CD14++/CD16+ , and CD14−/CD16+  subsets .The percentages of cMo provided by the different centers were compared with cMo percentages determined by centralized analysis. Of note, the five selected centers used Navios instruments  to perform correlation tests, Bland–Altman graphs, Mann–Whitney tests, and receiver-operating characteristic (ROC) curve.This retrospective study was approved by the local Ethics Committee (IRB MONDOR).8  and keep using the monocyte assay, 27 centers spontaneously implemented it, most often following the original publication and/or a meeting presentation  use routinely the “monocyte assay”. Among the 18 centers that do not use this test, eight (44%) intend to develop it soon, the ten remaining centers not excluding a possible implementation. Apart from the three centers that participated in the original study8 . Ten centers use at least one exclusion marker, while two do not use any exclusion antibody. Among the 30 centers using the “monocyte assay”, nine centers perform 1–2 tests per month, fifteen 3–6 tests, three 6–10 tests, and three more than 10 tests per month. All the surveyed cytometrists consider the “monocyte assay” useful for CMML diagnosis.Eighteen out of 27 (67%) centers use at least the published exclusion panel for non-monocyte mature blood cellsIn the second step, we sought to validate the routine use of the “monocyte assay” in centers that did not participate in the original study and had adapted the published technique. Having excluded the three centers which were part of the initial study, we selected five laboratories according to their antibody panel and their frequency of use, highlighting their keen interest in this test. Thus, we collected 329 useable flow cytometric raw data files provided by these centers, anonymized A to E. For each analyzed file, we collected the percentage of cMo provided by the center as well as the number of cMo events.15, which would therefore be easily eliminated. Thus, the three monocyte subsets  were identified in total blood samples following the exclusion gating strategy originally published8, so as to eliminate the other mature circulating cells. All the five selected centers but one use the published exclusion antibody panel shown in Fig. All received files were analyzed in a blind fashion by two different operators (skilled and trainee). The only 20 files showing discordant cMo percentages between these two analyses were reassessed in order to obtain a harmonized value. Given that no marker is common to the three sub-populations, positive gating of monocytes should be avoided. Indeed, markers classically used to select monocytes, such as CD14, CD64, CD33, or CD36, are expressed at a lower level by the ncMo subpopulationp < 0.0001; Fig. p < 0.0001; Fig. We compared the percentages of cMo arising from the agreement of the two blind analyses for the 329 FCM raw data files with those provided by the five centers. This comparison showed an excellent global significant correlation  were in agreement , Fig. 8), 201 (93.5%) displayed a percentage of cMo < 94% given by the center  and agreement for the 245 remaining files .As the threshold value of 94% cMo is the most relevant parameter in the “monocyte assay”, we compared patients for which the cMo percentage was ≥ 94% ter Fig. . Only onter Fig. , whereaster Fig. . In mostter Fig. . Hence, ter Fig. , thereby2, 23 reactive monocytosis, and 16 non-CMML malignancies. Among the 47 CMML patients, 32 (68%) were newly-diagnosed CMML. The 15 patients who had a pre-existing diagnosis received supportive care  or cytoreductive drugs, but none of them were treated with a hypomethylating agent.Among the 245 files displaying more than 10,000 cMo events, we collected clinical data when available . White blood cell (WBC) counts were variable with a mean of 18.9 ± 26.8 × 109/L (up to 161 × 109/L), and the absolute monocyte count was 3.9 ± 3.2 × 109/L with a monocyte percentage of 26.0 ± 10.4%. These patients displayed both mild anemia (mean hemoglobin: 11.5 ± 2.1 g/dL) and thrombocytopenia (mean platelets: 156 ± 115 × 109/L). Patients with reactive monocytosis were younger than CMML patients (63 ± 23 years), with also a majority of males (sex ratio = 2.8). The mean WBC count was higher (27.6 ± 47.2 × 109/L) related to the increase of neutrophils in these reactive contexts, and the mean absolute monocyte count was 4.3 ± 10.3 × 109/L. Patients with non-CMML malignancies had age close to that of CMML patients (76 ± 11 years), and presented with a mean WBC count of 20.9 ± 18.7 × 109/L with a mean absolute monocyte count of 2.5 ± 2 × 109/L  subset combined with the increase of iMo (CD14++CD16+) subset  and obtained yet again a 94% cutoff value of cMo with an area under the ROC curve (AUC) of 0.921 Fig. .2. Even though recurrent cytogenetic aberrations are found in 30–40% of CMML patients17, detection of such a clonal abnormality can help confirm diagnosis, especially in the absence of significant dysplasia2. About 90% of CMML patients have somatic mutations including TET2, SRSF2, and ASXL1 mutations20 with an average of 14 per patient21, yet none of them are specific to the disease. Additional mutations in genes of the Ras pathway are frequently detected in the proliferative form of the disease. Since some of these mutations can be detected in otherwise healthy older patients23, detection of such mutations may be not sufficient to confirm a neoplastic origin of the monocytosis;24 hence, there is a need to develop a diagnostic tool available as of peripheral blood examination. We showed 3 years ago that the “monocyte assay” was very effective in distinguishing CMML from reactive monocytosis. As an increasing number of laboratories regularly request our advice for raw data file interpretation, we thought of carrying out a survey focused on the practices of this test in France. This study reveals a strong adhesion of the French laboratories to the “monocyte assay” with 30 user centers. The number of tests performed in the different laboratories are heterogeneous, half of them using it three to six times a month. This low frequency reflects a targeted use of the “monocyte assay” in a context of CMML suspicion, and not a systematic use of this test for monocytosis management. Indeed, this mutiparameter flow cytometry assay may hardly be performed for each peripheral blood sample displaying an absolute monocyte count ≥ 1 × 109/L. In this regard, we proposed a new application of the Hematoflow™ solution25  which provides white blood cell (WBC) differentials by flow cytometry26 and allows the quantification of the CD16-negative monocyte fraction at the very same time. Hence, it provides a useful approximation of the cMo percentage and may easily detect samples that are suspected of being CMML and thus require further exploration by the “monocyte assay”.Peripheral monocytosis is a very common finding in routine laboratories. Since CMML should be suspected based on this sole arbitrary criterion, bone marrow examination is required for dysplasia assessment, blast quantification, and cytogenetic evaluation if no reactive causes can explain the sustained elevated absolute monocyte count11. By chance, the five selected centers all used Navios instruments (Beckman-Coulter).This original study published in 2015 included a multicenter validation of this test carried out by three laboratories with a standardized protocol. We sought here to validate the use of the “monocyte assay” by independent centers which were not part of the initial study and without prior standardization. The only criterion for center selection was the total number of tests performed and the antibody panel exclusion design, which should contain markers for both NK cells and immature granulocytes as these latter populations may overlap with ncMo and lead to a cMo percentage underestimation in the case of flawed exclusion. Thus, laboratories that were in training or that had just implemented the “monocyte assay” were not included. The type of flow cytometer was not a selection criterion since both Becton-Dickinson (LSR II) and Beckman-Coulter (Navios) instruments were used in our previous studiesWe received 329 files flow cytometry raw data with the corresponding cMo percentages and decided to duplicate their blind centralized analysis by a trainee operator and a skilled one in order to evaluate the easiness of the gating strategy once it has been set up. Both analyses were extremely correlated with very few discordant files. The cMo percentages arising from this agreement showed a very good global correlation with the cMo percentages provided by centers. Furthermore, the absence of major bias was a key input of this study, as the “monocyte assay” interpretation is related to a cutoff value.Hence, we decided to focus on the agreement of the cMo percentages below or above the 94% threshold. Only 18 out of the 329 files were discordant. Most of them were due to an insufficient number of cMo events acquired. Indeed, in this case, the separation of the cMo and the iMo populations may be tricky. We highlight here the usefulness of the density plot representation for the “cMo gate” delineation. Further exchange with the center E that provided the highest number of files with cMo events below 10,000 unveiled that only 50 µl of total blood had been used instead of 200 µl as recommended. These results highlight the importance of the pre-analytical conditions for the “monocyte assay” implementation.11. Concurrent cases of autoimmune diseases and/or systemic inflammatory syndromes have been reported in 20% of CMML patients27. In such cases, an increase of the iMo subset can lead to an underestimation of the relative cMo percentage, which drops below the 94% threshold11. Considering the three false-negative cases as genuine CMML, we obtained a “corrected” sensitivity of 100%. A specificity of 89.7% was calculated with five false positives  consisting of four reactive monocytosis and one MDS. Interestingly, a relative accumulation of cMo ≥ 94%, i.e., “a CMML-like signature” was described in roughly one-third of myelodysplastic syndromes at diagnosis11, a fraction of them being able to evolve into a genuine CMML11. These data highlighted the significance of the monocyte component in MDS reported by different studies. MDS with a relative monocytosis28, i.e., monocytes accounting for 10% of peripheral leukocytosis yet below 1 × 109/L or MDS with marrow monocytosis29, display a similar clinicopathologic and mutational profile to classical CMML30. Further investigations are needed to characterize these early stages of CMML which do not currently fulfill the WHO criterion of monocytosis ≥ 1 × 109/L.An accumulation of cMo ≥ 94% was observed in 44 of the 47 CMML, indicating a sensitivity of 93.6% according to previous results. The three false negatives showed a specific abnormal profile, drawing a “bulbous aspect” on the CD14/CD16 dot plot, related to an increase of the iMo subset combined to the disappearance of the ncMo population, which has been recently described in CMML patients with an inflammatory state8. The AUROC test performed using these new data led yet again to a 94% cutoff value of cMo, highlighting the robustness of this threshold.Eventually, this new independent cohort of patients allowed us to challenge the previous 94% threshold established in 201531, pending the demonstration of its clinical benefit that requires a prospective, international multicenter study.The simplicity of the “monocyte assay”, performed on whole-blood sample with a limited number of antibodies, the antibody exclusion panel not being imposed, and the ease of interpretation confirmed by the centralized review have favored the implementation of this phenotypic test in 30 laboratories in France. Our study confirms the successful multicenter use of this flow cytometry test, provided a minimum of 10,000 events were analyzed. This analysis demonstrates a low variability of cMo percentage quantification and validates the previously proposed 94% cutoff value, defining a robust, sensitive, and specific assay. Compared with genetic analyses, the simplicity of this flow cytometry approach may be likely increasingly widespread


Cohesin and CTCF are master regulators of genome topology. How these ubiquitous proteins contribute to cell-type specific genome structure is poorly understood. Here, we explore quantitative aspects of topologically associated domains (TAD) between pluripotent embryonic stem cells (ESC) and lineage-committed cells. ESCs exhibit permissive topological configurations which manifest themselves as increased inter- TAD interactions, weaker intra-TAD interactions, and a unique intra-TAD connectivity whereby one border makes pervasive interactions throughout the domain. Such ‘stripe’ domains are associated with both poised and active chromatin landscapes and transcription is not a key determinant of their structure. By tracking the developmental dynamics of stripe domains, we show that stripe formation is linked to the functional state of the cell through cohesin loading at lineage-specific enhancers and developmental control of CTCF binding site occupancy. We propose that the unique topological configuration of stripe domains represents a permissive landscape facilitating both productive and opportunistic gene regulation and is important for cellular identity. Eukaryotic genomes fold into topologically associated domains (TAD). Here the authors characterise a TAD regulatory architecture underlying lineage-specific gene regulation, finding that stripe TADs are associated with poised and active chromatin landscapes and linked to the cells functional state. This is achieved through the spatial clustering of regulatory elements with appropriate target genes and by creating permissive topologies within which lineage-appropriate regulators can emerge and function2. Hi-C studies across many cell types have shown that TADs are similarly positioned in pluripotent and differentiated cells4, notwithstanding the dramatic epigenome reorganisation and transcriptional changes which accompany these transitions in cellular potential. Building upon these observations, recent studies have uncovered quantitative differences in genome topology during development and reprogramming7. Changes in the extent of TAD insulation, as well as TAD connectivity have been associated with gene expression changes accompanying developmental10 or disease progressions13.Mammalian genomes are folded into structures known as topologically associating domains (TADs) that facilitate the regulation of gene expression16, it is well established that CTCF and cohesin are master regulators of TAD structure. These proteins are key components of genome topology at multiple scales, and thus have central roles in the connectivity and insulation of TADs21. How these ubiquitously expressed structural proteins mediate genome topologies that reflect cellular potential is poorly understood. In line with their importance in spatial genome organisation, polymer models of TAD formation incorporating cohesin-mediated loop extrusion and CTCF boundary elements gives rise to TAD structures that are very similar to experimental Hi-C datasets23. While these models do consider the occupancy of a CTCF-binding site as a parameter, it remains unclear how ChIP signals relate to binding site occupancy and how differentially occupied binding sites contribute to cell-type-specific topologies.Whilst other factors can play important roles in spatial organisation24. Here we track the developmental dynamics of stripe domains to show that apart from being merely structural constructs, stripe domain formation is linked to the functional state of the cell through cohesin loading at lineage-appropriate enhancer elements and developmental control of CTCF-binding site occupancy. Importantly, our results indicate that transcription is not a key determinant of stripe conformation, since stripe domains can be associated with poised enhancers and low levels of gene expression. We find that enhancer activation during lineage commitment contributes to stripe conformation and that lineage-dependant regulation of CTCF-binding site occupancy converts stripe domains into loop domains. Overall our results indicate that cell-type-specific control of genome topology may be achieved by coupling cohesin loading to lineage-appropriate enhancers and by regulating the occupancy of CTCF at specific genomic locations. Since both active and poised enhancers are associated with stripe domains, we propose that the unique topological configuration of these TADs make them well suited to a permissive structural environment within which lineage-appropriate gene regulation may readily emerge.To explore how genome topology relates to cellular potential, here we explore quantitative aspects of TAD structure between pluripotent embryonic stem cells (ESC) and lineage-committed cells. Our results reveal chromatin interactions in ESCs which become increasingly restricted with lineage commitment, in line with the known chromatin plasticity of ESCs. This permissivity manifests itself as increased interactions between TADs, weaker interactions within TADs and a class of TAD architecture with a unique connectivity whereby an individual element at one border makes pervasive interactions throughout the domain. Interestingly, such TAD structure is not unique to ESCs and is also readily observed in lineage-restricted cell types. A similar TAD architecture, so-called stripe domains has recently been described25 from biological replicates of mouse ESC grown in 2i conditions and compared these to mouse neural stem cell (NSC) Hi-C datasets17. Sequencing of the ESC libraries produced 1.6 billion paired-end reads, with 319 million total contact pairs retained after filtering  for normalisation of the Hi-C datasets and calculation of an interaction score for each contact in the observed contact matrix (see the section “Methods” for further information). Analysis of contact insulation supported previous findings6 that many TAD borders were similarly positioned between pluripotent and lineage-committed cells  . These differences were not a function of a difference in the size of TADs, nor a difference in the overall distribution of interaction scores between the two cell types  within each TAD (with a minimum size of 200 kb) were grouped into one of four defined TAD sectors corresponding to whether they were observed between TAD borders (border interaction), in the ‘leading’ or ‘trailing’ edge of the TAD, or in the inner-TAD region  showed the distinctive TAD topology of a self-interacting region flanked by contact insulation defining the borders  or 3′-stripe (n = 256) domains based on the location of the anchored border , cell cycle distribution, sequencing depth, Hi-C library preparation, TAD segmentation methods and cellular heterogeneity , mouse NSCs, post-mitotic astrocytes (AST)cis interactions, as expected if there was a specific loop interaction between the borders. In contrast, stripe domains contained a wide range of contact interaction distances with a broad distribution and had very few interactions covering the entire domain . Taken together, the classification of TADs into distinct structural groups emphasises the heterogeneity inherent in TAD structure and has revealed a diversity of TAD border behaviour. Thus, the distinct TAD classes offer an opportunity to explore the mechanisms that contribute to the formation of these different domain structures, their biological functions and their developmental dynamics.Finally, we assessed the relationship between TAD classes and epigenetic properties using publicly available datasets (Supplementary Table Olf) cluster on chromosome 10 are not expressed in ESCs and are contained within a 5′ stripe domain  and gamma (Pcdh-γ) clusters on chromosome 18 are also contained within a stripe domain, this time anchored from the 3′ end. The annotated enhancer, ‘ccr’ is known to regulate Pcdh-β and Pcdh-γ expression, but not Pcdh-α expression29 which resides in a neighbouring TAD. Like in the Olf cluster, ccr is located at the anchored border of the stripe, is enriched for the active enhancer marks H3K4me1 and H3K27ac and in this case, makes specific interactions with the -β and -γ genes to activate their expression in NSCs , as well as the pluripotency-specific transcription factors (TF) Nanog, Oct4, and Sox2 with respect to the TAD classes in ESCs. TAD classes in the repressive compartment exhibited weaker ChIP signals compared to active compartment TAD classes   Fig. . Second,01) Fig. , as prev01) Fig. . The com01) Fig. .The differential distribution of CTCF signal and the differential bias for motif orientation suggests that TADs, guided by the CTCF landscape, have distinct potentials for structure formation. To further understand the relationship between CTCF sites and domain topologies, we calculated the intra-TAD distance between convergent CTCF pairs that could interact, stratified by TAD class. This revealed that the distances of convergent CTCF peaks in loop domains spanned a larger proportion of the TAD than in stripe domains , but were weaker in stripe domains . CTCF motif entropy could not account for these differences, since it was similar irrespective of TAD class , in line with published reportsTo investigate whether TADs switched classes during differentiation, we compared TAD classes, where at least one border was maintained between the cell types. This revealed that loop domains were more stable through differentiation than stripes, where 61.5% of NSC loop domains arose from loops in ESCs, compared to 37.5% of NSC stripe domains Fig. , grey. WAmer2 is a negative regulator of the Wnt pathway and is expressed in NSC. It is located within a stripe domain in ESCs where it is not expressed. The stripe domain is ‘lost’ in NSCs and a new insulation site is observed, which is independent of CTCF occupancy changes but associated with NSC-specific expression of Amer2  for 10 min at room temperature (RT). FA was quenched using 0.125 M glycine for 5 min at RT. Cells were washed in 10 ml of cold PBS twice, centrifuging the sample at 750 rcf at 4 °C between washes. Cell pellets were resuspended in 10 ml Hi-C lysis buffer  and incubated on ice for 25-30 min. The cell lysis was aided by 10 strokes in a glass douncer using ‘tight’ pestle and nuclei spun down at 800 rcf at 4 °C for 5 min. Nuclear pellets were resuspended in 500 µl 1.2x NEB2.1 (New England Biolabs (NEB)) with 0.3% SDS, transferred to low-retention tubes (Eppendorf Protein Lo-Bind) and incubated at 37 °C for 30 min in a thermomixer (950 rpm). The temperature was then adjusted to 60 °C and nuclei were incubated at 60 °C for 5 min. Triton X-100 was added to the nuclei to final concentration of 2% and nuclei incubated for 15 min at RT with tilting to quench SDS. 1500 U HindIII (NEB) was added to the samples to digest the chromatin in nucleo. After 4 h at 37 °C, 750 U of fresh enzyme along with 10 µl NEB10x were added to the samples and incubated overnight at 37 °C (650 rpm). In the morning, the nuclei were pelleted for 5 min at 4 °C and 800 rcf, resuspended in 500 µl 1X NEBuffer 2.1, and chromatin further digested using 1000 U HindIII for 4 h at 37 °C shaking at 650 rpm and occasionally resuspended using low-retention tips. Filling of digested ends was performed as previously described. Nuclei were washed twice in 100 µl 1X T4 DNA ligase buffer (NEB) and resuspended in 200 µl of the same buffer. Ligation was performed in three steps: (1) 6 µl of T4 DNA Ligase (NEB M0202) was added to the nuclei and incubated at 16 °C for several hours with occasional resuspending; (2) 10 µl 10X buffer and 4 µl Ligase were added and samples incubated at 16 °C overnight; (3) nuclei were pelleted, resuspended in 100 µl fresh 1X ligase buffer, 6 µl ligase added, and incubated at 16 °C for 4 h with occasional resuspending. Biotin clean-up and sonication were performed as previously described, with the exception that the sonication time was shortened to 70 s to get average fragment size ~200 bp and thereby increase the proportion of informative fragments in the libraries after size selection. Size selection was done using SPRI Size Selection Beads (Beckman Coulter). To remove fragments <150 bp, 1.1X of SPRI beads was used (120 µl of sonicated DNA with 132 µl beads). After incubation at RT for several minutes, samples were put on a magnet. Short fragments are retained in the supernatant which is removed after 5 min incubation on the magnet. Beads were washed twice in 85% EtOH, dried and libraries eluted in 100 µl nuclease-free water after 3 min on the magnet. Biotin pull-down and library preparation were performed as previously described. Libraries were prepared from 15 to 20 µg DNA in 5 µg aliquots.Mouse ESC were cultured in media supplemented with fresh Leukaemia Inhibitory factor (LIF) and the two inhibitors, PD0325901 (MEK inhibitor) and CHIR99021 (GSK-3 inhibitor) (Stemgent) to maintain them in the groundstate of pluripotency and passaged at regular intervals. The ESCs used in this study were derived in the laboratory of Duncan Odom (CRUK CI) and routinely cultured in 2i conditions in-house. ESC Hi-C libraries were prepared from 10 million cells as previously describedv) using −log10(1−quantile(v)), as previously described in refs. 25.Public datasets used in this study are listed in Supplementary Table The ChIP signal within the specified distance of a genomic feature was extracted from the binned ChIP-seq profile . The distance of the ChIP signal to the genomic feature was binned at 1 kb resolution and the mean ChIP signal within the 1 kb bins calculated.Alignment frequency across the genomic regions was extracted from the alignment map and the distance of a ChIP signal to the start of the genomic region calculated. The distance was then converted to a proportion of the genomic region and binned to two decimal places. Within the genomic region, the mean signal within a bin was calculated to produce a mean signal profile for each region. The profiles for each region were smoothed using Loess to produce a profile for a ChIP-seq track across many genomic regions.56 for the CTCF motif(s) with a q-value threshold of 1%. Where one peak contained multiple significant motifs, the most significant was considered as representative of the peak.Peaks of CTCF binding were identified  and the genomic sequence underlying the peak was queried using FIMOAlignment maps for IP and Input datasets for ESC or NSC cell types were submitted to ChromHMM for segmentation using a bin size of 200 and an extension size of 150 bp. All other parameters were defaults. Each IP target was represented for each cell type at least once: H3K4me1, H3K27ac, H3K27me3, and Pol2 , ‘Uniquely mapped Reads’ in Supplementary Table Hi-C data was processed using a custom pipeline as previously reportedRead pairs are then filtered to remove ambiguous (chimeric) or unaligned reads. This results in the ‘Total Contact Pairs’ in Supplementary Table Construction of contact matrices. Hi-C matrices are created at the fragment end (f-end) level by assigning each read to a f-end. The reads used at this stage are from ‘Total Contact Pairs’ in Supplementary Table cis:trans ratios, f-end coverage . Once aligned, the read pairs were reunited to filter out reads where either (or both) failed to align. Additionally, read pairs that were aligned in very close proximity were removed. Finally, potential PCR duplicates were removed by comparing the aligned coordinates of read pairs in each map. Maps for biological and sequencing replicates were merged once the quality of the replicates had been validated (using the Hi-C data processing steps below). Sequencing replicate maps were merged and the PCR duplicate removal step applied to the composite map. The composite maps were used for all downstream analyses. Hi-C read statistics for ESC, AST, and NSC datasets are shown in Supplementary Table 25. The number of aligned reads, number of reads associated to a fragment end, and the number of fragment ends identified can be found in Supplementary Fig. 26. Briefly, the method first randomises Hi-C matrices so to preserve both genomic distance and marginal contact distributions but specific features, such as compartments, TADs, or loops are not maintained. The randomisation algorithm repeatedly samples pairs of observed contacts and shuffles them (using a Markov Chain Monte Carlo-like approach) by swapping the fragment end points with a ratio proportional to their contact distance probabilities, correcting for the asymmetric distribution of sampled and shuffled distances (please see their manuscript for further information). We note that this method controls for marginal coverage effects. Observed contact densities are then compared to the randomised contact densities, generating a normalised interaction score (as referred herein). Specifically, to calculate the interaction score for each observed contact, the distribution of Euclidean distances to the nearest k neighbours was determined in the observed (k = 100) and shuffled (k = 200) matrices and then compared using the Kolmogorov–Smirnov D statistic to visualise positive (higher density in observed data) and negative (lower density in observed data). These D-scores are used to visualise interactions on a −100 to +100 scale, and referred to as Interaction scores in the text.Genomic coordinates of read pair alignments were attributed to HindIII restriction fragment ends and near-cis interactors removed as previously described25 using interacting fends separated by between 100 and 400 kb. Borders were defined as a 1 kb region about the maxima within the regions in the top 95th percentile of the scaling track. TADs were then defined as the genomic regions between these TAD borders. In addition, TAD borders were identified using the observed insulation track of a 250 kb region to identify genomic regions where insulation was above the top 15th percentile (as in refs. 57). Within these regions, the minimal insulation position was used to define the TAD border and TADs were filtered to be between 100 kb and 4 Mb. TAD maps were subsequently categorised into two self-interacting groups and the LaminB1 track used to predict the group being the active or repressive compartment. ESC or NSC LaminB1 tracks were downloaded from UCSC and the mm9 coordinates converted to mm10 using liftOver.TAD borders for ESC datasets were identified as previously described57. Unless otherwise stated, the insulation tracks were computed at a 1 kb resolution within a 50 or 300 kb region to identify localised or broader insulation points, respectively. Very near cis interactions within the 0–10 kb band (for the 50 kb region) or 0–20 kb band (for the 300 kb region) were not considered. Insulation was plotted either as a distribution of mean contact insulation 200 kb around borders  were found where one fragment end was within the TAD and the other was also in the TAD or up to 10 Mb from either TAD border. Contacts were then categorised as intra-TAD if both fragment ends were within the TAD borders or inter-TAD if one of the fragment ends was located in the upstream or downstream region. The proportion of intra-TAD high-score contacts to the total number of high-score contacts (intra-TAD + inter-TAD) was calculated.This analysis was performed as described in ref. To visualise the enrichment of Hi-C contacts over many distinct genomic loci, the observed and shuffled contact matrices were compared. At each genomic region and bin size specified, the number of contacts in the binned matrix was summed. The summary matrix was then normalised to the maximal value, the observed/shuffled calculated and visualised on a log2-transformed scale. For aggregate analyses that compare whole TADs, the set of TADs visualised are selected so that the size of the TADs considered are most similar. Unless stated, the resolution of these plots is 5 kb.2 region and the edges represent a ~60 kb*(TAD length−60 kb) region. The 60 kb edge size parameter is influenced by dataset size; this parameter describes how far from a TAD border an interaction can be that could be considered as spanning the entire TAD.To identify distinct distributions of interacting contacts within genomic regions, we developed a method termed ‘domainClassifyR’ github.com/ChristopherBarrington/domainClassifyR. The method requires two inputs: (1) 2D regions within which contacts should be classified and (2) locations of contacts and an associated score from which ‘high-scoring contacts’ (score of >40) can be filtered , selecting N contacts randomly (using the R sample function), where N is the number of high-scoring contacts in the 2D region. At each iteration, the number of randomly selected contacts in each sector is calculated. A table of L rows and four columns is produced, where each column describes the null distribution for a sector in the 2D region. The number of high-scoring contacts in each sector is then computed. For example, a 2D region is defined and is found to contain 5000 observed contacts, of which 900 are above the minimum score threshold and 600 are positioned in the corner sector. Within this domain, 900 of the 5000 contacts are randomly selected and the distribution among sectors calculated. This is repeated 1000 times to generate the null distributions. For each sector, the number of observed high-scoring contacts is compared to the distribution of randomly selected contacts using the Z statistic. Conversion of the Z-score to a p-value and Benjamini–Hochberg correction for multiple testing  converts the Z-score into a Q-value. A pair of thresholds is applied to the Z-scores: a minimum threshold to identify sectors that are enriched and a maximum threshold to identify sectors that are not enriched. Sectors with Z-scores between these thresholds may be considered ambiguous and are excluded from further analysis with this thresholding approach. In this study, a sector must score more than 15 to be enriched, between 5 and 15 to be ambiguous, and <5 to be not enriched.The contacts that are wholly contained within the bounds of the 2D region are then grouped into one of the four sectors. The contacts that meet the minimum score threshold are then identified; these contacts are the ‘high-scoring’ contacts from ‘all’ contacts. For this analysis, a contact is deemed ‘high-scoring’ when its score is more than 40. A null distribution of contact positions is then generated; this describes the number of contacts that may be expected to be in a given sector if there was no bias due to interaction score. The set of all contacts is sampled The enrichment of each sector in 2D regions is calculated independently so each region may have multiple enriched sectors. The classification of the 2D region is the aggregate of the enriched domain sectors. Regions that have a sector classified as ambiguous are excluded from further analysis. Increasing the lower threshold (from 5) or decreasing the upper threshold (from 15) would decrease the number of ambiguous sectors identified but could decrease specificity (or homogeneity) of the classified groups. Therefore, a TAD presented here with a ‘Corner’ classification (loop domain) shows specific (and exclusive) enrichment of high-scoring contacts in the 60 kb interacting region between the TAD borders. TADs with multiple enriched sectors were not considered; in these TADs, the Leading + Corner or Trailing + Corner sectors were most common.The precise location of TAD borders between multiple datasets is challenging to assess. We note that smaller TAD border windows (i.e. 1 or 10 kb) reduces the number of overlapping TAD borders between cell types. For example, when a 1 kb distance was observed for border overlap between ESC and NSC, this would result in 627 of 4344 ESC borders (14.4%). The overlap between TAD borders increased as the border distance increased; with a 10 kb distance, 1192 of 4344 ESC borders (27.4%) overlapped; with a 50 kb distance, 2997 of 4344 ESC borders (69.4%) overlapped. We proceeded with the 50 kb constraint to be able to have enough common borders to be used downstream. For either dataset, consecutive common borders were then identified. Where a consecutive pair of borders were common to both datasets, the domain was considered to be ‘maintained’. Depending on the comparison, the TAD flanked by a consecutive border pair may be ‘split’ into multiple TADs or be the result of multiple TADs ‘merging’ together (see schematic in Supplementary Fig. R density function was used to plot the frequency distribution of the normalised distance metric. Since the number of TADs in each classification is different, the space occupied by each TAD on the x-axis is variable; each horizontal strip represents a TAD.For contacts within each TAD, the midpoint between the contacts was calculated and the distance of the midpoint to the centre of the TAD was calculated (Supplementary Fig.R density function used to compute the frequency distribution of TAD-peak distance within TAD classes, border proximity and motif orientation. The distributions were normalised to their maximum values and the reverse motif distribution subtracted from the forward motif distribution; a value close to 1 therefore indicates a high value in the forward motif distribution and a near zero value in the reverse motif.CTCF-binding sites within −50 and 150 kb of a classified TAD border were identified. The genomic distance between the TAD border and the midpoint of the CTCF peak was calculated and the Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Nuclei are sorted by selecting the G1-S-G2 populations through a FACS Aria or FACS Vantage flow sorter. Flow sorted nuclei are embedded in agarose plugs and HMW DNA is purified with proteinase K and RNAse A and then isolated. Pulsed-field gel electrophoresis (PFGE) is used for DNA quality control. HMW DNA is subsequently label and analyzed on the Irys platform. Optical mapping (OM) data are then processed for de novo OM and hybrid assemblies.Nuclei sorting and High Molecular Weight (HMW) DNA isolation workflow. Nuclei suspensions are prepared by homogenization or chopping of root tips from germinated seeds or leaves from seedlings or plants, either potted or  Solanum crops and wild relatives. The methods that we further elaborated and refined focus onNext-generation genome mapping through nanochannels  of plant genomes brings genome assemblies to the ‘nearly-finished’ level for reliable and detailed gene annotations and assessment of structural variations. Despite the recent progress in its development, researchers face the technical challenges of obtaining sufficient high molecular weight (HMW) nuclear DNA due to cell walls which are difficult to disrupt and to the presence of cytoplasmic polyphenols and polysaccharides that co-precipitate or are covalently bound to DNA and might cause oxidation and/or affect the access of nicking enzymes to DNA, preventing downstream applications. Here we describe important improvements for obtaining HMW DNA that we tested on •in vitro plantlets.Improving flexibility of using different tissues as source materials, like fast-growing root tips and young leaves from seedlings or •Obtaining nuclei suspensions through either lab homogenizers or by chopping.•Increasing flow sorting efficiency using DAPI  and PI (propidium iodide) DNA stains, with different lasers (UV or 488 nm) and sorting platforms such as the FACSAria and FACSVantage flow sorters, thus making it appropriate for more laboratories working on plant genomics.The obtained nuclei are embedded into agarose plugs for processing and isolating uncontaminated HMW DNA, which is a prerequisite for nanochannel-based next-generation optical mapping strategies. Specifications TableSolanum lycopersicum), we used root tips, for S. commersonii young leaves from in vitro plantlets and for S. etuberosum young leaves from plants grown in pots. Second, we tested different options to obtain nuclei suspensions, namely homogenizing with a Polytron or chopping with a razor blade. The protocol for tomato root tips is as follows:The whole workflow for this method is summarized in the graphical abstract. The method follows Šimková et al. with the following modifications. First of all, we introduced variations in the starting material types. For tomato  in Tris buffer . Incubate in a water bath at 4 ℃ for 20 min and wash three times in Tris buffer at 4 ℃ for 5 min.Transfer the seedlings to a 2% formaldehyde solution .S. commersonii and S. etuberosum young plant leaves is as follows:The alternative protocol for in vitro grown plantlets or detached leaves from potted plants in formaldehyde solution as described above.Fix whole After rinsing, place 0.5–1 g of leaves in a glass Petri dish with 1 mL of 1.5× IB bufferChop the tissues using a sharp razor blade until a soft homogenate is obtained. This should be formed by very small pieces of leaves in a green suspension. Continue from step 1.6.www.silkandprogress.cz), respectively. Alternatively, samples can be filtered through a Falcon® 40 μm cell strainer . Collect the filtered nuclei suspensions aliquots up to a volume of ∼4 mL.Filter the crude homogenates through a 50 μm nylon mesh into a new polystyrene tube and a 25 μm nylon mesh .Add DAPI to a final concentration of 2 μg mLKeep samples on ice until flow cytometric analysis and sorting.For flow sorting, we adjusted the protocols of Šimková et al. and Vrána et al. to allow the use of either the FACS Aria or the FACS Vantage flow sorters. We introduced the following modifications:−1)Stain the nuclei with DAPI  with the following settings:a) Solid-state laser in the UV range ; b) 70 μm nozzle, 70 psi; c) sorting speed: 300 events/s.We performed data acquisition and analysis with the BD FACSDiva software Select the G1, S and G2 nuclei for sorting using DAPI-A vs DAPI-W dot plots a.Fig. 1aIn order to use the FACS Vantage , the following protocol for staining with PI was used:−1) in the dark for at least 10 min prior to the flow cytometry measurements.Stain the nuclei with Propidium Iodide   Argon-ion Innova 304 Laser  ; b) 70 μm nozzle; c) sorting speed 500 events/s in counter sort mode.We performed data acquisition and analysis with the CellQuest software Select PI-stained G1, S and G2 nuclei populations for sorting using PI-A vs PI-W dot plots, avoiding the inclusion of debris a.In both sorting platforms, use a 50 mM NaCl solution in MQ  water as sheath fluid. Collection tubes for the sorted nuclei contain 400–500 μL of ice-cold 1.5× IB. This volume should be equal to the volume that comes with the sorted fraction and depends on the sorted-droplet volume and number of sorted nuclei. Keep the samples as well as collection tubes at 4 ℃ during sorting using a precision refrigeration unit (±0.2 ℃) connected to the flow sorter.Solanum nuclei.Plug preparation was performed following Šimková et al. We modified the centrifugation steps (speed and time) to make them more efficient to recover Pellet nuclei  at 500 g and 4 ℃ for 30 min.−1 DAPI or 50 μg mL−1 PI  agarose  dissolved in 1x IB.Keep the mixture at 52 ℃ for another 5 min and then slowly pour the mixture into pre-warmed plug molds  using a wide bore pipette tip.Solidify the plugs at 4 ℃ for 10 minPush the plugs into a polystyrene tube with 500 μL per plug of lysis buffer C ). Add freshly prepared proteinase K  to reach a final concentration of 0.3 mg/mL (30 μL of stock solution per 1 mL). For the two washes, prepare 62 μL of stock solution per 1 mL of buffer. Weigh 62 μg and dilute in 62 μL of MQ  water. Before weighing, let the proteinase K adapt to room temperature to avoid condensation on the powder. Add 30 μL for the first wash and keep the rest at 4 ℃ for the second wash.Incubate at 37 ℃ for 24 h under gentle shaking (50 rpm) in an almost horizontal position.−1 proteinase K  and incubate plugs for another 24 h under the same conditions.Change for lysis buffer B , 0.3 mg mLAfter the proteinase K treatment, rinse and store agarose plugs in ET buffer  at 4 ℃.Check DNA quality using pulsed field gel electrophoresis  a and b.FThe plugs obtained were ready for RNAse treatment, DNA release and labeling following the standard protocol recommended by the genome mapping platform manufacturer (BioNano Genomics).2 at 10x magnification  for 20 min at 37 ℃). DNA was readily accessible for restriction enzyme digestion even at low concentrations, confirming its suitability for physical mapping   a and b.F50 in the consensus genome map. Complex regions in the genome can be spanned and resolved better, which contributes to the contiguity of the assembly.Production of HMW DNA of superior quality, i.e., molecules of several hundred kilobases, has been identified as the bottleneck in nanochannel-based genome mapping technologies (BioNano Genomics). Our method yields the longest DNA molecules on average . Run costs become lower, as longer molecules mean that less coverage is needed when doing genome mapping. Moreover, longer molecules with proper labeling frequency imply higher NContamination by cytosol inclusions in different plant cell types represents another important impediment to the use of optical mapping. Such cytoplasmic compounds, mostly phenolic compounds, polysaccharides and other secondary metabolites co-precipitate with DNA and interfere with enzymes used in DNA labeling. When nuclei are flow-sorted, the contamination with such substances is minimized, and so ultimately leads to fewer false negatives in optical mapping. Purity also reduces clogging of nanochannels, thus allowing for higher throughput since it extends chip lifetime. In terms of cytoplasmic contaminants, flow sorting yields HMW DNA of much higher purity compared to other methods, without cell debris and very low amounts of chloroplast and mitochondria contamination , which c6 nuclei in four agarose miniplugs, which was enough to produce 73 Gbp of size-filtered data with single molecule N50 of 290 Kbp on the Irys platform. We obtained this sample of nuclei in 3 h of sorting in a FACSAria or 6 h of sorting in a FACSVantage flow cytometer. These results confirm the excellent quality of nuclear plant DNA obtained through flow sorting which is similar to the quality obtained from mammalian cell cultures. Moreover, molecule size distribution was on par with human samples .The yield of pure, HMW DNA from the tomato cv Heinz 1706 material (genome size 950 Mbp) amounted about 1.6 × 10Solanum crops and wild relatives, in order to further improve the quality of genome sequencing and assembly, and for comparative structural genomics including related crops and wild relatives. We introduced relevant modifications that enhanced both efficiency and versatility of this method. The main adjustments are related to the use of different source materials (root tips or young leaves from seedlings or in vitro plantlets), different methods to obtain nuclei suspensions (homogenization or chopping), two DNA-specific fluorescence dyes (DAPI and PI) with their corresponding lasers (UV and 488 nm or 514 nm) and both classic and modern sorting platforms (FACS Vantage or Aria). With these modifications, we expect that the method is also successful in different sorting platforms and laser configurations, meaning that laboratories without access to the latest flow sorting technology still can have access to next-generation mapping. One point of extra attention when following this protocol using sorting platforms is that only standard UV lasers and DAPI can be used, as the formaldehyde in the fixed nuclei interferes with PI fluorescence. However, the histograms obtained in this work were clear and well defined, and had low CVs (coefficients of variation – ratio of the standard deviation to the mean) of DNA peaks. The possibility to use either mechanical homogenization or manual chopping allows for flexibility depending on the plant species. For example, nuclei from S. commersonii leaves better preserve their integrity and carry less debris when obtained by chopping with a razor blade than using a Polytron whereas this kind of homogenizer is ideal for tomato root material.Flow sorted nuclei provide a good starting point for mapping and sequencing technologies where high purity and megabase-sized DNA is required. The protocol  that we In the case of elite or proprietary breeding material, seed propagation is often not possible or it is necessary to preserve the genotype to be analyzed so the only way to obtain enough material is through vegetative propagation. It would not be possible to isolate HMW DNA from this kind of materials from embryonic root tips. The modifications included in this method allow for isolation of nuclei from young leaf material, thus enabling the use of genome mapping for breeding lines that are propagated vegetatively.In conclusion, the workflow proposed here involving the coupling of flow sorting with nanochannel-based mapping will allow this genome mapping technology to fulfill its potential in plant genomics and genomics-based breeding.de novo sequencing of an increasing number of plant species. However, nearly-finished well-assembled genomes are not easy to obtain. Issues related to order and orientation of contigs and distribution of repetitive sequences remain major challenges [50 or percentage of whole genome assembled, by sizing and/or closing gaps, scaffolding, joining scaffolds, correcting assembly errors and even identifying, spanning and assembling repeated sequences. In addition, genome mapping can in its own right provide a comprehensive assay system for defining structural variation among related species or genotypes within a species [High-throughput NGS technologies have enabled the allenges . Genome allenges . Among sallenges  or Chicaallenges ), the neallenges ,9 have pThe nanochannel-based genome mapping technology has been described extensively ,9. This Medicago truncatula [Aegilops tauschii [Despite all these advantages, nanochannel-based genome mapping has been used only recently for the assembly of DNA in higher plants such as spinach , subterruncatula ,24. It htauschii  and moretauschii .Setting aside issues of genome size and complexity and computational limitations, one of the main bottlenecks for the application of nanochannel-based genome next-generation mapping to plant genomes is the requirement of high quality HMW DNA. Such DNA is easier to obtain from mammalian cells than from plant cells, because of many important differences in their composition. The rigid cell walls in plant cells demand for mechanical methods to disrupt them, which can cause shearing of the DNA. There are various contaminants in plants that are not found in mammalian cells, such as chloroplasts and a range of secondary metabolites which contaminate the DNA sample during the precipitation process . Sometim


Staphylococcus aureus. Here, we report the formicapyridines which are encoded by the same gene cluster and are structurally and biosynthetically related to the fasamycins and formicamycins but comprise a rare pyridine moiety. These compounds are trace-level metabolites formed by derailment of the major biosynthetic pathway. Inspired by evolutionary logic we show that rational mutation of a single gene in the biosynthetic gene cluster encoding an antibiotic biosynthesis monooxygenase (ABM) superfamily protein leads to a significant increase both in total formicapyridine production and their enrichment relative to the fasamycins/formicamycins. Our observations broaden the polyketide biosynthetic landscape and identify a non-catalytic role for ABM superfamily proteins in type II polyketide synthase assemblages for maintaining biosynthetic pathway fidelity.The formicamycin biosynthetic gene cluster encodes two groups of type 2 polyketide antibiotics: the formicamycins and their biosynthetic precursors the fasamycins, both of which have activity against methicillin-resistant  Formicapyridines are similar to pentacyclic fasamycin and formicamycin aromatic polyketides but with a pyridine moiety. Here the authors rationally mutate the biosynthetic gene cluster to increase production and identify a non-catalytic role for the ABM superfamily of proteins. In addition, some pathways are not linear, and the final product is accessed via several routes due to the inherent substrate plasticity of the biosynthetic enzymes; well-studied examples include the rapamycin and erythromycin pathways2. Thus, in practice, any biosynthetic pathway will lead to the accumulation of a mixture comprising the final product plus varying concentrations of pathway intermediates and shunt metabolites. The composition of such a mixture will vary further when alternate growth conditions are used3. Sometimes the final product of the pathway cannot be clearly discerned.An idealised linear biosynthetic pathway to a complex natural product can be imagined proceeding through a series of intermediate structures which would exist for some finite time as the pathway product accumulates. These hypothetical intermediates would exist freely in solution or bound to enzymes in the case of assembly line processes, and eventually all flux through the pathway would end and only the final product would exist. In reality no pathways proceed in this manner as the situation is complicated by varying rates of reaction for the different steps, meaning that some intermediates accumulate, while the inherent reactivity of other intermediates, or their ability to act as substrates for housekeeping enzymes not dedicated to the pathway, means that shunt metabolites often arise. Matters are further complicated by the fact that pathways are often convergent, with multiple units made in parallel before assembly into the final product, for example in the biosynthetic pathways to macrolide or aminoglycoside4. We recently reported the formicamycins, aromatic polyketides produced by Streptomyces formicae KY5 isolated from the fungus-growing plant-ant Tetroponera penzigi  operating in conjunction with post-PKS modifications that include O-methylation and halogenation, plus oxidative and reductive modifications. Intrigued by these compounds, which exhibit potent antibacterial activity, we employed targeted metabolomics to identify further congeners that may have been missed during manual analysis of culture extracts. This led us to identify the formicapyridines (1–9), pyridine containing polyketide alkaloids which represent additional products of the formicamycin (for) biosynthetic gene cluster (BGC). Products of type II PKS systems which contain a pyridine moiety are rare7.Such mixtures of compounds are said to comprise a series of biosynthetic congeners, and their identification can provide valuable information about a biosynthetic pathway and may sometimes lead to new biosynthetic understandingigi Fig. 5. In tot8. In contrast, the formicapyridines are minor shunt metabolites that arise due to derailment of the formicamycin biosynthetic pathway. We show here that reprogramming the for BGC, by deleting the forS gene encoding an ABM family protein, leads to significantly increased production of formicapyridines with concomitant reduction of the combined titre of fasamycins and formicamycins. These and other mutational data lead us to the hypothesis that ForS is not a cyclase but forms part of a multienzyme complex where it acts as a chaperone-like protein to aid in maintaining pathway fidelity and performance. The discovery and engineering of formicapyridine biosynthesis raises intriguing questions regarding the evolution of type II PKS biosynthetic pathways and the origins of natural product chemical diversity.Challis and co-workers have shown that the majority of actinomycete derived polyketide alkaloids, including those containing a pyridine moiety, arise from reactive intermediates formed after transamination of aldehydes generated from reductive off-loading of the thioester bound polyketide chain from a type I modular PK9. Instead, as most fasamycin and formicamycin congeners are halogenated , and a strain in which the halogenase gene was deleted (ΔforV)5 5 Fig. . Our earn = 3) ethyl acetate extracts of the wild type (WT), Δfor and ΔforV strains, along with equivalent extracts from uninoculated mannitol soya flour (MS) agar plates, were analysed by liquid chromatography high-resolution mass spectroscopy (LC-HRMS). Using the Profiling Solutions software (Shimadzu Corporation), the WT dataset , leaving twelve previously identified fasamycin and formicamycin congeners, two additional fasamycin/formicamycin congeners that remain uncharacterized due to trace levels, and the known isoflavone 6-chlorogenistein11  with mass spectra suggesting a close structural relationship to the fasamycins/formicamycins; these varied only in the number of methyl groups present. By searching for equivalent ions lacking chlorine atoms we identified three additional congeners (1–3). Compounds 1–6 were not initially observed in the UV chromatograms of the WT, and qualitative examination of the LCMS data suggested titres at least 100-fold lower than for the formicamycins. Furthermore, the m/z data implied the presence of a single nitrogen atom.Replicate  with MS characteristics like those of 4–6  was added, as our previous study showed this leads to the biosynthesis of brominated formicamycin congeners mM was a4–6 Fig. , support1–9 that were used to search the online chemical database REAXYS. This suggested that they represented new structures. To isolate sufficient material for structure determination and antibacterial assays, the growth of S. formicae KY5 was scaled up . After 9 days incubation at 30 °C the combined agar was chopped up, extracted with ethyl acetate, and the solvent removed under reduced pressure. The resulting extract was subjected to repeated rounds of reversed-phase HPLC followed by Sephadex LH-20 size exclusion chromatography leading to small quantities of purified 1–6.HRMS data allowed us to predict molecular formulae for 2 which was isolated in the largest amount (~2 mg). HRMS and 13C NMR data indicated a molecular formula of C28H25NO6 ; observed m/z 472.1753 ([M + H]+); Δ = −0.4 ppm) indicating 17 degrees of unsaturation. The UV spectrum showed absorption maxima at 229, 249, 272 and 392 nm indicating a complex conjugated system different to that of the fasamycins/formicamycins5. Inspection of the 13C NMR spectrum showed 21 sp2 carbons (δC 99.90–167.54 ppm), one carbonyl carbon (δC 191.81 ppm), four methyl carbons , one methoxy carbon (δC 55.80 ppm) and one sp3 quaternary carbon (δC 40.49 ppm). The 1H NMR spectrum revealed the presence of five methyl singlets , two aromatic proton singlets (δH 7.62 and 7.65 ppm), plus four aromatic proton doublets , 6.70 , 6.34  and 6.42 ). Limited 1H–1H COSY correlations meant we were reliant upon HMBC-based atomic connections, and through-space NOESY correlations, which led to two potential substructures consisting of 26 carbon atoms, leaving one carbonyl, one phenol and one quaternary carbon unassigned , the closest structural congener. The data were in excellent agreement with the main differences at C7, C22 and C23, consistent with the adjacent nitrogen atom , and the methoxy at C5 with H4 (e.g. 3 and 6). We also used NOESY and HSQC correlations to distinguish C14 and C16 once one was chlorinated: given the NOESY correlations to the gem-dimethyl groups of C26 and C27 with H16, and the disappearance of a HSQC linkage for C14, we concluded that C14 was chlorinated (e.g. 4–6). Compounds 1–6 exhibit optical activity with small [α]20D values between +8° and +13° indicating probable atropoisomerism about the axis of the C6–C7 bond. We attempted to assign the absolute configuration by combining computational approaches with electronic circular dichroism (ECD) spectra as done for the fasamycins5. However, despite scaling up production to isolate additional 2, meaningful ECD spectra could not be obtained. Full characterization data for 1–6 are presented in Supplementary Note 7–9, we have assigned preliminary structures based on 4–6, with a bromine atom replacing chlorine at C14.We first determined the structure of 1–6 displayed no antibacterial activity against Bacillus subtilis 16812 using overlay assays at 5 µg mL−1 (the average concentration for illustrating fasamycin and formicamycin bioactivity). Similarly, no inhibition was seen at tenfold this concentration (50 µg mL−1), with only small zones of inhibition at 100-fold (500 µg mL−1). To confirm this was not a result of reduced diffusion from the disc, assays were set up to test growth of B. subtillis 168 in liquid cultures containing compounds 1–6, but again they showed no activity.Compounds for and ΔforV mutants verified that the fasamycin/formicamycin BGC is required for formicapyridine production. The Δfor mutant does not produce 1–6 .Interrogation of LC-HRMS data from extracts of the Δ5. This would be followed by a series of cyclization and aromatisation steps, presumably in a sequential manner, with the final cyclization event probably involving formation of the B-ring. However, premature hydrolysis of the acylcarrier protein from putative intermediate 27, prior to the action of a final cyclase, would liberate the enzyme-free, β-ketoacid species 28 that would be highly facile to spontaneous decarboxylation yielding the methylketone 29. The action of an endogenous aminotransferase could then generate either of the species 30 or 31, which could undergo cyclization, dehydration and oxidation to yield 1. Consistent with a shunt pathway, no aminotransferase encoding gene could be identified in the for BGC, and although numerous putative aminotransferase encoding genes were identified in the genome, none could be linked to a role in formicapyridine biosynthesis. Alternatively, direct addition of ammonia to the C23 ketone would generate an imine, tautomerize to the enamine, and the resulting amino group could then attack the C7 ketone. Subsequent dehydration would lead directly to the formicapyridine system with no requirement of an oxidation reaction.The very low level of formicapyridines made by the WT strain suggests they are shunt metabolites arising from aberrant derailment of fasamycin/formicamycin biosynthesis. On this basis, we suggest a biosynthetic pathway as described in Fig. S. formicae KY5 was cultivated on MS agar plates for two days and then overlaid with a solution of  sodium acetate . This was repeated on the four following days, and after a total of 9 days incubation the agar was extracted with ethyl acetate and the most abundant congener was isolated , and the 13C NMR spectrum was acquired. Due to the small amount of material, and overlapping signals, only eight of the intact acetate units could be unambiguously identified based on their coupling contests. In addition, a singlet was observed for C25 which showed weak enrichment . The function of TcmI has been verified by in vivo mutational analysis19 and biochemical characterisation21. It catalyses cyclization of the final ring during formation of tetracenomycin F1 from tetracenomycin F2, and this reaction is remarkably like that proposed in our hypothesis for the formation of ring-B as the final step of fasamycin backbone assembly  comprising the conserved barrel domain of the cupin superfamily (cl21464)24. Finally, the gene products ForS and ForU are both single domain proteins (pfam 03992) belonging to the ABM superfamily (cl10022). A notable member of this family is ActVA-orf6, which functions as a monooxygenase during biosynthesis of the polyketide antibiotic actinorhodin; the structure of this enzyme has been solved and it is topologically related to the PKS cyclase TcmI and homologues25. Notably, ABM family domains are found in several PKS cyclases including the C-terminal domain of BenH from the benastatin BGC27, which also comprises an N-terminal TcmN like cyclase domain (pfam 03364); and WhiE Protein 1 and other members of the SchA/CurD-like family of PKS enzymes commonly associated with BGCs for the biosynthesis of spore pigments in Streptomyces spp30. SchA/CurD- and WhiE Protein 1-like enzymes are comprised of an N-terminal ABM domain and a C-terminal PKS cyclase domain .The gene product ForD shows significant sequence similarity to aromatase/cyclases such as the N-terminal domain pfam 03364) of the archetypical tetracenomycin polyketide cyclase TcmN, which belongs to the Bet v1-like superfamily (cl10022) of the a31 to make in-frame deletions in each of these five putative cyclase genes . Three independent mutants generated from each gene deletion experiment, along with the WT strain, were grown on MS agar and cultured at 30 °C for 9 days. To assess differences in secondary metabolite production the ethyl acetate extracts from each culture were subjected to HPLC(UV) and LCMS analysis  or the constitutive ermE* promoter (ΔforR/forR), although the titres did not reach that of the WT strain in all cases. For the ΔforU mutant complementation with forU restored production of the non-halogenated congener 10 only, indicating a polar effect on the downstream halogenase forV gene. Subsequent complementation with a forUV cassette under the ermE* promoter in which the two genes were transcriptionally fused led to the production of halogenated fasamycin and formicamycin congeners.To interrogate their roles, we used Cas9-mediated genome editingforS mutant when compared with the WT  and solvent extraction, followed by isolation (3.4 mg) and structural elucidation identified this compound (13) as the C24-carboxyl analogue of fasamycin C (which we have named fasamycin F). This is the first congener identified with the C24-carboxyl group intact.In contrast, production of formicapyridines was increased ∼25-fold in the Δ WT Fig. . These efor BGC is responsible for the production of three structurally differentiated pentacyclic scaffolds. Inspired by evolutionary considerations we introduced a gene deletion into the BGC, which significantly altered the relative levels of these metabolites: the titre of formicapyridines was significantly increased (∼25-fold) in contrast to the fasamycin/formicamycins, which were decreased to approximately one-third of the WT titre. These results raise a series of questions about the cyclization events associated with the fasamycin/formicamycin biosynthetic pathway, and with the maintenance of pathway fidelity.Using targeted metabolomics, we identified a family of pyridine containing polyketide natural products that we have named the formicapyridines. Remarkably, these compounds are derived from the fasamycin/formicamycin biosynthetic machinery meaning that the forS led to the isolation of shunt metabolite 13 with a C24-carboxyl group that was not observed from the WT. However, while ForS is implicated in ring-B cyclization it is not required for this role, nor, apparently, for the biosynthesis of any congeners. Rather, ForS seems to decrease the production of aberrant congeners from the pathway, e.g. formicapyridines, while increasing overall productivity. The most parsimonious interpretation of these data is that another of the for BGC gene products is the actual catalyst for ring-B formation, and that ForS acts as a chaperone, which modulates or stabilises the assembly, or arrangement, of a multienzyme complex to optimise production of the fasamycins, and therefore ultimately the formicamycins. This has the consequence of minimising the production of shunt metabolites (formicapyridines). Thus, while deletion of forS leads to the phenotype desired from our thought experiment, the mechanism by which this occurs is not what was anticipated. Interpretation of the for BGC bioinformatic analysis suggests the most likely candidate for a ring-B  cyclase is ForL due to its close relationship with TcmI, which catalyses a similar final cyclization step during tetracenomycin biosynthesis21. The apparent lack of any intermediates or shunt metabolites being accumulated by the remaining mutants is somewhat surprising and suggests an absolute requirement for the formation of a PKS-cyclase complex before biosynthesis can be initiated.Our data suggest that formation of ring-B of the fasamycin scaffold is the final biosynthetic step, followed by thioester hydrolysis to liberate the ACP and subsequent decarboxylation to remove the carboxyl group attached to C24 Fig. . Consist32. Pradimicins are pentangular polyketides similar to the benastatins27, and the BGC for their production contains three PKS cyclases equivalent to ForD (PdmD), ForL (PdmK) and ForR (PdmL)33. It also contains two ABM domain proteins  which were assigned monooxygenase roles. Through heterologous expression of PKS gene cassettes it was deduced that for the biosynthetic pathway to function correctly, and yield a pentangular backbone, all three cyclase genes (PdmDKL) plus the ABM domain monooxygenase PdmH must be co-expressed. In this pathway one oxidation reaction to form the quinone structure is required. These results led the authors to propose a model in which the two cyclases PdmKL and the monooxygenase PdmH form a multienzyme complex that engulfs the entire polyketide molecule during its assembly and work synergistically to ensure the correct reaction pathway occurs thereby minimising the production of shunt metabolites32. Similarly, formation of the unusual discoid metabolite resistomycin involves an extremely rare S-shaped folding pattern and requires the coordinated function, likely as a multienzyme complex, of core PKS proteins in addition to three distinct cyclase enzymes23. Moreover, heterologous reconstruction of the resistomycin pathway gave no products when the minimal PKS plus first cyclase were assembled23, a rare observation that is in keeping with our data showing the requirement of all the putative cyclases ForDLRU to produce any pathway derived metabolites, including shunts.These observations are reminiscent of studies regarding the biosynthesis of pradimicinfor BGC polyketide backbones does not require a monooxygenase. Consistent with this, deletion of forS does not abolish polyketide production, but instead affects pathway productivity and fidelity. Thus, based on our mutational data and the observations discussed above, we hypothesise that ABM family enzymes can act as monooxygenases and/or as ancillary proteins to tune, in some way, the PKS enzyme complex function, and therefore the biosynthetic pathway, during aromatic polyketide biosynthesis. Genes encoding these proteins occur commonly in PKS BGCs, and multiple paralogues are often present even when monooxygenase reactions are not required. It is noteworthy that several PKS cyclases occur as fusion proteins with an ABM domain, which can be located at either terminus. We speculate that these may represent examples of mature pathways where the chaperone-like function of the ABM family protein has become essential, leading to selective pressure for the encoding gene to become transcriptionally fused with other cyclase encoding genes.Biosynthesis of the 13C2] sodium acetate was purchased from CORTECNET. All solvents were of HPLC grade or equivalent. NMR spectra were recorded on a Bruker Avance III 400 MHz NMR spectrometer equipped with 5 mm BBFO Plus probe. 13C NMR spectra for 2 and isotopically enriched 2 after feeding  sodium acetate were recorded on a Bruker Avance III 500 MHz NMR spectrometer equipped with a DUL cryoprobe at 30 °C.Unless stated otherwise all chemicals were supplied by Sigma-Aldrich or Fisher Scientific.  sodium acetate (1 mL of a 60 mM solution) after 24 h, 48 h, 72 h, 96 h and 120 h. After a further 72 h the agar was extracted and purified using the methods described above to yield a sample of 2 (0.9 mg). This material was analyzed by LCMS and inverse gated 13C NMR . However, due to the weak and overlapping signals, only the following coupling constants (JCC) of the intact acetate units were recorded: C24–C1, 44.61 Hz; C2–C3, 68.58 Hz; C4–C5, 68.16 Hz; C20–C21, 56.18 Hz. In addition, C14, C16, C18 and C22 have coupling constants of 66.47, 67.61, 42.08 and 60.43 Hz, respectively. Spectroscopic data is presented in Fig. S. formicae ΔforS was cultivated on 4 L (~120 plates) of MS agar at 30 °C for nine days. The agar was extracted and purified using the methods described above to yield 13 (3.4 mg). This material was analysed by LCMS and 1D and 2D NMR . Spectroscopic data is presented in Supplementary Figs. S. formicae WT or mutant strains (n = 3) were grown on MS agar at 30 °C for nine days. A rectangle of agar (2 cm3) was excised from each petri dish, sliced into small pieces and shaken with ethyl acetate (1 mL) for 20 min. The ethyl acetate was transferred to a clean tube and the solvent removed under reduced pressure. The resulting extract was dissolved in methanol (200 µL) and analysed by LCMS but using the following modified UPLC method: Phenomenex Gemini C18 column ; mobile phase A: water +0.1% formic acid; mobile phase B: methanol. Elution gradient: 0–2 min, 50% B; 2–14 min, 50–100% B; 14–18 min, 100% B; 18–18.1 min, 100–50% B; 18.1–20 min, 50% B; flow rate 1 mL min−1; injection volume 10 μL.10 , formicamycin C 16 , formicapyridine D 4  and fasamycin F 13 . The content of 10 and 16 was determined by UV absorption at 285 nm. The content of 4 and 13 was determined by MS analysis of the base peak chromatogram (positive mode). Each standard solution was measured three times.Calibration curves (Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Source Data


However, the number of such records are extremely limited globally and the length of such time series are usually less than a decade. This situation severely handicaps their use in model testing and evaluation. Here we present a global assessment of freshwater mollusk  isotope data from 25 river basins that have stream water isotope values, water temperature data and shell material isotope signatures. Our data span a latitude range of 37.50°S to 52.06°N. We show that δ18O signatures in freshwater mollusks are able to explain 95% of the variance of stream water δ18O. We use shell δ18O values and water temperature data to reconstruct stream water δ18O signatures. With freshwater mussel life expectancy ranging from a few years up to 200 years, this translation of mollusk metabolic properties into long term stream water isotope records is a promising approach for substantially extending global stream water isotope records in time and space.Records of δ However, the vast majority of these studies have been restricted to relatively short time periods (covering a few years at best) in small experimental catchments. Long stream water isotope records, while of great value for enhancing our understanding of the water cycle in river basins, or assessing environmental and climatic changes on the continental water cycle, remain restricted to only a few large river basins as linked to the Global Network of Isotopes in Rivers (GNIR) of the International Atomic Energy Agency5.Stream water stable isotopes of oxygen and hydrogen have been used for decades in hydrological process studies on water source and age10. Freshwater mollusks live in a wide variety of aquatic habitats and potentially hold information in their successive growth bands on interannual fluctuations in stream water isotope signatures over multiple decades and even centuries. For rivers in India, δ18O signatures in freshwater mollusks have been shown to follow stream water δ18O across different topographic settings10. To date, no global assessment has been undertaken to see if mollusk isotope records match streamflow isotope records across different continents, elevations, climates and hydrological regimes (e.g. from rain to snow fed rivers).Recent work has suggested that freshwater mollusks have the potential to complement the scarce stream water isotope records as living archives of in-stream environmental conditions18O signatures are a strong proxy of δ18O in stream water across a wide range of climates and hydrological regimes – offering the potential for freshwater mollusks to reconstruct historical streamwater isotope signals in global rivers. We leverage the fact that oxygen isotopes in mollusk shell material precipitates in equilibrium with water and past studies that have shown it can serve as a robust proxy for water temperature reconstruction11.We hypothesize that shell-related δ18O isotope signatures and their corresponding precipitation and stream water δ18O isotope data. We base this on published oxygen isotope signatures obtained from growth bands of freshwater mollusk shells  collected in 25 river basins  with contrasting elevations and climates.Here we present a global assessment of mollusk shell δ18O signals. The mollusk sampling sites spanned a latitudinal range of 37.50°S to 52.06°N and an elevational range of 2 to 3250 m.a.s.l. Maximum elevations for the river basins ranged from 2 to 7620 m.a.s.l. and catchment areas ranged from ~75 km2  to 400.000 km2 (Niger River in Africa).From an initial list of 170 individual studies with published isotope signatures in freshwater mollusks, only 15 studies on 25 rivers and 33 sampling sites had both stream water isotope values and shell material isotope values. We relied on these 15 studies for conducting our comparative analysis Table . For 22 18O signatures (without correction for temperature effects) in mollusks mirrored the δ18O values found in stream water, with mean values plotting close to the 1:1 line (slope of regression line = 0.82) and standard deviations below 1.5‰  prevailing along a latitudinal gradient in the 25 river basins – spanning from equatorial to polar climates  and mollusk shell δ18O signatures – as expressed in high standard deviations of both precipitation and stream water isotope signatures  in the humid rain fed headwaters, to losing sections (defined as flow losses with increasing basin area) near the Sahara13, finally leading into more humid regions and gaining flow further downstream.Outliers to the general pattern included sites located at higher elevations [e.g. ET Köppen climate zone], where stream water would be slightly more depleted than shell material Fig. . Alterna14. This may eventually lead to an increasing discrepancy between shell and stream water δ18O, with shell δ18O signals exhibiting truncated patterns across potentially reduced growth periods. As a corollary this may lead to larger differences between stream water and shell δ18O signals, as noticed for both low and high latitudes in our set of 25 river basins . Towards higher latitudes, amplitudes of stream water temperature tend to increase, whilst mean annual water temperatures decrease  and environmental  conditions, bivalves may reduce or even cease their growth during more or less prolonged periods of timeins Fig. . As an ease Fig. . The Hur18O and temperature are a common tool for reconstructing shell δ18O values15. We adapted this approach to reconstruct stream water δ18O for a subset of 22 rivers for which both shell mean δ18O and mean stream water temperature were available  – the reconstructed data explaining 93% of the variance of measured stream water δ18O  in the glacier and snowmelt Andean rivers of the Mendoza province . At lower latitudes the warm and drier climate conditions [e.g. Aw Köppen climate zone] signals were more enriched in reconstructed and measured isotope signatures in stream water  of our dataset (Table 18O (±1σ) for calculating stream water δ18O . A notable exception to this pattern is the Niger River, where a considerable uncertainty (±3.6‰) is triggered by the large variability in shell δ18O (σ = 2.89‰), despite a rather small seasonal stream water temperature amplitude (12.6 °C). The Amazon River equally exhibits a large uncertainty in estimated stream water δ18O (±3.3‰), with the effect of a small seasonal stream water amplitude (6.2 °C) outweighed by a large variability in shell δ18O (σ = 3.01‰).Given the strong control of stream water temperature on mollusk shell growth, stream water δle Table . Our resgs Table  & S2d. Ater Figs . In Afri18O data across a latitudinal sequence of 33 sampling sites reveals the strong links between precipitation, stream water and shell signatures – suggesting the potential for freshwater mollusks to serve as archives of past isotopic signatures in rivers over a wide range of hydro-climatological settings. Long records of δ18O series are of great value for gaining a better understanding of long-term variability and/or non-stationary hydrological, ecological, biogeochemical and atmospheric responses to global change. However, the numbers of such records remain extremely limited globally and the length of such time series are usually less than a decade. An estimated 1000 freshwater bivalve species (order Unionoida) populate a large variety of river systems and lakes around the globe. Freshwater mussels are long-lived organisms, living an average of 10 years – with many species living 20 to 30 years and some up to two centuries16 (e.g. Margaritifera margaritifera)17. Such high longevity gives them the potential for recording across their successive growth bands long-term changes in environmental conditions. While past work has used mollusks for paleo-temperature reconstructions18, our analysis shows that mollusk shells formed in isotopic equilibrium with the surrounding water11 may serve as new tools for reconstructing stream water δ18O signatures over several decades from δ18O data series measured across mollusk shell increments.Our global assessment of mollusk shell δ18O data, four studies relied on whole-shell analyses for determining time-averaged δ18O values. Another eleven studies had analyzed successive growth increments, delivering either seasonal or inter-annual variability in δ18O signals over multiple years. A limitation to the full reconstruction of stream water δ18O data is the sensitivity of freshwater mussels to stream water temperature or salinity that may induce growth periods of different lengths depending on latitude, elevation and prevailing climate conditions. Aestivation and growth interruption may eventually cause a limited representativeness of shell δ18O data for reconstruction of annual or inter-annual stream water δ18O signatures19. Various studies on freshwater bivalves have shown that shell growth ceases at temperatures below 12 °C20. At high latitudes/elevations, shell δ18O signals exhibit an increasingly truncated sinusoidal pattern with narrow peaks  and wide troughs7. At lower latitudes/elevations, elevated water temperatures (20 to 35 °C) may substantially modify metabolic rates in freshwater mollusks and compromise biological processes such as survival, growth and reproduction21. In tropical regions with typically small temperature variations the dominant control on shell δ18O variations are the changes in stream water δ18O or monsoonal influences22. High discharge or low water conditions may lead to more or less prolonged growth gaps, extending up to 150 days in the Oubangui and Niger river basins9.In our global assessment of mollusk shell δAn additional source of uncertainty relates to the water sampling protocols applied in the 33 mollusk collection sites used in our study. While water had been sampled at, or nearby shell collection sites, sampling frequency differed strongly – ranging from unique grab samples of water to fortnightly water sampling protocols extending over several years.18O (time-averaged vs. successive growth increments) and river water δ18O (grab samples vs. continuous sampling) across our set of 33 sampling sites resulted in substantial differences between the related standard deviations of observed and reconstructed δ18O values. Ultimately, water and mollusk shell sampling frequency, as well as individual mollusk species’ physiology, ecology and life cycle are crucial for the interpretation of observed and reconstructed isotope signatures23.The highly variable temporal resolution in shell δ24. A thorough mineralogical assessment of the degree of preservation of aragonitic mollusk shells is therefore strongly recommended (e.g. via X-ray diffraction)25. An important next step in this context is the implementation of calibration studies – either via controlled experiments or field surveys – contributing to a better understanding of the relationships between stable isotope signals and ecological and/or environmental variables. This will eventually lead to an improved interpretation of measured variability as ecological and/or environmental ‘signal’ or stochastic ‘noise’26.When considering the effects of mollusk shell analysis for reconstructing historical data series, it is also important to consider that aragonite is thermodynamically unstable – with a gradual recrystallization to calcite leading to a ‘resetting’ of isotopic signatures18O records from mollusk shells have the potential to open up new research avenues for quantifying climate change impacts on the long-term isotope time series of precipitation and stream water27, leading to new mechanistic understanding of processes controlling water flow and quality28, or serving for the calibration and validation of flow and transport models30.Regardless of current limitations, δet al.31.We used the search terms SHELL, ISOTOP*, FRESHWATER and OXYGEN in Web of Science and found 170 individual studies on isotope signatures in mollusks. This list was reduced to 15 studies carried out on 25 rivers (lakes were ruled out), for which stable isotope data was available for both stream water and shell material  or spanning over successive growth increments (i.e. years of growth bands). For an individual site, mean values either correspond to an average of δ18O values from several shells or to an average of δ18O values sampled across shell growth increments.For the 33 mollusk sampling sites of our global assessment, δ18O in shell material to δ18O in stream water, the reported studies relied on equations relating water temperature and δ18O in stream water , such as: 18Oar values are first made relative to the Vienna Pee Dee Belemnite (VPDB) reference, they are commonly converted to the Vienna Standard Mean Ocean Water (VSMOW) as per Gonfiantini et al.15:18O from shell δ18O and stream water temperature:For converting δentclass1pt{minimas Tables –S3.12 to determine δ18O ranges in precipitation (Table For the 25 river basins, we relied on the Köppen-Geiger climate zones as per Rubel and Kottekon Table .Supplementary information