Neocortical computations underlying vision are performed by a distributed network of functionally specialized areas. Mouse visual cortex, a dense interareal network that exhibits hierarchical properties, comprises subnetworks interconnecting distinct processing streams. To determine the layout of the mouse visual hierarchy, we have evaluated the laminar patterns formed by interareal axonal projections originating in each of ten areas. Reciprocally connected pairs of areas exhibit feedforward/feedback relationships consistent with a hierarchical organization. Beta regression analyses, which estimate a continuous hierarchical distance measure, indicate that the network comprises multiple nonhierarchical circuits embedded in a hierarchical organization of overlapping levels. Single-unit recordings in anaesthetized mice show that receptive field sizes are generally consistent with the hierarchy, with the ventral stream exhibiting a stricter hierarchy than the dorsal stream. Together, the results provide an anatomical metric for hierarchical distance, and reveal both hierarchical and nonhierarchical motifs in mouse visual cortex. Mouse visual cortex is a dense, interconnected network of distinct areas. D’Souza et al. identify an anatomical index to quantify the hierarchical nature of pathways, and highlight the hierarchical and nonhierarchical features of the network. The classical notion of a cortical hierarchy posits that signals from primary visual cortex (V1) are routed through increasingly specialized cortical areas by ascending feedforward (FF) pathways, whereas descending feedback (FB) pathways selectively shape cortical responses within the receptive field (RF) depending on past experiences and task demand5. Accordingly, each higher area is thought to assemble the feature-selective RFs of lower areas and integrate them to form increasingly complex representations of the visual scene whose diverse spatiotemporal features are distributed across multiple higher-order areas6. While this bottom-up view of visual processing has inspired theoretical models of object recognition and categorization7, it fails to account for the densely reciprocally interconnected network of the neocortex as well as the inferential nature of visual perception12. Furthermore, it is unclear if a sequential ranking of areas, as proposed by several hierarchical models14, adequately describes the visual cortical network or whether the visual system includes nonhierarchical motifs that involve communication between areal pairs that do not exhibit conventional FF and FB relationships15.Visual perception is accomplished in part by generative intracortical mechanisms that produce invariant object recognition, provide contextual influences, and compare incoming information with prior knowledge16. Thus, pairwise comparisons of distinct FF and FB signatures allow the construction of sequences of interactions between functionally specialized areas. While interactions between hierarchical levels could conceivably occur in discrete steps, interareal processing can alternatively be graded, relying on connections that are neither completely FF nor FB. This necessitates assigning hierarchical distance values between levels and ranking areas in the context of all possible interareal connections19 rather than by pairwise comparisons using qualitative criteria4. In primate visual cortex, this procedure drastically reduces the number of solutions for ordering areas20. Here we show that in mouse, a similar procedure provides a unique insight into the hierarchical network. Specifically, it allows addressing the extent to which pairwise hierarchical interactions between areas A and B, and nonhierarchical interactions between B and C coexist.Because FF and FB pathways project to different cortical layers, the laminar architecture of interareal pathways is thought to be essential for hierarchical processing in primates and rodents14. The study designated a FF or FB label to each laminar pattern such that the consistency of hierarchical relationships was maximized; however, the analysis did not account for lateral connections – i.e., connections between areas occupying the same hierarchical level14. Thus, it is unclear to what extent consistent hierarchical relationships govern the ‘ultra-dense’ mouse cortical graph in which almost all possible connections between visual areas have been shown to exist21. Such a dense network could lead to reciprocally connected pairs that exhibit FF laminar patterns in both directions being more frequent than in macaque19.A recent study, which analyzed laminar patterns of axonal projections throughout the mouse neocortex, generated a hierarchy of the cortical and thalamocortical networkHere, we investigated the degree to which visual areas fit into a consistent sequence of levels by examining anterogradely labeled interareal projection patterns. The results show that reciprocal connections on average adhere to an essential hierarchical rule: the more FF a pathway is in one direction, the more emphasized is the FB nature of the reciprocal pathway. By employing a regression model to determine hierarchical level and distance values, we show that while the network exhibits hierarchical features, it is characterized by numerous lateral connections, and is best described as comprising five overlapping levels. Single-unit recordings of RF sizes in different areas further show that the structural hierarchy is consistent with the physiological hierarchy.22. To evaluate these patterns, we injected the anterograde tracer BDA into V1 and 9 of the 10 higher visual areas that have been previously identified through topographic mapping of projections from V124, and whose borders have been identified through anatomical and molecular landmarks:25 LM , AL , RL , P (posterior), LI (laterointermediate), PM , AM , A (anterior), POR , and PORa (anterior POR) . In order to minimize BDA uptake by broken fibers of passage, and to ensure labeling of neurons in all six layers, iontophoretic injections with fine pipettes were performed through the depth of cortex. Laminar patterns of axonal projections in the nine target areas were examined in the coronal plane. Coronal sections were numbered beginning from the posterior pole of occipital cortex, and projections to individual areas were identified by their locations relative to landmarks formed by bisbenzimide labeled, callosally projecting neurons as well as by their locations relative to each other  Fig. . Each arher Fig. .Fig. 1IdCallosal projection landmarks were observed in situ and identified in coronal sections. A band of callosally projecting neurons determined the boundary between V1 and an acallosal zone on the lateral side that included areas LM, LI, and AL Fig. , inset. 19.An example of an injection into area AM is illustrated in Fig. A critical feature of a cortical hierarchy is the presence of identifiable FF and FB pathways. To identify the respective anatomical signatures of FF and FB pathways, we first focused on the termination patterns of projections emanating from, and terminating in, V1. As the primary geniculo-cortical target, V1 can be regarded as the lowest area of the visual cortical hierarchy; accordingly, projections originating in V1 and terminating in the other areas were classified as FF projections, whereas the respective reciprocal connections descending to V1 were regarded as FB. V1 projections to higher areas terminated in a column with relatively sparse terminations in L1 Fig. . In cont26  of pathways, we measured the optical density ratio (ODR), defined here as the ratio of the optical density of labeled axons in L2-4 to that in L1 + L2-4, for each connection. This ratio was chosen because (i) the density of projecting axons in L2-4, relative to that in L1, provided a clear distinction between FF and FB pathways, evident in the projections to and from V1 Fig. ; and . To obtain a quantitative measure of hierarchical distance between any two areas, a logit transformation of the ODRs was performed so that the ratios were mapped from a  interval onto the real number line   indicating that the more FF a pathway is in one direction, the more FB the reciprocal connection is on average in the opposite direction, implying an ordered arrangement of areas. A similar negative association was observed for the raw ODRs without the logit transformation  = 14.30, p < 10−15; 95% conf. int.: ). The plot demonstrates that hierarchical distances  are predictive of laminar projection patterns.We next used a beta regression model to estimate hierarchical levels for each area that best predicted the ODRs for each interareal pathway. Area V1 was set at level 0, and the model estimated the values for each area such that the difference between the hierarchical levels of any two areas best matched the logit ODR for the connection linking them. Figure 27, which identifies the model with the best predictive power for the inclusion of new data. To generate the various models, a cumulative procedure was first employed in which the highest n areas (n going from 1 to 9), when arranged in the increasing order of their hierarchical levels from V1 to POR , resulting in hierarchical models comprising fewer than 10 levels. A beta regression fit was performed for each such model, and the best model was assessed as the one with the lowest Akaike information criterion (AIC) valuePOR Fig. , were coPOR Fig. . This wap < 0.05, Wald test for multiple coefficients), and blue lines connect areas whose estimated level values lacked a significant difference; the latter were accordingly considered to indicate lateral connections because they interconnect areas that do not occupy different hierarchical levels. The results demonstrate that V1 and POR are significantly separated from all other visual areas in this network based on the beta regression model, whereas RL is the only area not hierarchically distant from LM , with mean ODRs of 0.57 ± 0.02, 0.46 ± 0.08, and 0.32 ± 0.11, respectively . Despite the significant differences in the means of the ODRs, the overlap in ODR values between FF, lateral, and FB pathways indicate the presence of a hierarchy in which a subset of areas interact with each other in a nonhierarchical fashion.We next examined the distribution of ODRs after classifying individual pathways into FF, lateral, and FB categories based on the hierarchical levels of the respective interconnected areal pair. FF, lateral, and FB connections were characterized by distinct distributions of ODR values .Because areas at increasingly higher levels may be expected to integrate inputs from an increasingly larger number of lower areas with higher cortical magnificationp < 0.001 for dorsal stream, p < 0.001 for ventral stream, one-way ANOVA), the increase was more pronounced in the ventral stream  of all the areas, significantly larger  and PM  and LM  had the smallest mean RF diameters. We next examined if the structural hierarchy was consistent with differences in RF diameters. Whereas in the majority of cases, RF size differences were consistent with relative hierarchical positioning, with higher areas exhibiting larger RFs, we also found several instances of areas at the same hierarchical level exhibiting different RF diameters  and longer latencies  in their spike response in awake mice. We further performed pairwise comparisons of RF areas and spike latencies to determine if areas at different hierarchical levels showed dissimilar physiological properties. Differences in RF sizes were consistent with hierarchical level differences in 11/15 cases . For time to first spike, again 11/15 pairs showed a consistent relationship with hierarchical ordering .To further probe the robustness of the ODR-derived hierarchy, we examined its consistency with findings of previous anatomical and physiological studies based on much larger datasets. A recent study examined physiological properties, including the spike latencies and RF sizes, in each of six visual areas of head-fixed awake mice33. We therefore asked whether analyzing the visual cortical system as two separate networks would result in a different hierarchical organization, and whether each stream exhibits a stronger hierarchical organization than the entire network of ten areas. For this purpose, we examined the hierarchical organization of the dorsal and ventral streams after the inclusion of only interareal connections within each stream. When connections between streams were eliminated, the negative correlation between the ODRs of reciprocal pathways was enhanced , indicating a stricter adherence to FF/FB relationships within streams. The beta regression model was then used to construct the hierarchy of areas without cross-stream connections  = 14.52, p < 10−15; 95% conf. int.: ). The best model was once again assessed using the AIC value. Here, a model comprising five levels – V1, RL/LM, AL/A/PM, AM/P, and LI/POR – had the lowest AIC value , a distance measure that has been used to construct the primate cortical hierarchy33 and to provide different temporal constraints to visual computations54. Here we show that the mouse ventral stream area LM and dorsal stream area RL reside at low levels in the hierarchy, with POR and AM at the highest levels of each stream. While dorsal stream areas AL, RL, and PM are all involved in processing optic flow information57, they appear to underlie distinct functions. RL has been shown to be specialized for optic flow signals inherited from the retina58 and for the encoding of proximal visual stimuli in the binocular field59, whereas PM is not part of the specialized circuit that relies on direction selectivity produced in the retina58. RL and AL also form overall stronger connections than PM and AM with somatosensory areas, with PM and AM more strongly linked with cingulate and retrosplenial areas48. These findings suggest the existence of hierarchical substreams within the dorsal stream, analogous to that proposed in primate cortex:60 we speculate that RL and AL are part of a posterior parietal substream that guides heading behavior and locomotion, and which relies on information acquired from direction selective retinal cells, and that PM and AM are linked more strongly with a parieto-premotor/prefrontal pathway that underlies the parsing of externally generated object movement relative to optic flow during self-motion.The two visual processing streams are thought to underlie distinct functions10, and are thought to underlie skilled visuomotor functions and color perception in humans61. These observations support the notion that nonhierarchical circuits expand the role of the visual cortex in mediating higher-order functions62.Eliminating connections between the two streams enhanced the negative association between the ODRs of FF and FB pathways Fig. , consist41, the mouse pulvinar is also likely a critical component of signal processing in POR. POR was recently shown to be primarily activated not through the visual cortical hierarchy, but rather almost exclusively from signals transmitted through the collicular-pulvino-cortical pathway63, further indicating that V1 is not the exclusive gateway into the cortical hierarchy, but that retinal signals can enter the cortical network at higher processing stages via difference sources including the pulvinar. Furthermore, because hierarchical relationships are often correlated with other functional motifs, including response properties29 and the balance between excitatory and inhibitory input64, deducing the algorithms underlying hierarchical processing requires a detailed examination of thalamocortical and cortico-cortical connectivity at a cellular and dendritic resolution, as well as of the most efficient direct and indirect paths between areas for signal transmission across the network65.While our focus here was the intracortical visual network, the essential role of the thalamus in regulating visual signals must be considered in the description of cortical function. In addition to being involved in controlling interareal communicationAll experimental procedures were approved by the Institutional Animal Care and Use Committee at Washington University in St. Louis (protocol no. 20190094).In all, 6–16-weeks-old C57BL/6J male and female mice were used for the analyses of interareal axonal projection patterns. The in vivo single-unit recordings were performed in 5–8-weeks-old C57BL/6J male and female mice.−1/13 mg kg−1, IP), and thereafter secured in a head holder. To retrogradely label callosally projecting neurons for areal identification in the left hemisphere, 30–40 pressure injections  of bisbenzimide  were made into the right hemisphere  using glass pipettes (20–25 µm tip diameter). Interareal connections within the left hemisphere were anterogradely labeled by inserting a glass pipette (15–20 µm tip diameter) into one of ten cortical areas and performing iontophoretic injections  of biotinylated dextran amine . Injections of BDA were performed stereotaxically and at two depths (0.3 mm and 0.5 mm) below the pial surface. The stereotaxic coordinate system had its origin at the intersection of the midline and a perpendicular line drawn from the anterior edge of the transverse sinus at the posterior pole of the occipital cortex. The coordinates of the injected areas were : V1, 1.1/2.8; LM, 1.4/4.0; AL, 2.4/3.7; RL, 2.8/3.3; PM, 1.9/1.6; P, 1.0/4.2; A, 3.4/2.4; LI, 1.45/4.2; AM, 3.0/1.7; POR, 1.15/4.3. Post-surgery analgesia was provided by subcutaneous injections of buprenorphine (0.05 mg kg−1).A single injection of anterograde tracer was performed in each animal. Each mouse was anesthetized using a mixture of ketamine and xylazine  and perfused through the heart with heparinized (0.01%) phosphate buffer , followed by perfusion of 4% paraformaldehyde . Brains were removed, postfixed , and equilibrated in sucrose . To confirm the location of the injection site and to enable the identification of areal targets of axonal projections, bisbenzimide labeled neuronal landmarks in the left hemisphere were imaged in situ under a stereomicroscope (Leica MZ16F) equipped for UV fluorescence (excitation/barrier 360 nm/420 nm). The left hemisphere was then cut in the coronal plane on a freezing microtome at 40 µm. Sections were recorded as a complete series across the caudo-rostral extent of the hemisphere, and each wet-mounted section (in 0.1 M PB) was imaged under UV illumination using a fluorescence microscope equipped with a CCD camera  and MetaMorph software (Molecular Devices). BDA labeled fibers were visualized by incubating the sections in avidin and biotinylated HRP (Vectastain ABC Elite), and enzymatically reacted in the presence of diaminobenzidine and H2O2. Sections were mounted onto glass slides and cleared with xylene. The reaction product was intensified with AgNO3 and HAuCl221, coverslipped in DPX, and imaged under dark-field optics. Layers were assigned by the size and density of pale silhouettes of cell bodies whose distribution resembled the patterns seen in contrast-inverted Nissl stained sections.At least 3 days after BDA injections, the mice were overdosed with ketamine/xylazine (500 mg kgFor single-unit recordings of receptive fields (RFs), mice were first anesthetized with urethane . Tungsten microelectrodes dipped in DiI (5% in absolute ethanol) were inserted into each of the 10 visual areas in the left hemisphere, guided by stereotaxic coordinates. Recording depth was measured from the pial surface, and electrode insertion was controlled and monitored using a micromanipulator (Sutter Instruments). Recordings of spiking activity were acquired from L2/3 neurons using the TEMPO software , and single units were isolated through the use of a digital spike discriminator (FHC Inc.). Recorded signals were amplified and bandpass filtered at 300–5000 Hz.RF location was first determined by moving a light bar over a dark background of the monitor screen, and listening to the audiomonitor response to spike discharges. To measure RF sizes, a circular patch (5° diameter) of a drifting sinusoidal grating (0.03 c/deg) was presented at multiple locations on the display screen. Spatial response plots were generated from contour lines connecting points in visual space with similar mean response strengths to visual stimuli. The response strength for a neuron was measured as the mean firing rate during the 2-s stimulus. For each recorded neuron, the response field was fit with a Gaussian, and the RF diameter was computed from the contour corresponding to two standard deviations (SDs) of the fitted Gaussian after transforming the elliptical field into a circle. Average RF diameters are presented as means ± standard error of the mean.43. The flattened cortices were sectioned at 40 µm in the tangential plane on a freezing microtome. The sections were washed with 0.1 M PB, treated with 0.1% Triton X-100 and normal goat serum (10% NGS in PBS), and incubated with an antibody against the M2 muscarinic acetylcholine receptor  for at least 24 h. Next, sections were washed with 0.1 M PB, and treated with a secondary antibody labeled with Alexa Fluor 647 . Sections mounted on glass slides were imaged with a CCD camera (Photometrics CoolSNAP EZ), and the location of the recording site marked with DiI was compared with the M2 staining pattern. The areal location of the recording site was assessed using published maps based on M2 expression43.After each recording, mice were perfused through the heart with heparinized PB followed by 1% PFA (see Histology for details). The left cortical hemisphere was separated from the rest of the brain, flat-mounted, postfixed in 4% PFA, and cryoprotected in 30% sucrose23, and based on their locations relative to other projection sites. BDA labeled projection fields were then superimposed over the pattern of bisbenzimide labeled callosal patterns in the same section, and axonal terminations were assigned to specific areas according to previously published maps23.Two animals were used for the injection of each area; thus a total of 20 injections (one injection per mouse) were performed. For each injected animal, 3–5 adjacent coronal sections containing anterogradely BDA labeled projections in each target area were used for quantification of termination patterns. Occasionally, the terminal projections contained retrogradely labeled cells. Such projections were excluded from analysis. Projections were assigned to areas by their location relative to bisbenzimide-labeled callosal landmarksFor analyses of the termination pattern in each area, each coronal section was imaged under dark-field illumination at ×10 magnification, after which grayscale images were recorded. To quantify the laminar termination patterns formed by each interareal pathway, pixel intensity values from layer 1 (L1) and from layers 2 to 4 (L2-4) were averaged to obtain the ratio of the optical density of labeled axons in L2-4 to that of the total axonal projections in L1 + L2-4. This ratio, referred to as the ODR, was measured in each coronal section, and averaged for each pathway. Pixel intensity values were obtained from the grayscale images using a custom-written MATLAB script. Regions in L1 and L2-4 that lacked labeled axons were used to calculate background intensities in each section. These background intensity values were respectively subtracted from all pixel values in L1 and L2-4. To avoid oversampling of background regions, we only selected pixels within the top 70% of the highest intensity value in each section in Figs. δij between any two areas i and j, which we define as the difference between the respective hierarchical level values (hi and hj) of the two areas. Thus,We evaluated the consistency of hierarchical relationships across the network of visual areas by identifying a hierarchical distance measure that would be independent of the injection site and target areas  for the respective hierarchical distances between the two injected areas and the shared target area i. Accordingly, the y-intercept in each panel of Fig. Therefore,67. The regression analysis used here is an adaptation of the previously reported model that used discrete counts of retrogradely labeled cells instead of the continuous measure of optical densities of anterogradely labeled fibers used here for quantifying hierarchical relationships19.Statistical tests for generating and analyzing the cortical hierarchy were performed in the Open Source software R66, the distribution is reparameterized in terms of the mean, We used beta regression to estimate hierarchical distance values between a pair of areas that would best predict the ODR for pathways connecting the two areas. The beta distribution is a continuous probability distribution defined on the interval  typically parameterized by two shape parameters, betareg function in the betareg package (ver. 3.1-4)66.To fit a hierarchical model, we estimate the expected value of the ODR and set the logit transformation of the ODR equal to the hierarchical distance between two areas. This defines a relation between a linear predictor and the ODR27 was used to assess the number of levels that best described the hierarchy. The AIC is defined as minus twice the log likelihood plus twice the number of parameters estimated. In the large sample limit, it approximates the same result as the leave-out-one cross-validation score, thus giving a measure related to prediction error. The model with the lowest AIC will yield the best balance of goodness of fit and model complexity and among a set of models is expected to best predict new data sets. The AIC values for the different models (see Results section) were obtained using standard methods in R.The Akaike information criterion (AIC)Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Mice carrying targeted mutations are important for investigating gene function and the role of genes in disease, but off-target mutagenic effects associated with the processes of generating targeted alleles, for instance using Crispr, and culturing embryonic stem cells, offer opportunities for spontaneous mutations to arise. Identifying spontaneous mutations relies on the detection of phenotypes segregating independently of targeted alleles, and having a broad estimate of the level of mutations generated by intensive breeding programmes is difficult given that many phenotypes are easy to miss if not specifically looked for. Here we present data from a large, targeted knockout programme in which mice were analysed through a phenotyping pipeline. Such spontaneous mutations segregating within mutant lines may confound phenotypic analyses, highlighting the importance of record-keeping and maintaining correct pedigrees.Twenty-five lines out of 1311 displayed different deafness phenotypes that did not segregate with the targeted allele. We observed a variety of phenotypes by Auditory Brainstem Response (ABR) and behavioural assessment and isolated eight lines showing early-onset severe progressive hearing loss, later-onset progressive hearing loss, low frequency hearing loss, or complete deafness, with vestibular dysfunction. The causative mutations identified include deletions, insertions, and point mutations, some of which involve new genes not previously associated with deafness while others are new alleles of genes known to underlie hearing loss. Two of the latter show a phenotype much reduced in severity compared to other mutant alleles of the same gene. We investigated the ES cells from which these lines were derived and determined that only one of the 8 mutations could have arisen in the ES cell, and in that case, only after targeting. Instead, most of the non-segregating mutations appear to have occurred during breeding of mutant mice. In one case, the mutation arose within the wildtype colony used for expanding mutant lines.Our data show that spontaneous mutations with observable effects on phenotype are a common side effect of intensive breeding programmes, including those underlying targeted mutation programmes. Such spontaneous mutations segregating within mutant lines may confound phenotypic analyses, highlighting the importance of record-keeping and maintaining correct pedigrees.The online version contains supplementary material available at 10.1186/s12915-022-01257-8. Manipulating embryonic stem (ES) cells to insert or alter DNA to study the effect of targeted genes in the resulting organism is a widely practised technique, and the creation and characterisation of mutant organisms are key steps in exploring gene function. The advent of the CRISPR-Cas9 system has prompted exploration of its potential unwanted off-target mutagenic effects, which are of particular concern for its use as a therapeutic tool. While some studies have reported that off-target effects are “minimal and manageable” , 2, withThe Sanger Institute Mouse Genetics Project was a large-scale programme that generated and screened mice carrying knockdown alleles created by the KOMP (Knock Out Mouse Programme) and EUCOMM  programmes –9. Each S1pr2stdf, has already been described [From our routine ABR screening of mice carrying targeted knockout alleles, we identified twenty-two lines where a hearing impairment phenotype did not segregate with the targeted allele Fig. , Table 1escribed . We thenlowf)  (ENSMUST00000068025) Wtsi targeted allele [n = 15) were homozygous for the Klhl18lowf allele, while unaffected littermates (n = 8) were heterozygous or wildtype. We were not able to obtain DNA samples from affected mice of the remaining two lines . Further characterisation of the unusual Klhl18lowf phenotype is described in [Mice homozygous for this mutation displayed the unusual phenotype of low frequency hearing loss (wf) Fig. a. We mapwf) Fig. : Fig. S1 protein  and the  protein  Fig. 2ddlowf  mutation was mapped to a 5.2 Mb region on chromosome 6  (ENSMUST00000101045)  allele was observed to cause male sterility; no pregnancies or pups were obtained from twelve different affected males, paired in matings for at least 66 days. Affected females were fully fertile. We mapped the mutation to a 7 Mb region on chromosome 10  data from the gEAR database [Map3k5 showed low expression in most cell types of the cochlear duct, Map7 was expressed in multiple cell types, in particular the outer hair cells and outer pillar cells, increasing over time in both. Map7 is also expressed in the lateral wall, although not as strongly  allele exhibited circling and head bobbing behaviour and had no response to any stimulus up to 95 dB, the maximum sound output of our equipment  (ENSMUST00000232335)  allele was mapped to a 26.9 Mb region on chromosome 10 , 22 of which contain the affected exons. The deletion results in the loss of the 3’ end of the coding sequence for eleven of those transcripts, and in the loss of internal exons for the remaining eleven  allele exhibited complete deafness , four lncRNA genes, one miRNA and one snRNA . There were no gross malformations of the ossicles and inner ear . Mice homozygous for this allele displayed a rapid reduction in endocochlear potential (EP) between P14 and P56 which correlated with the progression of their hearing loss, while hair cell degeneration followed at a later age [In addition to the above seven lines, mice from three other lines displaying nonsegregating phenotypes were sequenced . Because of this variability in ABR thresholds, we did not restrict the zygosity of identified variants during variant processing , we found that the mutations were not present in the parental ES cell lines , MHER (spdz), MEEK (rhme), MDLY (ttch) and MEWY (jigl)). In the MAKN (stdf) line, the mutation must have arisen no less than three generations before the phenotype was observed  line, the mutation must have been present in the chimaeric offspring of the microinjected founder , we examined the pedigrees of the mice originally found to have these phenotypes and the mice used as founders for our eight breeding colonies. For each line, we identified the latest possible point at which the mutant allele could have arisen, assuming that it only occurred once. In five lines, the mutation could have arisen just two generations before it was observed (MEBJ (nt lines .rthm) and therefore arose when the ES cells were targeted or during the ES cell processing prior to microinjection, and one is likely to have arisen spontaneously in a wildtype colony . The remaining six mutations could have occurred either during ES cell targeting and processing or during breeding of the colony carrying a targeted allele , Atp2b2Tkh (MEBJ), Table Ctxn3, Ccdc192, Map3k5 and Map7) within the rthm and rhme deletions in the MFFD and MEEK lines, and more study is required to identify which gene is responsible for the hearing phenotype or whether, in the case of the rthm deletion, it is one of the noncoding RNA genes that underlies the deafness and vestibular dysfunction in this line. For the rhme deletion, existing expression data and the associated male infertility support Map7 as the best candidate gene. Full analysis of these candidates, especially the noncoding RNA genes in the rthm deletion, is outside the scope of this study, which aimed to describe the spontaneous mutations arising within the Mouse Genetics Project and to identify their likely origin.Here we have described seven mutant alleles affecting hearing which, along with the previously described mutation , arose a [Klhl18 , 11, S1p8 [S1pr2 , 57–59,  [Atp2b2 , 31–36, b2 [Tbx1 , 60, 61, [Pcdh15  and Espn15 [Espn ), two ofttch) mice: a missense mutation in Muc13 and a deletion in the Kabuki syndrome gene Kmt2d , neither of which affected hearing nor had any obvious effect on homozygotes , although since we can only identify the latest possible time of occurrence from the pedigrees, we cannot rule out the possibility that the other mutations occurred within the targeted ES cell. In addition, the presence of the Klhl18lowf mutation in multiple lines suggests that it arose within the wildtype colony used for expansion of the mutant lines  line  with Datesand Aspen bedding, with up to 5 adult mice of a single sex in each cage. Extra nesting material and cardboard tubes were provided for environmental enrichment. The temperature and humidity were controlled , and a 12-h light/dark cycle was maintained throughout the study. The mice had free access to water and food  and were checked daily for signs of ill health.Klhl18tm1a(KOMP)Wtsi [Tbx1tm1Bld [Mice carrying knockout first conditional-ready alleles were observed for gross behavioural abnormalities using a modified SHIRPA  test at 9 weeks old, and tested by ABR at 14 weeks old, as described in . Mice asx1tm1Bld .n numbers for each experiment are given in the figure legends and always refer to the number of mice .Affected mice were compared to unaffected littermate controls of the same age and, where possible, sex, although not to the exclusion of mice of the required phenotype available. Randomisation is not appropriate for experiments with paired mutant and littermate controls. Samples younger than P14 were collected prior to genotyping, effectively blinding the collection. No other blinding was carried out. Sample sizes were calculated using the power calculator at dssresearch.com along with data from previous experiments of the same kind, with a 5% significance level in all cases. Individual power calculations and exclusion criteria are listed under each relevant method; if no exclusion criteria are listed, all experimental mice were included. The ABR tests were carried out as previously described , 66, 67.Power calculation: Six animals per genotype are required for 98.4% power to detect a meaningful effect size of 20 dB given a standard deviation of 8.45 dB.Exclusion criteria: If a mouse showed evidence of poor physiological condition, such as a reduced heartbeat, during ABR recording, recording was stopped, and the data from that session was not included. This is standard procedure and thus pre-established.Mouse behaviour was observed in their home cage for signs of circling or headbobbing. To detect balance dysfunction, we also used the contact righting reflex test. Mice were placed in a large Petri dish sized such that the back of the mouse was in contact with the dish lid but not under pressure. The Petri dish was inverted, and the mouse was monitored for 30 s. If it did not turn itself over within that time, it was marked as affected, while if it turned itself over promptly, it was marked as unaffected. Some mice righted themselves after a delay and were tested a second time.Exclusion criteria: If the results of the righting test were unclear, the mouse was not used for mapping.Atp2b2Tkh), the outcross offspring were backcrossed to C3HeB/FeJ wildtype mice. Backcross offspring were assessed by ABR or contact righting reflex, then after culling, a tissue sample was collected for DNA extraction. The initial genome scan was carried out using a standard marker panel . Genomic DNA (approximately 1 ug) was fragmented to an average size of 150 bp (WTSI) or 180–280 bp (Novogene) and subjected to DNA library creation using the Agilent SureSelect Mouse All Exon Kit. Adapter-ligated libraries were amplified and indexed via PCR. Enriched libraries were subjected to 75 bp paired-end sequencing , with the exception of the spdz libraries, which were subjected to 150 bp paired-end sequencing  following the manufacturer’s instructions. Sequences were aligned to GRCm38 using bwa [spdz fastq files, which were checked and processed using FastQC [Two affected mice of each line were selected for exome sequencing. DNA was extracted using phenol and chloroform, and exome sequencing was carried out by the Wellcome Trust Sanger Institute (WTSI), with the exception of the sing bwa , with thg FastQC  and Trimg FastQC , then alg FastQC . All bamg FastQC  using GAg FastQC  . Of the ten candidate genes , only Ctxn3, Map7 and Map3k5 had sufficient expression data in the gEAR.To investigate candidate gene expression, we used single-cell RNAseq data from the mouse inner ear at E16, P1, P7 , 87, P15Pcdhjigl and Espnspdz mice respectively). Images of the organ of Corti were taken at roughly 20% intervals along the cochlear duct and the macula of the utricle and saccule and crista of the ampullae were also imaged. Whole images were adjusted in Photoshop to normalise dynamic range across all panels.The temporal bones were isolated. The inner ears were dissected out and fixed by 2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer with 3mM calcium chloride at room temperature for 3 h. Cochleae and the vestibular system were finely dissected in PBS. This was followed by further processing using an osmium-thiocarbohydrazide-osmium (OTOTO) method . The samExclusion criteria: If dissection damage was too great to observe hair cells in either cochlea, the mouse was not counted. This is standard procedure and thus pre-established.After culling, the ear canals were checked for cerumen, then the mouse was decapitated and the bulla, tympanic membrane and middle ear cavity inspected for any abnormalities, including the presence of fluid, inflammation or any other obstructions in the middle ear. Observations were recorded on a standard tick sheet. Ossicles were dissected out and stored in 10% formalin. The inner ear was removed and fixed in Bodian’s fixative , washed in water and 70% ethanol, then cleared by gentle rotation in 3% KOH for 3 days, changed daily. Samples were then placed in G:E:B  for the final stage of clearing, then stored in G:E . Images of ossicles and middle ears were taken using a Leica stereomicroscope with a Leica DFC490 camera. To carry out microCT scans, cochleae were immobilised using cotton gauze and scanned with a Scanco microCT 50 to produce 14μm voxel size volumes, using an X-ray tube voltage of 80kVp and a tube current of 80μA. An aluminium filter (0.05 mm) was used to adjust the energy distribution of the X-ray source. To ensure scan consistency, a calibration phantom of known geometry (a dense cylinder) was positioned within the field of acquisition for each scan. Test reconstructions on this object were carried out to determine the optimum conditions for reconstruction, ensuring consistency in image quality, and minimising blurring. Reconstruction of the cochlea was performed in Thermo Scientific Amira software.Samples from adult mice were collected, fixed in 10% formalin, decalcified in 0.1 M Ethylenediaminetetraacetic acid (EDTA), embedded in paraffin wax and cut into 8μm sections. Samples from P4 pups were treated similarly, but no decalcification step was needed. For histological analysis, slides were stained using a trichrome stain, containing Alcian blue, Sirius red and Haematoxylin. Immunohistochemistry was carried out using a Ventana Discovery machine and reagents according to the manufacturer’s instructions (DABMap™ Kit (cat.no 760–124), Haematoxylin (cat.no 760–2021), Bluing reagent (cat.no 760–2037), CC1 (cat.no 950–124), EZPrep (cat.no 950–100), LCS (cat.no 650–010), RiboWash (cat.no 760–105), Reaction Buffer (cat.no 95–300) and RiboCC (cat.no 760–107)). Primary antibodies used were rabbit anti-PMCA2 , rabbit anti-MUC13  and rabbit anti-MYO7A , and the secondary antibody was anti-rabbit . All antibodies were validated by the manufacturer and/or had been successfully used for immunohistochemistry on paraffin-embedded sections of mouse tissue in previous studies , 97. AntEspnspdz mice were excluded for this reason and have not been counted in the total assessed.Exclusion criteria: If damage which occurred during sectioning and staining was too great to observe hair cells, the sample was not counted. This is standard procedure and thus pre-established. Only one set of sections from a pair of Espnspdz (P25) and rthm (P28) mice, which were snap-frozen in liquid nitrogen, and the organs of Corti of 4-day-old (P4) Atp2b2Tkh mice, which were dissected out and stored at − 20 °C in RNAlater stabilisation reagent (Ambion). All RNA dissections were carried out during a fixed time window to avoid circadian variation . For the brains, RNA was extracted using TRIzol, in some cases followed by processing through Direct-zol minipreps . For the organs of Corti, RNA was extracted using either QIAshredder columns  and the RNeasy mini kit , or the Lexogen SPLIT kit , following the manufacturer’s instructions. RNA concentration was measured using a nanodrop spectrophotometer (ND-8000). RNA was normalised to the same concentration within each litter, then treated with DNAse 1  before cDNA creation. cDNA was made using Superscript II Reverse Transcriptase  or Precision Reverse Transcription Premix . Primers for sequencing cDNA for testing Espn splicing in Espnspdz mice were designed using Primer3 [rthm and Tkh mice was carried out on a CFX Connect qPCR machine (Bio-Rad), using probes from Applied Biosystems  and Sso-Advanced Master Mix . Relative expression levels were calculated using the 2−ΔΔct equation [Hprt as an internal control. Jag1 was used to check for the quantity of sensory tissue present in the organ of Corti samples because it is expressed in supporting cells [−ddCt calculation, there are always unequal variances between wildtype and mutant groups, and we therefore chose suitable statistical tests; the Wilcoxon rank sum test  [rthm wildtypes and homozygotes, and a Welch’s one-way ANOVA [Atp2b2Tkh qPCR, which involved three groups .We collected the brains of adult  Primer3    for compay ANOVA  for the −ΔΔct calculation of relative expression levels). The power is 99.1%.We estimated the power to detect a difference of 40% for a sample size of 4 wildtypes and 4 homozygotes, with a standard deviation of 0.01 for wildtypes and 0.2 for homozygotes, which is based on previous data (the discrepancy in standard deviation is the result of the 2Atp2b2Tkh organs of Corti where the Jag1 levels differed by more than 20% between the samples were not included. This is a pre-established criterion based on our previous work.Data from Additional file 1.Additional file 2.Additional file 3.


Knowledge regarding genetic diversity and population structure of breeding materials is essential for crop improvement. The Texas A&M University Potato Breeding Program has a collection of advanced clones selected and maintained in-vitro over a 40-year period. Little is known about its genetic makeup and usefulness for the current breeding program. In this study, 214 potato clones were genotyped with the Infinium Illumina 22 K V3 Potato Array. After filtering, a total of 10,106 single nucleotide polymorphic (SNP) markers were used for analysis. Heterozygosity varied by SNP, with an overall average of 0.59. Three groups of tetraploid clones primarily based on potato market classes, were detected using STRUCTURE software and confirmed by discriminant analysis of principal components.The highest coefficient of differentiation observed between the groups was 0.14. Signatures of selection were uncovered in genes controlling potato flesh and skin color, length of plant cycle and tuberization, and carbohydrate metabolism. A core set of 43 clones was obtained using Core Hunter 3 to develop a sub-collection that retains similar genetic diversity as the whole population, minimize redundancies, and facilitates long-term conservation of genetic resources. The comprehensive molecular characterization of our breeding clone bank collection contributes to understanding the genetic diversity of existing potato resources. This analysis could be applied to other breeding programs and assist in the selection of parents, fingerprinting, protection, and management of the breeding collections. Solanum tuberosum L.) is the world’s fourth most important crop after maize, rice, and wheat1. Worldwide, over one billion people consume potatoes as a staple food2. Potatoes, the leading vegetable crop in the United States, are grown commercially in 30 states. Idaho grows more potatoes than any other state, followed by Washington, North Dakota, Wisconsin, and Colorado2. Even though potato production in Texas is comparatively lower , growers can harvest and provide fresh potatoes to the market earlier in the growing season than other states, and often receive two to three times higher prices than growers from Northern States2.Potato , late blight (Phytophthora infestans), potato psyllid , high-temperature tolerance, cold sweetening resistance, health and nutritional properties, and broad adaptability. The TAMU Potato Breeding program has developed/co-developed and released 17 cultivars, including clonal selections. Some of them make up a substantial and increasing share of the regional/national potato production and have become important contributors to the economies of several states. Of all the cultivars released over the past 15 years by the 12 US potato breeding programs, those developed by the Texas program have ranked in the top four to five nationally in the total area approved for seed certification over the past several years3. This has been due, in large measure, to the popularity of the four Texas Russet Norkotah strains  with improved plant type to withstand environmental stresses. The Texas Russet Norkotah strains with increased vine vigor and some resistance to early dying (Verticillium wilt) are an outstanding early market alternative to the standard Russet Norkotah variety4.The Texas A&M University (TAMU) Potato Breeding Program was established by the late J. Creighton Miller Jr., Ph.D. in 1972 aiming to provide improved early maturing cultivars with high yield and quality that would enable Texas potato producers to remain viable and competitive, and to supply superior products to consumers. In recent years, the emphasis has been placed on increasing yield and quality in addition to disease/pest resistances like Potato Virus Y (PVY), nematodes  population was more genetically diverse, but less genetically sub structured than the Phureja (diploid) population10. Ellis et al.11 used the 12 K SNP array for fingerprinting and diversity analysis of the cultivated potato collection from the International Potato Center (CIP) in Peru and reported some genetic redundancies among individual accessions with some putative misclassified accessions. Recently, Igarashi et al.12 used the 12 K SNP array to characterize and compare 164 Japanese potatoes, including 70 breeding clones for chip processing with North American and European potatoes. Thus, the success of potato breeding depends on the understanding and use of the available gene pool of varieties and breeding clones. The Potato SNP array has been very useful for performing a robust and direct comparison of genetic diversity among different gene pools but has never been applied to the advanced clones selected over multiple years.Further, the genetic characterization of clone bank collections is essential to assess their diversity and population structure. The identification of suitable genotypes from the study could serve as a source of new alleles in potato breeding programs. Molecular markers have been used to test the genetic diversity of potatoes. Recent advances in the development of high‐throughput genotyping platforms together with whole-genome coverage and affordability have turned single nucleotide polymorphisms (SNPs) into one of the most promising tools for the investigation of genetic diversity. Several studies have implemented the Infinium Potato Array  for genetic diversity studies. The 8 K SNP array distinguished diverse North American varieties based mainly on market classes13. Different statistical approaches have been developed to identify selection footprints. According to Vitti et al.14, they are of three main types: (a) measures based on the allelic frequencies , (b) measures based on the differentiation between and within species/groups , and (c) within population/groups measures based on extended haplotype homozygosity . These methods have been applied to several crops, including wheat15, oat16, maize17, rice18, tomato19, and potato20. The PCAdapt method tests how much each variant is associated with population structure, assuming that outlier variants are indicative of local adaptation. It does not need grouping of individuals into populations and can handle admixed individuals21. The iHS approach measures the amount of extended haplotype homozygosity (EHH) for a given SNP within-population whereas XP-EHH compares the extended haplotype homozygosity between two populations22. Recent selection events in which haplotypes have almost or fully risen to fixation are detected by iHS and XP-EHH statistics22. Thus, methods for detecting evidence of selection provides a mechanism for highlighting genomic regions which are often associated with functional traits.Further, with the availability of high-density genotype data, it is possible to identify regions of the genome that provide evidence of selective pressure commonly known as signatures of selectionThe goal of this research was to investigate potato varieties and advanced clones of the TAMU Potato Breeding Program (entered in the clone bank over 40 years of breeding) at the molecular level to assess genetic diversity for further genetic enhancement of important economic traits. In this study 214 TAMU potato clones were genotyped using 22 K SNP markers to (a) examine the genetic diversity and the population structure in the TAMU Potato Breeding clone bank collection, (b) to identify candidate loci under selection, (c) identify a “core set” to better manage the clone bank collection, and (d) check the accuracy of pedigree records of the clones.Two hundred fourteen clones, including commercial and reference varieties maintained by the TAMU Potato Breeding Program, were genotyped with the Infinium 22 K V3 Potato Array. Stringent screening of the SNP markers using MAF in removed 10,669 SNP (50.7%) markers and additional filtering for more than 10% “No call rate” removed 252 (1.2%) SNP markers. After filtering, a total of 10,106 polymorphic SNP markers were selected for analysis  were mapped to 12 chromosomes represented as the 12 pseudomolecules of the potato genome DMv4.03The mean expected heterozygosity value of the SNP markers was 0.39, ranging from 0.10 to 0.50. Minor allele frequency (MAF) ranged from 0.05 to 0.50, with a mean of 0.31 Fig. . The polBased on the diploid genotypic calls analysis using TASSEL, the average pairwise divergence among genotypes (π), at SNP locations, was 0.39. This represents the nucleotide diversity per assayed SNP in the clones. The expected number of polymorphic sites per nucleotide (θ), which estimates the mutation rate in the collection, was 0.169 with 10,106 segregating sites. Tajima’s D, which estimates the normalized measure of the difference between the observed (π) and expected (θ) nucleotide diversity was 4.29.STRUCTURE analysis showed that the number of subpopulations (K) ranged from zero to ten when using the diploid genotyping model . The K value with the maximum likelihood was K = 3 Fig. . Clones find.clusters function was three  value obtained using The dendrogram generated using Nei genetic distance and hierarchical clustering also revealed the presence of three clusters in the population Fig. . The assUnder the current naming convention, the Texas Potato Breeding program typically uses a clone code that provides information about the place where the cross was made, where it was selected, year the cross was made, family number, selection number, type of skin, and sometimes type of flesh and other characteristics. For example, in ATX91137-1Ru, ‘A’ cross made in Aberdeen, Idaho, TX = selected in Texas, 91 = year cross was made, 137 = family number, -1 = selection number, and Ru = russet skin. After inspecting the dendrogram, we observed mislabeling in 5 clones . For instance, a russet clone AOTX98096 − 1Ru was mislabeled as a red clone AOTX98096 − 1R. Likewise, COTX04303-3Ru/Y was mislabeled as COTX04303-3R/Y. The SNPs grouped both of them with russets and Russet Norkotah strains in the dendrogram. Inspection of parentage gave a hint about the error and the minitubers produced in the greenhouse further confirmed that these should be russet clones  gene25. Likewise, after adopting the false discovery rate of 0.01, 127 SNPs  had PIC values ranging from 0.30 to 0.38, while for the remaining percentage, it was < 0.30. The SNPs having PIC values ranging from 0.25 to 0.5 are considered moderately informative33. This may support the idea that breeding efforts, genetic diversity in our set of clones has not been narrowed. Similar PIC values were previously reported in collections of potatoes tested for genetic diversity using the SolCap SNP array34.The availability of SNP arrays has enabled germplasm genotyping of crops like potatoes. SNP distribution across the genome assessed by analyzing filtered SNP density shows the typical pattern of distinctly reduced recombination in pericentromeric regions and increased varying recombination rates in euchromatic regions for all chromosomes. Larger regions with no SNP coverage are usually found in large pericentromeric regions, where repetitive DNA makes it difficult to distinguish unique flanking regions around SNPs35. Potato is an outcrossing species; thus, heterozygosity is usually higher than expected. Selection, migration, mutation, hybridization, polyploidization, and introgression elucidate the high diversity of potatoes10. The average percent heterozygosity (59%) observed in the TAMU potato collection was similar to Hirsch et al.7 and Igarashi et al.12, who reported the average value of 56% and 60%, respectively. However, the lowest heterozygosity value we observed was 22% that of ATX91322-2Y/Y, which was found to be a diploid selected by the program based upon five cluster calling. Igarashi et al.12 reported that the average percent heterozygosity of the 2 × varieties (23%) was much lower than that of the 4 × genotypes. Five clusters genotypic calling has successfully been used to predict ploidy determinations of diploid, triploid, and tetraploid samples36. When the simplex and triplex frequency was close to zero, the sample was considered to be diploid, and when the frequency was over 0.20, then the progeny samples were considered to be tetraploid. This finding is similar to Hirsch et al.7, indicating that greater ploidy can be correlated with greater heterozygosity, and vice versa. Almost all the highly heterozygous clones had negative inbreeding coefficients, which happen when observed heterozygous clones are larger than expected due to an excess outcrossing. Increasing the heterozygosity of clones and widening their genetic base are important aspects of breeding programs to have desired combinations of abiotic and biotic stress tolerances and high yield. It is clear from the heterozygosity analyses and SNP evaluation, that the Texas A&M potato breeding program harbors considerable genetic diversity.Heterozygosity is an indicator of genetic variability in a population and it is related to the polymorphic nature at each locus. In this study, a high level of heterozygosity was observed; in the TAMU potato breeding collection, this could be due to the high levels of genetic variation at loci with vital significance for adaptive response to environmental changes. Loss of heterozygosity was related to lower fitness37 provides a distinction between randomly altering loci and non-randomly evolving loci arising from the directional selection, introgression, genetic bottleneck, and/or drift. Generally, a positive value of Tajima’s D arises from an excess of intermediate frequency alleles and can result from population bottlenecks, structure, and/or balancing selection38. These factors are likely present in potato breeding clones. The observed Tajima’s D value of 4.29 in this study is higher than in sorghum (0.30)39 and soybean (1.08)40, both of which show a significant bottleneck in their population history. To elucidate the possibility that the elevated D value occurred due to population subdivision, we assessed population structure as suggested by Pritchard et al.41. When SNPs from all chromosomes were included in the analysis, a significant subdivision was observed  existed among the strains and Russet Norkotah43. However, clonal selections (strains) were not differentiated genetically despite using more than 11,000 genome-wide SNP markers. Previous studies using Amplified Fragment Length Polymorphism (AFLP) and microsatellite marker were also unable to detect differences between intraclonal variants of the potato cultivar Russet Norkotah44. The differences between the strains could be due to epigenetic variation, and most of them may not be observed by SNPs45.The clustering of individuals gives interesting cues for increasing diversity in breeding programs and germplasm collections. Clear knowledge of the germplasm structure and clusters assists in parental choice in breeding programs, improving genetic diversity, and enhancing the potential gain from the selection. Both help to increase breeding program efficiency to face new demands from consumers and the industry, as well as new ecological issues like adaptation to climate change and pest resistances. In this study, clustering corresponded with the similarity in the genetic background of the clones. However, clustering was not found depending on the place where the original cross was done suggesting a huge gene flow across breeding programs due to the reciprocal exchange of true potato seed of unselected families and use in crossing blocks of parental germplasm from potato breeding programs throughout the United States. Bali et al.11 at the International Potato Center (CIP) found 4.4% of accessions were genetically mismatched, and in some cases, the SNP results identified the mixed accession. Barcoding, automatic data collection, curation, and other quality control strategies will help to minimize errors. Studies such as this can help identify and correct errors in the breeding program. In addition, SNP fingerprints and genetic distance comparisons can be useful for plant variety protection (PVP), as well as for the verification of the identity of clones in the foundation, certification, and breeding programs.Maintaining consistent and unique clone names in the clone bank is important for future cultivar identification, research, and breeding. There are many instances in the program where naming errors could be introduced. The longer a clone is handled in the program, the greater the potential of mixing or mislabeling. The Infinium 22 K V3 Potato SNP Array generated unique genetic fingerprints to identify accessions where errors had occurred. The majority (97.6%) of the clones evaluated had no errors in genetic identity. It is common in most gene banks to have some errors in the collection. Ellis et al.46. Potato breeding efforts currently center on improving different market classes such as chip and French fry processing, pigmented, table russets, and yellows47. Most  hybridizations are made between clones within a market class. Over time one might expect these market classes to diverge, not only in terms of the few traits that define each class, but also in terms of unlinked, selectively neutral DNA markers47. Several of the identified SNPs and sweep regions in this study are associated with functions of interest and warrant further investigation.Our main goal for selection signature analysis was to detect regions that show preferential selection in the genome of potatoes. To accomplish this, we used three different, but complementary, statistical methods: PCAdapt, iHS, and XP-EHH. The use of a combination of methods for selection sweep detection allows different emerging patterns of selection to be identified, and it also improves the reliability and accuracy of the analyses48 using genome-wide association mapping had also found a strong association for flesh color at 49.4 Mb on the same chromosome. Similarly, SNPs belonging to the sweep region detected by this study on chromosomes 1 and 9 (Table 48. The SNPs at the sweep region (10.58–11.08 Mb) on chromosome 7 were reported to have a significant association for the stolon attachment trait in potato48. In a previous QTL study, Manrique-Carpintero et al.35 identified candidate genes  in the photoperiod regulatory pathway associated with length of plant cycle and tuberization in the QTL region on chromosome 2 around 46 Mb. The XPEHH test from our study has also detected a sweep at the same location (46.15–46.65 Mb) on the same chromosome  detected by both iHS and PCAdapt test in our study matched with this finding. In the potato Sucrose transporter 4 (SUT4) gene involving an accumulation of sucrose and starch in the terminal sink, organs are located at 65.8 Mb on chromosome 451. Our XPEHH test between the ChipRu and Red groups selected three SNPs  at 65.9 Mb on the same chromosome. It is well known that Red potatoes have less starch and more sugars than Chip and Russet potatoes52. Parra-Galindo et al.53 reported the QTL AnthoX_Adeny, colocalized on chromosome 10 at 57.3 Mb (PGSC0003DMT400060833/Adenylyl-sulfate kinase gene), explaining 41.1% of the phenotypic variance of pelargonidin. As illustrated in a colored potato study, the red cultivars contained predominantly pelargonidin derivatives, while the purple/blue varieties had peonidin, petunidin, and malvidin as the main aglycones54. These results will allow a better understanding of the genetic architecture and will open avenues for studying candidates for biochemical and functional studies of admixed advanced potato selections.A SNP PotVar0120627) was selected by PCAdapt at 48.6 Mb on chromosome 3 controlling the white-to-yellow flesh color. Sharma et al.20627 was55. Nevertheless, all core germplasm sets do not have a fixed size, as different crops and targets require different sampling percentages. In earlier studies, a core set of 48 was defined for capturing the genetic diversity of a collection of 350 tetraploid cultivated potato varieties by using simple sequence repeats (SSR) data56. A core set of 27 genotypes was developed from 138 accessions of potato cultivars from the Western Highlands region of Cameroon using SSR markers57. Core Hunter software has been used for core set selection in earlier studies of different crops, including wheat58, Brazilian grapevine germplasm59, banana60, sweetpotato61, and common bean62. In many reports, genetic diversity and cluster analysis were used to evaluate the efficiency of the development of the core germplasm set. In the present study, the genetic distance increased as expected after the removal of genetically similar clones during core germplasm set development. Having a core collection as a backup of important genes and source of parents is a good idea. However, in reality, core collections may not meet the needs of modern breeding approaches, such as genomic selection and genome-wide association studies where more individuals are desired to increase the statistical detection power.Most plant breeders want to make better use of plant genetic resources in their breeding programs but have trouble maintaining many clones and prioritizing clones for parental selection. Some breeders define a subset of clones that reflect the greater collection. The core subset can also be maintained as a backup collection to preserve important genes. In this study, we proposed a core set of 43 potato clones, accounting for 20% of the total collection using CoreHunter software. A sampling percentage of 20 ~ 30% was suggested by Hintum et al.In conclusion, our analysis of the genetic diversity and population structure of the advanced clones in the TAMU Potato Breeding Program found a significant subdivision among clones, indicating a heterogeneous collection. Further, the SNP markers used in the study allowed the differentiation among breeding clones and the development of a core germplasm set of 43 clones, accounting for 20% of the total collection. Additionally, we used the SNP array to validate pedigree information. The genome-wide SNP characterization of these 214 clones, development of the core set, and reporting of the correct pedigree in this study will be useful for future genomic studies, parental selection, and germplasm management in potato breeding program.Two hundred fourteen potato clones were included in this study (Supplementary Information Table −1) were prepared.Genomic DNA was extracted from 50 to 80 mg of fresh young potato leaves from tissue culture plantlets using the DNeasy Plant Pro Kit . DNA quality was examined using 1% agarose gel 1X TBE (Tris–Borate and ethylenediaminetetraacetic acid) and staining with GelRed  using a V.U.V. transilluminator . Quantification of DNA was performed in a spectrophotometer . DNA concentration was verified using the Quant-iT PicoGreen dsDNA Assay Kit  and samples with uniform DNA concentration  for five-cluster  marker calling using a custom tetraploid cluster file based on the PolyGentrain polyploid module calling of reference tetraploid samples . The SNP genotype data were filtered to exclude low-quality, monomorphic SNPs, and loci with ≥ 10% missing data. Also, the alleles-design option was displayed in GenomeStudio to get genotypes in nucleotide format for STRUCTURE input. The genotyping data were transformed into diploid form as AAAA = AA, BBBB = BB, and AAAB, AABB, ABBB = AB to use in analysis packages which do not support polyploid data.Samples were assayed using the Infinium 22 K V3 Potato Array on the Illumina iScan  at Michigan State University. V3 Potato array includes the SNP from the Infinium 8303 Potato Array with additional markers from the Infinium high‐confidence SNPs (69 K)snpReady63 package in R using the diploid genotypic calls.SNP genotypic data were used to study genetic diversity and to understand the genetic relationship among clones. Allele frequencies, polymorphic information content (PIC), heterozygosity, and inbreeding coefficient were calculated in 64 using the default settings for the diploid genotypic calls. The normalized measure of the difference between the observed (π) and expected (θ) nucleotide diversity, known as Tajima’s D, was also computed in TASSEL.The average pairwise divergence among genotypes, which represents the nucleotide diversity per bp, π (pi), and the expected number of polymorphic sites per nucleotide, θ (theta), were estimated in TASSEL v5.2.3965 using an admixture model of the diploid genotypic calls. STRUCTURE places clones in subpopulations based on similar patterns of variation. For each dataset, three replicates were performed for each value of K from one to ten with a 50,000 burn-in time, and the number of Markov Chain Monte Carlo replicates also set to 50,000. For each K, we checked whether the run parameters  reach convergence within the burn-in period. The most probable K-value was determined by STRUCTURE Harvester66, using the log probability of the data [LnP(D)] and delta K (ΔK) based on the rate of change in [LnP(D)] between successive K-values.Population structure was determined using STRUCTURE software version 2.3.4adegenet package67 in R to identify and describe clusters based on genetic relationships using a diploid form of genotyping data. The feature find.clusters was used to identify the number of clusters within the population. The K-means clustering decomposes the variable’s total variance into between-group and within-group components. The lowest associated BIC had defined the best number of subpopulations. The correct number of principal components (PCs) to be maintained was verified using a cross-validation feature . In this analysis, the data is divided into two sets: a training set (90 percent of the data) and a validation set (10 percent of the data). The members of each group are chosen by stratified random sampling, ensuring that at least one member of each group or population is reflected in the original data in both training and validation sets. DAPC is performed on the training set with a variable number of retained PCs, and the degree to which the analysis can accurately predict group membership of excluded individuals  is used to determine the optimum number of retained PC. The sampling and DAPC procedures are repeated many times at every PC retention level. The best number of PCs that should be taken is associated with the lowest root mean square error. SNPZIP analysis was used to identify alleles with the largest contributions to form the linear discriminants and allocate the genotypes to the clusters. The coefficient of genetic differentiation among groups (Fst) was calculated using stamppFst in StAMPP package68 in R.Was done using the 69 was calculated, and a distance matrix was obtained with the StAMPP package68 using the tetraploid SNP genotype calls. The resulting matrix was used to build a dendrogram using the hierarchical clustering (method = “Ward D”) implementing in the Ape package70 in R. Duplications, mislabeling, and errors with the naming were identified from the dendrogram based on clustering. After removing duplicates and mislabeled clones, a core set of clone bank collection was developed for long-term in-vitro maintenance.Pairwise Nei genetic distance21 was based on allele frequency differentiation whereas, the iHS71  and the XP-EHH22 were based on linkage disequilibrium (LD) patterns.Signatures of selection analyses were performed using 10,106 SNPs applying three complementary statistical methods. The outlier test PCAdapt72 was used to identify loci related to diversification in R. The option for performing LD clumping was applied, this removes variants in LD and ensures that more PCs capture population structure instead of LD structure72. The initial number of PCs was set as K = 20, and the scree plot was used to pick the K that explains much of the variance. The choice of K was also verified by projecting individuals on the principal components  to see if the clustering level was consistent with the value selected for K. The Mahalanobis distances were then used to search for outlier SNPs and transformed into p‐values to perform hypothesis testing. A Q-Q plot of the predicted p-values vs. observed p-values was used to visualize the distribution of the p-values. The cut‐off for identifying selections was then based on the q‐value method using the qvalue R package73, using 5% as false discovery rate threshold.PCAdapt Version 474 with a window size of 1 Mb and 500 iterations, including 200 burn-in and pruning iterations, was used to derive haplotypes for iHS and XP-EHH analyses. The iHS and XP-EHH analysis was done using the rehh package75 in R software. To allow better visualization and analysis of regions under selection, the iHS and XP-EHH scores were standardized to a distribution with zero mean and unit variance. In addition, p-values were calculated with the threshold set at 1 percent, as defined in Gautier and Naves76 and FDR performed following Storey and Tibshirani77. Candidate selection sweep regions were classified as SNP regions identified as being under selection by at least two of the statistics applied. Genes spanning 250 kb upstream and downstream of the candidate selection regions were retrieved from the representative gene annotation for the pseudomolecules from the Potato Genome Sequencing Consortium (PGSC) public data79 retrieved from http://solanaceae.plantbiology.msu.edu/pgsc_download.shtml.SHAPEIT280. This software generates subsets based on multiple genetic measures, including both distance measures and allelic diversity indices (http://www.corehunter.org). The function sampleCore was run on a precomputed Nei’s distance matrix of 214 clones.A core set of most diverse clones was identified using Core Hunter 381. Pedigree information was assembled from variety release publications, published potato pedigree database, and TAMU potato program breeding records. If both parents were genotyped, the pedigree conflict rate was used to identify pedigree errors. For each of the parent–offspring trios (two parents and one offspring) in the dataset, a pedigree conflict metric was calculated as the percentage of monomorphic  markers in the cross at which the genotype of the offspring was different. When only one parent was genotyped, the marker vs. pedigree plot was used to confirm (or not) the known parent.A curated dataset was used to check the accuracy of pedigree records using the methodology of Endelman et al.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.Supplementary Information 13.Supplementary Information 14.Supplementary Information 15.


Extreme conditions and the availability of determinate substrates in oil fields promote the growth of a specific microbiome. Sulfate-reducing bacteria (SRB) and acid-producing bacteria (APB) are usually found in these places and can harm important processes due to increases in corrosion rates, biofouling and reservoir biosouring. Biocides such as glutaraldehyde, dibromo-nitrilopropionamide (DBNPA), tetrakis (hydroxymethyl) phosphonium sulfate (THPS) and alkyl dimethyl benzyl ammonium chloride (ADBAC) are commonly used in oil fields to mitigate uncontrolled microbial growth. The aim of this work was to evaluate the differences among microbiome compositions and their resistance to standard biocides in four different Brazilian produced water samples, two from a Southeast Brazil offshore oil field and two from different Northeast Brazil onshore oil fields. Microbiome evaluations were carried out through 16S rRNA amplicon sequencing. To evaluate the biocidal resistance, the Minimum Inhibitory Concentration (MIC) of the standard biocides were analyzed using enriched consortia of SRB and APB from the produced water samples. The data showed important differences in terms of taxonomy but similar functional characterization, indicating the high diversity of the microbiomes. The APB and SRB consortia demonstrated varying resistance levels against the biocides. These results will help to customize biocidal treatments in oil fields. Uncontrolled microbial activity can increase oil contamination, microbially induced corrosion, biofouling and the generation of dangerous metabolic byproducts, such as hydrogen sulfide  and/or ADBAC (continuous low dosage application) as chemical treatments for produced water4.In mature fields, the secondary recovery of oil requires water/gas injection to push the oil from the well to the platform surface. To decrease storage costs, produced water generated during oil extraction is commonly used, but it first receives a biocidal treatment to prevent microbial contamination in the well. Among the most employed biocides worldwide used in produced water treatment are glutaraldehyde (27% of the market share), dibromo-nitrilopropionamide , tetrakis (hydroxymethyl) phosphonium sulfate  and alkyl dimethyl benzyl ammonium chloride 5. APB produces several types of organic and inorganic acids and other metabolites under anaerobic conditions. The generated metabolites interact with steel surfaces, affecting electrochemical reactions and, consequently, the corrosion rate. The SRB group, in addition to increasing corrosion, is known to generate H2S, a toxic, corrosive and inflammable gas, which causes concerns in terms of oil field structural integrity and worker safety6.Due to the extreme environmental conditions found in oil fields, such as high temperature, low oxygen content and high salinity, some specific groups of bacteria are present. Among them, the main biocide targets are acid-producing bacteria (APB), also known as fermentative bacteria, and sulfate-reducing bacteria (SRB)7. Factors such as microbiome composition, treatment history and resistance level need to be observed to choose the best method for microbial control. Therefore, the aim of this study was to evaluate four different produced water samples in terms of their microbiome compositions and metabolic activities. The Minimum Inhibitory Concentration (MIC) of glutaraldehyde, DBNPA, THPS and ADBAC against SRB and APB consortia obtained from produced water samples were determined.The application of similar treatments over the years without technical monitoring selects those microorganisms with high biocide resistance levelsFour samples of produced water from three Brazilian oil fields were used in this study. Two samples were collected in different wells from the same offshore field located in the state of Rio de Janeiro (Offshore 1a and Offshore 1b), and two samples were collected from different fields in the state of Bahia (Onshore 1 and Onshore 2) . A sulfur standard was used to determine the sulfate content. pH was determined with a pH meter . The microbial concentration was determined by ATP quantification using a luminometer  and specific reagent kit .Produced water samples were characterized immediately after receipt according to salinity, sulfate content, pH and microbial concentration. Salinity (chloride content) was determined by potentiometric titration in a Titrino Plus 848 . The equipment was connected to a silver electrode, and 0.01 mol LTwo 100 mL aliquots of each sample were filtered through 0.45 µm membranes to obtain duplicates. Total genomic DNA was extracted from each membrane using a DNEasy PowerSoil Kit  following the manufacturer’s instructions. The extracted DNA was quantified by a Quantus fluorimeter .8. PCR amplification was carried out by mixing ~ 100 ng of genomic DNA, 1.0 mM MgCl2, 0.5 µM of each primer, 0.2 mM of dNTP, 2 U Platinum Taq DNA Polymerase High Fidelity , and 1 × reaction buffer. The thermal cycling conditions for amplification were an initial denaturation (2 min at 94 °C) followed by 25 cycles of amplification  and a final extension cycle (6 min at 72 °C) in a Mastercycler Personal 5332 Thermocycler .The V4 region of the 16S rRNA gene was amplified using primers 515F (5´ GTGCCAGCMGCCGCGGTAA 3´) and R806 (5´ GGACTACHVGGGTWTCTAAT 3’), both modified to contain an Illumina adapter region Amplicons were purified using Agencourt AMPure XP beads  following the manufacturer’s instructions. Sequencing was conducted on the Illumina MiSeq platform  with a v2 500 kit, which generates paired end reads of 250 bp.9) using the SILVA database10 (release 138.1). Amplicon sequences are available at the European Bioinformatic Institute (EBI) under the EBI Metagenomic Database with accession number ERP121357.All the data were processed following standard bioinformatics pipelines, including quality control, sequence alignment , taxonomic classification, operational taxonomic unit (OTU) clustering and community richness analysis  was performed to determine whether a linear or unimodal ordering method could be applied to the data. The highest gradient obtained was greater than 3.0 SD units, which indicated that a restricted method based on a unimodal model was adequate. For that, we used CCA  with an explanatory matrix (abiotic factors) with general averages for the following: pH, chlorides and sulfates. The communities were tested and inferred by the possible ecological preferences for the explanatory variables, considering the different collection points. For a more conservative approach, it the analysis was carried out with the phyla and the main bacterial genera identified. Thus, a triple plot CCA was performed to better observe how explanatory variables (vectored lines) influence the distribution of samples and locations, represented in the axes. The type 2 scaling parameter was used to observe the position of the maximum values of the variables with the explanatory ones by the average of the orthogonal distance between samples and vectored lines. The analysis was conducted using the Vegan community ecology package in R version 2.5-7.−1 glucose was used. The medium was purged with N2 to decrease the oxygen content16 (1 h per L) and added to 30 mL sealed bottles. The bottles were sterilized in an autoclave for 15 min at 121 °C. The same process was carried out for the SRB growth medium, which was modified Postgate E18 . The pH was adjusted to 7.6 with NaOH solution, and before the use, 4.0 mL·L−1 of a 0.025% resazurin solution previously filtered in a 0.22 µm membrane filter, was added.Specific culture media were used for SRB and APB growth. For APB growth, Phenol Red Broth medium  with 15 g LAPB and SRB consortia were enriched in specific media for 21 days at 35 °C (average oilfield temperature). APB and SRB consortia were transferred weekly (10% (v/v)) to sterile sealed bottles containing Phenol Red Glucose Broth and modified Postgate E, respectively. After inoculation with SRB or APB consortia, a layer of mineral oil was added to maintain the anaerobic environment.−1 of the biocides were dosed in 96-well microtiter plates19 containing specific culture medium and an inoculum of 1 × 106 cells/mL, quantified by a luminometer  using a specific reagent kit . A positive control of each consortium was used for the validation of the test. The bottles were incubated in an anaerobic jar with an anaerobiosis generator  for 72 h at 35 °C.To evaluate the resistance of the consortia against glutaraldehyde, DBNPA, THPS and ADBAC, MIC analyzes were carried out. Concentrations of 25, 50, 100, 250 and 500 mg·L20. Worldwide, the volume of produced water is increasing due to the maturation process of reservoirs and the greater number of production fields 22.In the O&G industry, produced water is the term used for water emulsified with oil during the extraction process, and due to the large volumes generated, it is considered one of the most important effluents in O&G. In 2009, the world generation of produced water was estimated to be > 70 billion barrels per year. In Brazil, the average generation is approximately 224 million barrels per day21. Despite the proximity of the onshore fields where the samples were collected , Halanaerobium (APB), Syntrophotalea (sulfur-reducing), Halomonas (APB), Desulfonauticus (SRB), Sulfurospirillum (sulfur-reducing) and Guyparkeria, also named Halothiobacillus (sulfur-oxidizing), were predominant. The identification of these groups of microorganisms can be explained by the extreme conditions found in oilfields, such as high sulfur derivative contents in water, high salinity and high temperatures.Similar to the physical–chemical characteristics, the microbiome of produced water changed according to the sample collection location. However, some microorganisms with important ecological roles have been frequently reported. Sulfate- and sulfur-reducing, acid-producing, thermophilic, halotolerant and halophilic bacteria are the most common groups comprising oil reservoir ecosystems2S generation and increased corrosion rates25. Produced water normally provides favorable conditions for the development of SRB due to the concentrated supply of sulfate and low oxygen content27. All samples evaluated in this work demonstrated a high abundance of SRB. These microorganisms are able to use sulfate (SO42−) as an electron acceptor in dissimilatory sulfate reduction for anaerobic respiration processes, generating H2S and other byproducts28.Among the microorganisms found in the samples, SRB are generally the main target in biocidal treatments due to the negative impact caused by HIn the analyzed samples, the sulfate content was highest in samples Onshore 1 and Offshore 1a (Table 2S but use only sulfur (S0) and oxidized forms as electron acceptors (except sulfate). Bacteria from sulfur-reducing groups are able to grow through the dissimilatory reduction of elemental sulfur to sulfide in a respiratory type of metabolism 30 and are frequently found in extreme environments, such as coal mine wastewater and heavy metal-containing waters 32. Despite their similarities with the SRB group, sulfur-reducing bacteria show important differences in taxonomy33, suggesting that organic substrates suitable for SRB may not be suitable for their growth34. This group of microorganisms seems to have a key role in the balance of determinate microbiomes, but in most environments, especially in oilfield wastewaters, their role remains unclear.Similar to SRB, sulfur-reducing bacteria generate HHalanaerobium and Halomonas intrinsically enter seawater due to their halophilic characteristics and are involved in corrosive processes38. Halanaerobium, predominant in Offshore 1a, is a strictly anaerobic and moderately halophilic genus that requires NaCl concentrations between 30,000 and 200,000 mg L−1 for optimal growth39. The role of APB microorganisms in biocorrosion has been demonstrated, mostly due to their capacity for biofilm formation 40 and the production of acids from sugars41. In addition, studies have demonstrated the synergistic role of APB in SRB biofilms, where the presence of both groups enhances biofilm aggressiveness 42. Another APB identified in the Offshore 1a sample was Geotoga. Despite the low relative abundance in this sample, Geotoga has been identified in oil reservoirs44 and fuel storange tanks45.APBs such as Syntrophotalea, also named Pelobacter47. The Syntrophotalea genus consists of strictly anaerobic gram-negative bacteria whose members are unable to ferment sugars, metabolizing only an extremely limited number of substrates. This genus plays an important role in the fermentative degradation of unusual organic matter, such as ethanol and butanol, and is able to use sulfur (S°) as an electron acceptor in these reactions32. In addition, the association of Syntrophotalea species with methanogenic bacteria in the degradation of crude oil alkanes, an intrinsic substrate from oil reservoirs, was observed49.Another abundant genus found in the analyzed samples was 21, the microbiome complexity may be influenced by the water composition.A significant number of unclassified genera (38% in the four samples), especially those collected from onshore fields, and the high number of OTUs demonstrated the high complexity of the microbiome in produced water. In addition to environmental factors, such as temperature, pressure and geographic locationDesulfovibrio, Desulfoplanes and Desulfonauticus were influenced by this factor.Figures On the other hand, produced water is a complex matrix, where a small group of abiotic factors cannot reduce all other influences. In this case, factors not evaluated in this work may have contributed to the Onshore 1a microbial composition. Extreme conditions, such as pressure and temperature, and even the selective pressure caused by chemical agents applied in oil fields are examples of possible interfering abiotic factors.Through the taxonomic abundance of OTUs, the metabolic abundances were predicted. Despite a quite different microbiome taxonomy, the four samples showed remarkably similar metabolic activities Fig. A.Figure 51. Secondary metabolites, such as pantothenate, fatty acids, prodigiosin, biotin, cofactors and vitamins, inosine monophosphate, among others were predicted by KEGG according to microbial composition data.As expected, primary metabolism, for maintenance of bacterial life, was the most abundant in all samples; however, biosynthesis of secondary metabolites also demonstrated substantial relative abundance. Secondary metabolites are produced for survival under certain conditions, such as microbial competition for nutrients or space and protection against abiotic stress53. These metabolic activities have been described as a trigger for biofouling in oil fields, causing damage to membrane units, clogging pipes and harming systems and processes55.Other important metabolic activities found were quorum sensing, biofilm formation, carbon, sulfur, nitrogen and methane metabolism, antimicrobial resistance and degradation of aromatic compounds. Quorum sensing and biofilm formation are related to population density control and response to stress and can also be associated with substrate limitation or other types of stress56. Initial hydrocarbon degradation presents high energy barriers to be overcome due to the apolar C–H bond. Pelobacter, Desulfovibrio and other SRB are part of complex communities involved in anaerobic degradation of hydrocarbons under syntrophic conditions57.Carbon metabolism is essential for microbial growth due to its activity as an electron donor for respiration, and it is incorporated into cells as biomass. Usually, volatile fatty acids (VFAs) are the most common carbon sources in produced water, but hydrocarbons, including aromatic compounds, have also been found2S)58. The main mechanism for H2S generation is the dissimilatory sulfate-reduction pathway, where sulfate is converted to sulfite and then to sulfide by an irreversible reaction. Additional studies suggest two possible alternative biosouring mechanisms: 1) thiosulfate reduction, when the medium presents a low sulfate content, or the presence of sulfate-reducing prokaryotes, and 2) sulfide generation through hydrogen and formate metabolization, mostly by the Desulfonauticus genus59.Metabolism of sulfur and its derivatives has been frequently associated with negative impacts in oil fields and can increase biosouring in oil reservoirs . Analyzing the gene ontologies belonging to the observed OTUs, some similarities and differences can be observed among the samples Table .Table 1P65. Vikram et al., 2015 described efflux systems as the main resistance mechanism used by Pseudomonas fluorescens biofilms against glutaraldehyde. A comparison of the response to biocides of glutaraldehyde- and PBS-exposed biofilms at the transcriptomic level revealed the induction of genes related to RND systems and ABC transporters66.Antimicrobial efflux mechanisms were present in the four samples and can include several types of efflux systems. The resistance-nodulation-division system (RND) operates as part of a tripartite system and is found ubiquitously in bacteria, archaea and eukaryotes. RND pump ontologies, such as AcrA/AcrB/TolC, MexAB/OprM and AdeABC systems, were widely found in microorganisms from produced water samples. These systems can extrude multiple antimicrobials using energy conversion from the proton motive force due to their broad substrate specificity67. These enzymes are targets of β-lactam antibiotics, but conformational modifications can lead to the nonrecognition of the active site by antibiotics and, consequently, to antimicrobial resistance. PBP ontologies found in the samples indicated higher abundances of the genes mrcA, mrdA and ftsl. Despite the cross-resistance between antibiotics and biocides being studied68, the role of PBP in resistance to biocides, specifically in oil fields, remains unclear.Mechanisms related to target modification identified in the microbiome of produced water samples are derived from penicillin-binding proteins (PBPs), which, in addition to their role in cell wall synthesis, precisely in peptidoglycan crosslinking, are membrane-associated macromolecules that play key roles in resistance mechanisms69. ALDHs from different microorganisms show different substrate specificities but are able to degrade biocides such as glutaraldehyde and formaldehyde71. For these reasons, microorganisms able to produce ALDH enzymes should present advantages in terms of intrinsic resistance against aldehyde-based biocides.The modification/degradation of antimicrobials by microorganisms can occur through the action of enzymes such as aldehyde dehydrogenase (ALDH), which catalyzes the oxidation of aldehydes, commonly present in biocides, to carboxylic acids. These enzymes are also involved in several other important metabolic processes, including glycerol production and ethanol oxidationAlthough present in lower abundance in most samples, β-lactamases, uniquely responsible for β-lactam degradation and resistance, were also present. Genes codifying β-lactamase classes A, B, C and D were found, but those codifying classes C and D demonstrated the highest abundances in the microbiomes.73. Due to these microbial interactions and diverse microbiomes, oilfield microorganisms have an interdependent lifestyle, which must be deeply studied to fully understand their metabolic activities.In general, the microbiome from produced water samples demonstrated a huge variety of pathways involved in primary and secondary metabolism. Studies have shown that microorganisms found in oil fields carry out extremely complex “cross feeding” among them named anaerobic syntrophySRB and APB consortia from produced water samples were previously enriched in specific media, except consortia from Offshore 1b and Onshore 1, which did not present cultivable SRB or APB or cultivable SRB, respectively. MICs of biocides commonly applied in produced water treatment  were applied to the SRB and APB consortia.−1), while Onshore 2 presented reduced susceptibility to glutaraldehyde and THPS . In general, SRB consortia were less susceptible to DBNPA and presented resistance against ADBAC. SRB resistance to ADBAC and other quaternary ammonium compounds has also been reported in several studies 76 but needs further study.Table −1 to exert bacteriostatic effects on the APB consortia.High levels of APB resistance were reported, and unlike for SRB, only DBNPA and ADBAC showed effectiveness at the tested dosages. Glutaraldehyde and THPS required dosages above 500 mg L77, neem78, and clove oils80 and plant extracts such as those of green tea81 and garlic81, amino acids82 and biosurfactants83 have all been successfully evaluated against oilfield microorganisms and biofilms. Another way to broaden the effectiveness of water treatment is the use of synergistic molecules, especially those with different modes of action or cell targets.The use of alternative biocides can reduce microbial resistance, changing the biocidal mechanisms to control microorganisms. Green molecules are becoming a new trend due to their lower environmental impact and renewable sources. Natural oils such as lemonThis work revealed the high diversity of extremophiles in the produced water microbiome, even in the samples collected from the same platform. Despite the diversity, SRB, APB and sulfur-reducing bacterial groups were found with high abundance in the four samples. Functional characterization of gene ontologies predicted for the microbiomes demonstrated similar activities, mostly due to the similar nature of the samples. In terms of resistance, the SRB and APB consortia enriched from produced water samples demonstrated varied resistance levels to biocides commonly used in water treatment, indicating a specific taxonomic response to each biocide and reinforcing the idea of the necessity of combined treatments (with more than one molecule) or new molecules to broaden treatment effectiveness. To confirm the findings found in this work, further evaluations to identify the bacterial composition of SRB and APB consortia need to be carried out in addition to further resistance studies.Supplementary Information.