Ocean conditions near the grounding zones of Antarctica’s ice shelves play a key role in controlling the outflow and mass balance of the ice sheet. However, ocean observations in these regions are largely absent. Here, we present a detailed spatial survey collected with an underwater vehicle in a basal crevasse located in the ocean cavity at the Ross Ice Shelf grounding zone. The observations depict fine-scale variability in ocean forcing that drives asymmetric melting along the lower crevasse sidewalls and freezing in the upper reaches of the crevasse. Freshwater release from melting at depth and salt rejection from freezing above drives an overturning circulation. This vertical circulation pattern overlays a dominant throughflow jet, which funnels water parallel to the coastline, orthogonal to the direction of tidal currents. Importantly, these data reveal that basal crevasses influence ocean circulation and mixing at ice shelf grounding zones to an extent previously unknown. In situ observations reveal how ocean physical properties drive ice melting and freezing within an ice shelf basal crevasse. Note that hereafter we simply refer to conservative temperature as temperature and absolute salinity as salinity.The Filchner-Ronne, Ross, and Amery ice shelves have been stable over the modern satellite observational record  an effective temperature of −92.50°C (f< 0°C) in the water column that forms saline marine ice with a temperature characteristic of the salinity- and pressure-dependent freezing point Icefin . These features extended upward from a relatively flat ice shelf base with concave features separating the crevasses . The ocean circulation within the crevasse exhibited a three-dimensional structure that varied considerably with horizontal and vertical position in the feature. The strongest velocities flowed southward  through the crevasse at a moderate rate of v = −6 cm s−1 in a jet concentrated along the western sidewall below 490-m depth  less than 0 s−2 (−5 s−2> N2 ≤ −1 × 10−5 s−2) occurred around 485- and 492-m depth. The shallower instabilities were sampled toward the lower limit of the observed downwelling plume . Visidewall . Interlesidewall . These dan 0 s−2 . On the ng plume  where thng plume  and visung plume  located ng plume . We attr−5 s−2> N2 ≤ 2 × 10−5 s−2) over the 490- to 508-m-depth range of the throughflow jet, which exhibited broad upwelling  section of the western and eastern crevasse walls beneath 476- and 494-m depth, respectively . Scallopd) between 13 and 22 cm (table S1). Symmetrical scallops also occurred on the western sidewall keel at 508-m depth, with λ = 48 to 63 cm and d = 10 to 13 cm. Scallops along the western sidewall above this depth (between 482 and 506 m) were instead asymmetric, with an elongated vertical axis of λ = 88 to 157 cm and a horizontal axis of λ = 43 to 78 cm. These asymmetric scallops also exhibited greater incision depths, ranging from 15 to 32 cm.Scallops exhibited quasi-symmetrical axes along the eastern sidewall, with axes oriented in the horizontal and vertical directions of wavelength (λ) ranging from 45 to 78 cm and incision depth (v), with crests perpendicular to the throughflow jet’s horizontal southward flow  tidally driven currents in the underlying ocean cavity (fig. S1). Scallops along the eastern sidewall followed a similar transition from a more random orientation along the crevasse to keel at 506-m depth to a generally more organized north-south horizontal axis pattern upward in the crevasse to 494-m depth  to meteoric ice .The runnels along the eastern sidewall initiated as 60- to 120-cm-wide and 30-cm-deep features carved into the marine ice above the supercooling horizon (within sensor uncertainty), located at 471-m depth at the time of sampling. They deepened and widened below this depth, etching into the meteoric ice in the above-freezing portion of the crevasse to a maximum of Our observations of a vigorous sinking plume adjacent to the eastern runnels and their progressively deeper incision into the ice with depth in the crevasse agree with a localized melting mechanism. However, the presence of scallops superimposed on the eastern wall runnels below 494-m depth and negligible observed vertical ocean currents indicate that horizontal circulation dominated from this depth to the keel. This is additionally supported by negligible further incision of the runnels despite a twofold widening between 494-m depth and the crevasse keel. We also interpret that the western runnels’ small vertical range results from the primarily horizontal throughflow jet dominating melting along most of the above-freezing portion of this crevasse sidewall.<0.10 cm , which is the square root of the kinematic shear stress exerted by a surface on a flow . At the time of sampling, the throughflow jet adjacent to the western wall exhibited a dominant southward horizontal and secondary vertical circulation pattern from 487-m depth to below the crevasse keel (u∗ = 0.32 cm s−1 on the ocean (table S1). Relating u∗ to the scallops’ horizontal λ produced a dimensionless drag coefficient (CD) between 2.70 × 10−3 and 9.20 × 10−3, with a mean of 5.90 × 10−3 . The point of no flow occurred at a roughness length (z0) of 3.53 cm from the ice surface or d.The immobile crevasse sidewalls exerted drag on ocean flow through the crevasse, which induced velocity shear as it transitioned from free-stream to no flow across the boundary layer. This phenomenon can be represented by the friction velocity . The z0 was smaller at 2.21 cm with a narrower range of d. Despite a much smaller sample number (N = 70) used to derive these parameters along the eastern sidewall than the western sidewall (N = 839), the scallops throughout the crevasse exerted a similar frictional drag on the adjacent oceanic flow.Scallops along the eastern sidewall from 483- to 506-m depth modified the weaker and less-organized horizontal flow on this half of the crevasse to exert an average u∗ = 0.21 cm s−1 and z0 = 4.16 cm. This translated to 1 × 10−3 ≥ CD ≤ 5.50 × 10−3 (mean = 2.30 × 10−3) based on the runnels’ horizontal λ and d. Note that while the analysis yielded a satisfactory fit (table S1), the lack of data located further away from the wall and the localized nature of runnel melt features cause uncertainty regarding the shape of the boundary layer velocity profile. We suspect that d may relate to u∗ and CD in these vertically oriented features more appropriately than λ. Nonetheless, the quantification of frictional forces exerted by various ice morphology on the ocean reveals that they induce shear to increase turbulent kinetic energy, which potentially enhances melting throughout the crevasse.The downwelling plume adjoined vertically oriented runnels along the eastern sidewall over an observed depth range of 471 to 483 m. Implementing the same analysis as above yielded Considering phase changes occurring both directly at the sidewall ice-ocean interface  where shear induced by the throughflow jet efficiently mixed the sparse ocean heat into the ice face. Melt rates along the eastern wall were lower at a maximum around 0.17 ± 0.04 m year−1, due to reduced current speeds on this side of the crevasse. Melting decreased with height in the crevasse to near zero at 486-m depth, as current speeds reached a minimum and pressure release caused ocean thermal driving to approach 0°C. The elevated melt rates along the lower western crevasse sidewall relative to the eastern sidewall are consistent with its shallower slope. This suggests that the throughflow jet is a persistent feature that enhances localized melting and erodes this section of the crevasse to produce its shallower slope.The transition from above to below freezing ocean temperatures paired with highly variable current speeds  affected−1, with frazil crystal formation . We convert our transfer velocities to average values of these parameters  so that they may be considered in future studies that are unable to directly calculate CD. Transfer velocities . A portion of this dive extending upward to 492-m depth in the crevasse located around 4100 m from the GL .The visual onset of marine ice on the eastern sidewall coincided with the depth that neighboring ocean temperatures transitioned to supercooled . The tuned rising and sinking plume model reflects this, with simulated total neap tide freeze rates increasing from ∼65% greater than spring melt rates near the western supercooling horizon to ∼500% greater near the crevasse roof .Predicted spring tide melting persists in the upper portion of the crevasse that was supercooled during neap tide at low rates of 0.03 to 0.12 m yearOur observations expose in unprecedented detail the ice morphology, three-dimensional ocean circulation, and thermodynamics within an ice shelf basal crevasse. We identify a dominant jet flowing through the crevasse and an overturning circulation driven by freshwater release from melting on the lower sidewalls and salt rejection from freezing above . This ovThe dominant horizontal throughflow jet intensifies estimated melting by four times along the lower western sidewall, when compared to rates driven solely by the overturning crevasse circulation. The higher velocities in this jet more efficiently entrain ambient water from the center of the crevasse, which dilutes cold and fresh GMW released during melting and preserves the upwelling plume’s positive thermal driving. This process raises the supercooling horizon along the western half of the crevasse to the depth predicted solely by pressure release, thus largely negating the cooling and freshening from melting. However, we posit that GMW accumulation from the integrated melting driven by the throughflow jet along the crevasse sidewall will lower thermal driving and the supercooling horizon downstream (south at the time of sampling) of our study site, which would in turn influence crevasse geometry.Icefin survey conducted outside of the crevasse identified quasi-two-dimensional ripple formations in the ice base with crests oriented orthogonal to the northeast-southwest (perpendicular to GL) tidal currents that dominated circulation to first order in the underlying ocean cavity  and faster current speeds that drove melt rates on the order of 100 m year−1 showed that ice morphological features can form in under 12 hours (The portion of the 12 hours  (20), thThe crevasse width is an order of magnitude lower than the internal deformation radius given the observed ocean conditions, and recorded maximum north-south velocity shear across the crevasse exceeds predicted geostrophic velocities by six times . This signifies that Earth’s rotation does not alter the flow direction as it feels the friction of the crevasse wall  to a 110-m2 area to convert area fluxes  to volume fluxes . Meltwater only comprised ∼0.40% of the jet’s volume transport due to the low GMW concentrations from relatively weak ice shelf melting. Nevertheless, the nontrivial total volume funneled through the crevasse in this jet at the time of sampling argues that the crevasse extends laterally across RIS for a considerable distance.The dominant jet flowing through the crevasse transported a volume of water southward at 5.54 m3 s−1 over a 550-m2 area at the time of sampling. The small area that this water funneled through obscures the potential larger influence of this transport. If the volume transport rate through the crevasses is standardized by the flux gate area, then it becomes 28% of the inflow through one of the main pathways that translates the source ocean water mass (HSSW) beneath RIS that feeds our study region  onboard features  and a prIcefin: a Neil Brown Ocean Sensors conductivity-temperature (CT) sensor and a Valeport ultraP pressure (P) sensor. The manufacturer-stated accuracies are C: ± 0.010 mS cm−1, T: ± 0.005°C, and P: 0.100 dbar. The C, T, and P accuracies translate to uncertainties in SA of ±0.008 g kg−1 and Θ of ±0.018°C. All sensors were factory calibrated before entering the field in 2019, and the CT sensor was field calibrated through a cross comparison of vertical profile data from beneath the borehole at RIS with data obtained independently with an RBR Concerto3 CTD profiler. This resulted in offsets/root mean square residuals of C: 0.249 mS cm−1/0.003 mS cm−1 and T: 0.018°C/0.002°C. The fact that these residuals are lower than the manufacturer-stated accuracies lends confidence to the quality of data after application. The CT sensor recorded at a frequency of 5 Hz and the P sensor recorded at 1 Hz. Pressure measurements were interpolated to match the 5-Hz CT data to derive hydrographic variables.We follow the processing reported in  Remove background atmospheric pressure reading;C and T (excluding borehole data);2) Remove outliers ±2 standard deviations (SDs) from the mean for C, T, and P;3) Apply three-point weighted mean filter to C and T measurements with time lag ;4) Align C and T data when Icefin moved at speeds less than 5 cm s−1;5) Remove SA, density (ρ), etc., using the Thermodynamic Equation of Seawater 2010 (6) Derive hydrographic variables such as Θ, 7) Average hydrographic variables into 1-m bins with a ±1 SD range.SA and Θ were 0.002 g kg−1 and 0.002°C for the west profile and 0.002 g kg−1 and 0.003°C for the east profile. These ranges are well below the initial uncertainties derived from manufacturer-stated C, T, and P accuracies, which supports our analysis and interpretation of small-scale variability in these properties within the crevasse.After postprocessing, a mean of 77 and 73 samples were averaged into each 1-m bin of the associated hydrographic variable for the west and east crevasse profiles, respectively. The average ±1 SD range for each 1-m bin of z direction discussed here. Three-dimensional ocean current velocities come from the DVL on board Icefin, which doubles as an acoustic Doppler current profiler (ADCP). As the DVL retrieves vehicle motion from an immobile surface, the ADCP function monitors water column velocities in 2-m bins at a variable start distance from the vehicle. The minimum nominal altitude from a surface for the ADCP to operate is 10 m, and gradients in vehicle pitch, roll, heading, and velocity control the distance of the first bin from the vehicle and the sampling frequency. Here, we subsample ADCP velocities to a fixed rate of 1 Hz; they can reach a maximum of 5 Hz. Manufacturer-stated accuracies for our ADCP are 1% of the DVL-recorded vehicle velocity in that direction. Icefin typically travels at ≤50 cm s−1 in the x direction, making uncertainty in ADCP × velocities 0.50 cm s−1. Vehicle motions in y and z are typically substantially slower, so the associated ADCP y and z uncertainties are also lower.We follow the processing reported in  Remove measurements if bin depth is below seafloor depth;2) Recalculate bin depth and horizontal location considering vehicle pitch and roll;−1 or unadjusted (before differencing vehicle velocity) value is 32,767 m s−1 ;3) Remove measurements if exactly 0 m s4) Remove measurements if absolute value of vehicle pitch or roll is greater than 30°;x and y velocities);5) Recalculate measurements considering vehicle pitch and roll (excluding z velocities);6) Convert measurements from vehicle reference frame to geographic reference frame (excluding 7) Apply 30-s rolling mean filter;8) Filter measurements for gradients greater than 1 SD from the mean in vehicle speed, pitch, roll, and individual bin velocity; and9) Collate bins into 1-m vertical bins and then remove measurements if they exceed 1 SD of the mean for that 1-m depth range.x, y, and z velocity components reflect ocean currents in the eastward (u), northward (v), and vertical (w) directions.After postprocessing, the ADCP Icefin. The altimeter manufacturer states an accuracy of up to 1 mm. The DVL takes into account pitch, roll, and heading when producing ranges, and we apply these corrections to the altimeter in postprocessing.We follow the processing reported in (The elevation data from each of these sensors were postprocessed in the following manner:1) Remove data when vehicle pitch or roll exceeds 30°;2) Remove data when the gradient in vehicle pitch, roll, or heading exceeds 1 SD of the respective mean;3) Despike by removing data that exceed 2 SDs of the mean of the gradient in elevation of the surface;4) Investigate elevation profiles and manually remove outliers; and5) Apply a 3-point weighted mean filter to ice elevations and an 11-point weighted mean filter to seafloor elevations.After postprocessing, 90% of the ice elevation measurements were at a horizontal resolution of 63 ± 59 cm (mean ± 1 SD), with a minimum and maximum spacing of 0.06 and 627 cm, respectively. Eighty-six percent of postprocessed seafloor data were at a horizontal resolution of 44 ± 23 cm (mean ± 1 SD), with a minimum and maximum spacing of 18 and 169 cm, respectively.SA, and σΘ differences at each depth bin from 492 to 535 m between dive 1 and dive 3 (fig. S3). Note that we averaged dive 3 east and west profiles into a mean “central” crevasse profile in this exercise, because the dive 1 data were located toward the crevasse’s center. Dive 1 and 3 profiles exhibited very little change in Θ, SA, and σΘ with depth between the upper layer and in the crevasse, large variability across the pycnocline, and contrasting behaviors in the lower layer . The differences between profiles reflected these patterns , which led us to select the upper and crevasse layers for our offsets. We decided this because variability was most consistent between profiles in these layers, and the target of this effort is to investigate ice-ocean thermodynamics in an ice shelf basal crevasse. The Θ, SA, and σΘ differences in the upper and crevasse layers ranged from 0.0091° to 0.0293°C, from −0.0041 to 0.0052 g kg−1, and from −0.0037 to 0.0038 kg m−3, respectively. We selected the mean difference of Θ = 0.0191°C, SA = 0.0014 g kg−1, and σΘ = 0.007 kg m−3, and applied each to the dive 3 profiles as constant offsets to predict hydrographic conditions in the crevasse during spring tide. Note that the AS and σΘ differences are within the uncertainty range of the bin-averaged data and thus not significant.During dive 1, a small subset of data were collected up to 492-m depth in the crevasse located around 4750 m from the GL . These dSA changes reflect the turbulent mixture between three water masses:We estimate the concentrations of water masses in the ocean cavity beneath RIS with a three-point endmember partition  HSSW formed in Ross Sea polynyas during winter  Fresh GMW produced by ocean-driven ice shelf melt (SA = 0 g kg−1.3) Subglacial freshwater (SGW) produced by geothermal and frictional heating of the ice upstream of the GL (−1)−1 determined by the following equation  denotes current speeds  and y intercept (b) of the boundary layer logarithmic profile along sections of each crevasse wall with distinct circulation patterns. We express the respective z0 value as a fraction of the morphological incision depth d of features along the crevasse wall (table S1).We combine ADCP velocities with scaled ice morphologies obtained with  effects , which d effects . The logU ≥ 3 cm s−1) with the quadratic stress formula , practical salinity (BS), and pressure (Bp) at the ice-ocean interface and BT resides at the freezing point dictated by the local salinity and pressure; a = −5.73 × 10−2°C psu−1, b = 9.39 × 10−2, and c = −7.53 × 10−8°C Pa−1 are considered constants. The heat  flux divergence is the difference between the ocean heat  flux toward the ice We proceed with the conservation of heat equations in the following formw and T denote the measured ocean density and temperature along the crevasse walls, pc = 3974 J kg−1°C−1 is once again the specific heat capacity of cold seawater, and In i = 918, ipc= 2009 J kg−1°C−1, and −6 m2 s−1 are the density, specific heat capacity, and thermal diffusivity of ice, respectively. We assume that the temperature gradient through the ice shelf . Several nondimensional parameters commonly used in In B, T, and BS. BT and BS and solve for BT and BS can be calculated.The three unknowns in the melt rate parameterization are We follow a similar method to that described in the above section to estimate freeze rates of frazil ice crystals suspended in the supercooled water column by parameterizing heat and salt transfer at the crystal-ocean interface (Bp). In The conservation of heat and salt is as followsC represents the fraction of frazil crystals suspended in the water column at any time (varied from 1 × 10−9 to 1 × 10−5), and in T and S) are identical to the observations used at each depth bin in In Nu = 1 is the dimensionless Nusselt number, the ratio of convective to conductive heat transfer at a boundary, r is their radius. We choose the respective mean, minimum, and maximum value of r to be 0.60, 0.50, and 0.70 mm, which are within the range of observations , then use it to solve for D over the full crevasse extent along each sidewall. Values of D were initially set to half the width of the crevasse to compute D values. The plume model equations are as followsWe integrate our hydrographic observations of 2 s−1) in the region of the crevasse where we lack data in both the western and eastern halves by setting the upwelling flux wwestD equal to the downwelling flux weast(l − D), where l − D is the total width of the crevasse minus the upwelling plume width. We make the assumption that l − D equals the eastern half-width of the crevasse at our uppermost measurement of weast (471-m depth), effectively setting the downwelling plume width. We then find a value of D at our uppermost measurement of wwest (487-m depth) that after running the model produces a best fit to weast(l − D) at 471-m depth, with a residual of 0.01 m2 s−1 or ∼10% of the observation. The model iterates in 1-m bins to be consistent with our sampling resolution, considering the conservation of mass , and the entrainment constant E0 = 0.07  and the ambient fluid density ρa is the mean of ρwwest and ρweast at each depth bin; ρ0 = 1027 kg m−3 is the reference density.Beginning with  of mass  and 28 a of mass . In Eq. −1, we interpret that this marine ice does not melt away during spring tides. Therefore, we consider a range of initial values for C (1 × 10−9 to 1 × 10−5) in our model and select the lowest initial value of C, which produces an Cinitial = 1 × 10−6 fulfills this requirement for the full range of CD values considered , and a lower layer density of ρ2 = 1027.95 kg m−3 that represents the mean of the water column below the pycnocline (thickness H2 = 12 m); this produces iR = 559 m. We choose to consider the full water column above the pycnocline as our upper layer, because the vertical density gradient was very weak over this depth range.We follow (We follow (gv is directed through the crevasse and is based on an assumed level of no flow at our lowest data point (532-m depth). When the lateral density gradients (gv = −1 cm s−1. This is one-sixth of the maximum observed southward velocities in the throughflow jet.The calculated geostrophic velocity All statistics were performed in MATLAB. The above sections provide the details of these statistical analyses, with the underlying mean and SD functions being native to MATLAB.


Our findings suggest that these new pharmacophores have potential as antimicrobial agents, though further optimization is needed.In a previous study published by our group, successful modification of the antibiotic chloramphenicol (CHL) was reported, which was achieved by replacing the dichloroacetyl tail with alpha and beta amino acids, resulting in promising new antibacterial pharmacophores. In this study, CHL was further modified by linking the basic amino acids lysine, ornithine, and histidine to the primary hydroxyl group of CHL via triazole, carbamate, or amide bonding. Our results showed that while linking the basic amino acids retained antibacterial activity, it was somewhat reduced compared to CHL. However, in vitro testing demonstrated that all derivatives were comparable in activity to CHL and competed for the same ribosomal binding site with radioactive chloramphenicol. The amino acid–CHL tethering modes were evaluated either with carbamate ( S. pneumoniae  chloramphenicol binding, supporting the conclusion that all compounds share either the same overlapping sites or at least partially occupy them. 50 inhibition concentration was measured and the Ki values were calculated  Phe-tRNA was prepared using specific tRNA under standard conditions . 70S ribosomes (0.3 μM) were first incubated at 37 °C for 15 min with MF-mRNA (2.0 μM) and 0.6 μM uncharged tRNA phenylalanine initiator-phenylalanine specific (deacylated tRNAfMet in order to prefill the P-site). Nonenzymatic binding was then performed, followed by the addition of appropriately charged Ac[3H]Phe-tRNA at a final concentration of 0.5 μM, which occupied the A-site. The complex named pre-translocation was then incubated with EF-G  in the presence of GTP (0.12 mM) and was incubated at 37 °C for an additional 10 min. The post-translocation complex that was formed as described above carried a deacylated tRNA in the E-site and a peptidyl-tRNA in the P-site, resembled the elongating ribosome, and was isolated free of the unbound ligands  via centrifugation through a 10% sucrose cushion at 65,000× g for 18 h at 4 °C. The pellet was diluted in buffer A and was used for the puromycin reaction.70S reassociated ribosomes free of endogenous tRNAs were prepared from a et al.  and wereG)3AAAU]  was prepG)3AAAU] . EF-G wieviously . Ac[3H]Pl., 1988 ) and was3H]-phenylalanine was purchased from Amersham Pharmacia Biotech , whereas [14C]-chloramphenicol was obtained from Perkin Elmer . Specific tRNAs were purchased from Sigma.L--chloramphenicol (150 dpm/pmol) and increasing concentrations of radioactive CHL, to choose the best radioactive CHL concentration for competition experiments [14C]-chloramphenicol was studied in competition experiments with cold chloramphenicol or CHL derivatives by maintaining a constant concentration of [14C]-chloramphenicol (6 μM) and increasing concentrations of non-radioactive competitors.Reassociated 70S ribosomes  were incubated in buffer A  with phenylalanine-puromycin per ribosome.The reaction volume was 20 μL with a final puromycin concentration of 1 mM, in the absence or presence of 10 μM of each antibiotic. The reaction was stopped by the addition of an equal volume of 0.3 M sodium acetate saturated with MgSO


Caenorhabditis elegans involves the nuclear translocation of CaM kinase-1 (CMK-1) in the FLP thermo-nociceptors neurons, causing reduced animal heat sensitivity and avoidance responses. The phosphorylation of CMK-1 on T179 by CaM kinase kinase-1 (CKK-1) is required for nuclear entry. Recently, we identified a specific nuclear export sequence (NES) required to maintain CMK-1 in the cytoplasm at rest (20°C) and showed that Ca2+/CaM binding is sufficient to enhance CMK-1 affinity for IMA-3 via a specific nuclear localization signal (NLS) in order to promote nuclear entry after persistent heat stimulation (90 min at 28°C) . Here, we identified additional functional NES and NLS on CMK-1, whose activity can counteract previously identified elements. Furthermore, we clarify the relationship between the CaM-binding-dependent and T179-dependent effects. T179 phosphorylation can promote nuclear entry both downstream of CaM binding and as part of an independent/parallel pathway. Moreover, T179 phosphorylation can also produce the opposite effect by promoting nuclear export. Taken together, our studies suggest that multiple calcium-dependent regulatory mechanisms converge to bias the activity pattern across a network of NES/NLS elements, in order to control CMK-1 nucleo-cytoplasmic shuttling, and actuate stimulation-dependent nociceptive plasticity.Nociceptive habituation is a conserved process through which pain sensitivity threshold is adjusted based on past sensory experience and which may be dysregulated in human chronic pain conditions. Noxious heat habituation in  In response to neuronal activation, these kinases phosphorylate diverse intracellular substrates in order to actuate changes in cell physiology (2+/CaM in the regulatory domain and the kinase can be fully activated after phosphorylation of a specific threonine in the activation loop (T177 in human) by CaMK kinase I (CaMKKI). In mammals, CaM binding on CaMKI is a prerequisite prior to the phosphorylation of T177. The Caenorhabditis elegans CaMKI (named CMK-1) displays similar structural and functional properties, with a conserved activation loop threonine (T179 in the worm sequence) (Caysiology . CaMKI iysiology . This auequence) . CMK-1 mequence) . The subequence) .288-294 canonical nuclear export sequence of CMK-1, located immediately adjacent to the CaM-binding domain in the regulatory domain and representing a major determinant of CMK-1 cytoplasmic localization at 20°C. Second, the NLS71-78 canonical nuclear localization sequence, located on the N-terminal catalytic domain of CMK-1, and which can interact with IMA-3 importin in vitro. We furthermore showed that the binding of Ca2+/CaM on CMK-1 can strengthen its interaction with IMA-3 importin via NLS71-78 (C. elegans CaMKKI (named CKK-1) and CMK-1 phosphorylation on T179. Indeed, a T179A mutation on CMK-1 as well as a null mutation in the ckk-1 gene can both decrease CMK-1 nuclear accumulation at 28°C  identify remaining CMK-1 elements working as secondary NES and NLS and (ii) better understand the role played by T179 phosphorylation. Our results elucidate how the interplay between multiple, antagonistic pathways are integrated to control CMK-1 localization, and furthermore suggest new regulation opportunities that might be used in other neurons and contexts, in order for CMK-1 localization to integrate different signaling inputs.The role and the mechanisms controlling CMK-1 subcellular localization have been so far best characterized in FLP thermo-nociceptor neurons where CMK-1 regulates nociceptive habituation. Previous experiments conducted in transgenic animals expressing different CMK-1 mutants in FLP with constitutive nuclear or cytoplasmic localization notably showed that CMK-1 activity in the cytoplasm promotes heat avoidance response, while its activity in the nucleus acts antagonistically to reduce heat avoidance response . In wildNLS71-78 , constit at 28°C . However288-294 as the major determinant of CMK-1 cytoplasmic localization at 20°C, and NLS71-78 as the main drive for CMK-1 nuclear entry after calcium increase at 28°C  are deactivated. In order to identify these secondary elements, we conducted further mutagenesis analyses in the CMK-1 NLS71-78; NES288-294 double mutant background.Our previous studies identified NES at 28°C . A serie at 28°C . The prepe CMK-1 . The sim71-78; NES288-294 double mutant. We previously identified NLS297-307 as a secondary canonical NLS making a minor contribution to the heat stimulus-evoked nuclear accumulation of CMK-1  mutant protein displays impaired activity-dependent nuclear accumulation . We recaunc-68(dom13) mutation, which chronically elevates FLP cytoplasmic calcium levels at both 20°C and 28°C to promote CMK-1 nuclear accumulation (unc-68(dom13) mutation was fully abolished by the T179A mutation at either temperature  an indirect pathway, through which CaM binding is required prior to phosphorylation of T179, which in turn promotes the nuclear accumulation of CMK-1.At 20°C, the T179D mutation could also favor CMK-1 nuclear localization in a W305S mutant background abolishing CaM binding . These o71-78 or NES288-294 because these two elements are the main regulators of stimulation-dependent CMK-1 localization. In the first model, T179 phosphorylation would act by masking NES288-294. In the second model, T179 phosphorylation would act by unmasking NLS71-78.Our next goal was to determine how T179 phosphorylation could promote CMK-1 nuclear accumulation. We tested two hypothetical models implicating either NLS288-294 mutations. We found that T179A markedly promoted CMK-1 cytoplasmic retention at 28°C regardless of the NES288-294 mutation . Therefore, it is unlikely that the T179A mutation predominantly causes CMK-1 cytoplasmic translocation by aberrantly revealing NES288-294 at 28°C.To test the first model, we examined the interaction between T179A and NES71-78 mutations. The T179D nuclear translocation effect at 20°C was abolished in a T179D; NLS71-78 double mutant  mutant protein at 28°C was not more nuclear than CMK-1(wt), but actually significantly more cytoplasmic with a ratio similar to that of CMK-1(T179D) at 20°C  and microinjected in the worm gonad according to a standard protocol . We usedEntry plasmids containing specific promoters were constructed by PCR from N2 genomic DNA, with primers flanked with attB4 and attB1r recombination sites and cloned into pDONR-P4-P1R vector (Invitrogen) by BP recombination. Plasmids and primer sequences are reported in the Entry plasmids containing specific coding DNA sequences were constructed by PCR from N2 cDNA with primers flanked with attB1 and attB2 recombination sites and cloned into pDONR_221 vector (Invitrogen) by BP recombination. Plasmids and primer sequences are listed in cmk-1 coding DNA sequences) were amplified with the KOD Hot Start DNA Polymerase . Primers were phosphorylated in 5’ and were designed to contain the desired point mutation(s) and to hybridize in a divergent and back-to-back manner on the plasmid. After electrophoresis, linear PCR products were purified from agarose gel (1%) with a Zymoclean-Gel DNA Recovery kit (Zymo Research) and circularized with DNA Ligation Kit ‘Mighty Mix’ (Takara). The plasmid templates, primer sequences, and names of resulting mutation-carrying plasmids are reported in All the point mutations were generated by inverse PCR-based site-directed mutagenesis . In brie3 , transferred on a glass slide, and covered with a coverslip. Imaging was carried out during the next 5 min.Worms were synchronized according to standard procedure with hypochlorite treatment and grown at 20°C. First-day adult animals were collected from NGM bacterial plates with distilled water, transferred to 1.5 ml microcentrifuge tubes, and washed once with distilled water. Twenty µl of a dense worm suspension were transferred to PCR tubes and incubated in a thermocycler at 20°C or 28°C for 1.5 hr. Prior to imaging, worms were immobilized with the addition of NaNFLP images to measure the nuclear/cytoplasmic ratio were acquired in a Zeiss Axioplan2 fluorescence microscope, with a 40×  objective and constant illumination parameters.For CMK-1 subcellular localization analysis, the intensity of fluorescence was first measured for each neuron in three regions of interest (ROIs): nucleus, cytoplasm, background. The nuclear/cytoplasmic ratio was calculated as (nucleus-background)/(cytoplasm-background). A ratio >1 indicates a nuclear accumulation of CMK-1, while a value <1 a cytoplasmic biased ratio. All three ROIs were ellipses of the same area. The background ROI was defined in a worm region close to the neuron to take autofluorescence into account. Nuclear and cytoplasmic ROIs were defined based on the mNG green signal and the shape of the neuron, via a previously validated procedure .https://www.jamovi.org). A visual inspection of Q-Q plots suggested that all datasets were following a normal or nearly normal distribution, but some datasets returned significant results with the Shapiro-Wilk test (p<0.01). For that reason, we conducted robust ANOVAs. No outlier was excluded.Comparisons were made with one-way and two-way ANOVAs followed by Bonferroni post hoc tests using Jamovi (The jamovi project (2021). jamovi (Version 1.6) [Computer Software]. Retrieved from No specific a priori power analysis was performed. N determination was based on previous similar experiments and adjustments made based on the actual measure variability. All Ns are mentioned in the figures. Ns represent independent animals . At least two independent transgenic lines  were scored for each genotype, each on at least three different experimental days. Wild type control was systematically run in parallel. Thermal stimulation conditions at 20°C and 28°C were run in parallel during imaging session. Some wild type control data are reused across some figure panels for datasets matching the same acquisition sessions.Strains and plasmids generated during the study will be made available upon request. In this follow-up study to their previous work , the authors report additional insights into a complex network of nuclear export (NES) and nuclear localisation (NLS) sequences in the CaM kinase‐1 (CMK‐1), which is implicated in the plastic regulation of the FLP thermo‐nociceptors neurons. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:C. elegans thermal nociceptor" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Richard Aldrich as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Shawn Xu (Reviewer #2).Thank you for submitting your article "Multiple antagonist calcium-dependent mechanisms control CaM Kinase-1 subcellular localization in a The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. Overall, we are very excited about the work presented in this manuscript, but we think that a few revisions are needed to better support your major claims. We believe that the requested revisions are very feasible and can be addressed with some new experiments/data. Please address the points below and submit a point-by-point response alongside your revised manuscript.Essential revisions:Reviewer #1:1) The authors identify a secondary NLS/NES structural element-pair that is, according to their claim, a basal, stimulation-independent structural modulator of CMK-1 subcellular distribution. The presented model suggests that this new NLS/NES pair regulates a basal shuttling rate of CMK-1 between the cytosol and the nucleus. However, this is not per se demonstrated in the current version of the manuscript. I suggest applying FLIP or iFRAP to demonstrate that these secondary elements are indeed regulating a basal nucleo-cytoplastic shuttling of CMK-1 in FLP neurons.2) It is not clearly described how the authors ended up studying S325. Is this the only strongly predicted phosphorylation site in the whole CT part of the protein? Did the Authors specifically perform phosphorylation-site prediction to define candidate sites?3) The axis of supplementary Figure 4 is clipped and some parts of the violin plots are missing.Reviewer #2:1) Figures 1-3 share some of the same control datasets. For example, it seems like the nucleus/cytoplasm ratios of CMK-1 wild-type animals at 20{degree sign}C and 28{degree sign}C are from the same trial of experiments in Figure 1B and 2C. If data are re-used in different figures, this should be clearly clarified and stated in figure legends.2) Based on theory or data, it would be useful for the authors to be more specific about the extent to which a prolonged temperature rise and mutations in functional domains of CMK-1 would be expected to alter worms' FLP-related behaviors like heat avoidance reversals and heat adaptation. It would be helpful if the authors could include at least one functional behavioral assay to validate their findings in terms of the signaling pathway.3) Line 106: In silico analysis tools first appearing in the manuscript should be briefly explained by a few sentences or a citation. Besides, a detailed explanation of this method is missed in the "Materials and methods" section.4) Typos. Some examples:Line 62: "through which" instead of "though which".Line 65: "decrease CMK-1 nuclear accumulation".Line 194/ 218/ 236: in the three sub-headings "T179D phosphor-mimic mutation" could be replaced by "T179 phosphorylation". Reviewer #1:1) The authors identify a secondary NLS/NES structural element-pair that is, according to their claim, a basal, stimulation-independent structural modulator of CMK-1 subcellular distribution. The presented model suggests that this new NLS/NES pair regulates a basal shuttling rate of CMK-1 between the cytosol and the nucleus. However, this is not per se demonstrated in the current version of the manuscript. I suggest applying FLIP or iFRAP to demonstrate that these secondary elements are indeed regulating a basal nucleo-cytoplastic shuttling of CMK-1 in FLP neurons.We agree that it would be very nice to be able to expand the analysis of our model with quantitative kinetic data. We have actually invested a lot of time and energy (and money) in attempting to establish in vivo FRAP in our lab over the last year, but we must admit we reached our limits. There are a lot of technical difficulties associated with the need to work with a moving whole animal preparation (and not immobile cells as used in the vast majority of FRAP studies). We tried to establish a micro-fluidic system, in order to avoid the need for interfering pharmacological agents, but we were never able to obtain sufficient stability of the head region. Furthermore, to obtain conclusive data in these tiny worm neurons with very limited signal area and relatively weak signal-to noise ratio, we would need to photobleach an entire compartment and measure its ‘replenishment’ kinetics over very long periods. But we were not so far able to achieve this with our current resources. Therefore, in the scope of the present revision, the suggested experiments are unfortunately out of our reach. To nevertheless address this concern, we revised the Result/Discussion to emphasize the need for quantitative kinetics analyses in future studies. The relevant text reads as follows:“Our studies have identified multiple functional intrinsic elements, including NES, NLS and phosphorylation sites, and provide a more complete picture of their interplay in FLP thermo-nociceptor neurons. These elements have antagonistic effects, favoring either export or import, and in the case of the T179 phosphorylation, favoring both export and import. We speculate that the dual regulatory role of T179 phosphorylation in promoting bidirectional CMK-1 translocation across the nuclear envelope could be at the origin of cell stimulation-dependent CMK-1 shuttling cycles. Importin/exportin based transport is an energy consuming process. Therefore, if a single phosphorylation event promotes the two antagonistic pathways, it must also come with some benefits. Considering the cytoplasmic localization of CKK-1, we propose that a dynamic nucleo-cytoplasmic shuttling might contribute to ensure that the T179 phosphorylation status of CMK-1 is frequently “refreshed”, in order for the nuclear pool of CMK-1 to reflect the current activation status of CKK-1 in the cytoplasm. In order to test this hypothetical model, future studies will need to quantify the actual shuttling kinetics of wild type and mutant CMK-1 at different temperature. Furthermore, the whole process is likely to be also influenced by the activity of phosphatase(s) responsible for CMK-1 dephosphorylation on T179. In order to obtain a full picture of the phosphorylation-dependent CMK-1 localization regulation, future studies will also need to identify these phosphatases and determine their subcellular locus of action in vivo.”We also carefully scanned our manuscript to make sure we were very cautious in our statements about the kinetic aspect and the baseline shuttling aspect. We made some text adjustments in the abstract and in the Result/Discussion section.While the question of the shuttling kinetics will unfortunately remain open, we believe that our manuscript now fairly addresses this aspect without overselling our speculations about the potential role of activating two opposite drives. In addition, we want to stress that, the absence of shuttling kinetic data do not affect our main conclusions about the multiplicity of the antagonist regulatory mechanisms controlling CMK-1.2) It is not clearly described how the authors ended up studying S325. Is this the only strongly predicted phosphorylation site in the whole CT part of the protein? Did the Authors specifically perform phosphorylation-site prediction to define candidate sites?There are only 5 potential phosphosites in the considered C-terminal region of CMK-1. S325 and S327 received a high score (>0.7) using NetPhos-3.1. S327 prediction is not associated with any particular kinase, whereas S325 is a potential target of kinases known to regulate neuronal function (including PKA and CaMkinase themselves), and is part of the prevalent RXXS motif. We chose to follow up on S325 only for these reasons. We didn’t make any claim about the other residues.et al. 2019 10.1371/journal.pbio.3000341). These were the only criteria that drove our initial intention and ultimately, we were lucky to follow this fortunate intuition.In the revised manuscript, we have revised the text to cite the NetPhos-3.1 prediction tool and the very interesting article by Bradley and colleagues supporting the prevalence of the RXXS motifs (Bradley 3) The axis of supplementary Figure 4 is clipped and some parts of the violin plots are missing.We expanded the X-axis range in the revised supplementary figure 4 to make sure all the datapoints are visible.Reviewer #2:1) Figures 1-3 share some of the same control datasets. For example, it seems like the nucleus/cytoplasm ratios of CMK-1 wild-type animals at 20{degree sign}C and 28{degree sign}C are from the same trial of experiments in Figure 1B and 2C. If data are re-used in different figures, this should be clearly clarified and stated in figure legends.We complemented the legends of the relevant figures  accordingly for more clarity.2) Based on theory or data, it would be useful for the authors to be more specific about the extent to which a prolonged temperature rise and mutations in functional domains of CMK-1 would be expected to alter worms' FLP-related behaviors like heat avoidance reversals and heat adaptation. It would be helpful if the authors could include at least one functional behavioral assay to validate their findings in terms of the signaling pathway.We agree that it is important that the present mechanistic follow-up study describes a physiologically relevant process. There is substantial evidence it is the case. Indeed, previously published data demonstrated that the subcellular localization of CMK-1 is essential to adjust FLP-dependent heat avoidance. In our two previous studies , we examined a series of mutants and exogenous chimeric proteins  and clearly showed that CMK-1 nuclear localization is necessary and sufficient to reduce heat avoidance, whereas CMK-1 cytoplasmic localization is necessary and sufficient to promote heat avoidance. Therefore, it is reasonable to expect that any new mechanisms that we show to be able to modulate CMK-1 localization is likely to be of potential physiological relevance. As for the new elements discovered here, it is unfortunately very difficult to design a clearly interpretable behavioral experiment that would add any further solidification on this aspect. First, regarding the new NES/NLS pair, it is a set of secondary elements, whose impact only becomes very salient when the primary NES/NLS pair is impaired. Performing behavioral experiments in a background combining three or more mutations would become tricky to interpret. Any result supporting our model would be a very weak support, whereas we could easily justify a result not fulfilling our expectations, because of a severe chronic miss-regulation of CMK-1. Second, regarding the T179 affecting mutations, they are even more challenging to work with. Indeed, while T179A and T179D produce major effects on CMK-1 localization on their own, they also create strong loss and gain of CMK-1 kinase activity, respectively. Regardless of the behavioral phenotypes we would obtain, we would not be able to disentangle the contribution of kinase activity versus localization.To follow up on this comment, we modified the introduction to more explicitly refer to past experiments having demonstrated the functional importance of CMK-1 localization.3) Line 106: In silico analysis tools first appearing in the manuscript should be briefly explained by a few sentences or a citation. Besides, a detailed explanation of this method is missed in the "Materials and methods" section.This a very good point. In the revised manuscript, we have introduced the suggested information under a new specific Method section subheading, and also provide citation about these tools in the Results and Discussion section.4) Typos. Some examples:Line 62: "through which" instead of "though which".Line 65: "decrease CMK-1 nuclear accumulation".Line 194/ 218/ 236: in the three sub-headings "T179D phosphor-mimic mutation" could be replaced by "T179 phosphorylation".We corrected these typos and take another deep look at the manuscript to remove remaining typos.


The genomic distribution of cleavage and polyadenylation (polyA) sites should be co-evolutionally optimized with the local gene structure. Otherwise, spurious polyadenylation can cause premature transcription termination and generate aberrant proteins. To obtain mechanistic insights into polyA site optimization across the human genome, we develop deep/machine learning models to identify genome-wide putative polyA sites at unprecedented nucleotide-level resolution and calculate their strength and usage in the genomic context. Our models quantitatively measure position-specific motif importance and their crosstalk in polyA site formation and cleavage heterogeneity. The intronic site expression is governed by the surrounding splicing landscape. The usage of alternative polyA sites in terminal exons is modulated by their relative locations and distance to downstream genes. Finally, we apply our models to reveal thousands of disease- and trait-associated genetic variants altering polyadenylation activity. Altogether, our models represent a valuable resource to dissect molecular mechanisms mediating genome-wide polyA site expression and characterize their functional roles in human diseases. The authors develop deep learning models to identify genome-wide polyA sites at nucleotide resolution and calculate site strength. They further examine genomic parameters regulating site usage and reveal genetic variants altering polyA activity. One gene can contain multiple polyA sites leading to alternative polyadenylation (APA) events that generate RNA isoforms with different 3’ untranslated regions (3’UTRs) or coding sequences. Transcript isoforms with the same coding sequence but different 3’UTRs can show variable translation efficiency, stability, and subcellular localization. APA plays important regulatory roles in gene expression during various physiological processes, such as oncogenesis, development, and stress response7.Cleavage and polyadenylation is an essential step of mRNA maturation and comprises two steps: endonucleolytic cleavage of a nascent transcript followed by polyA tail synthesis. The process is crucial for transcription termination, and the underlying regulatory mechanisms are tightly coupled with gene transcription and RNA splicing9. These RNA-binding proteins form subcomplexes that recognize unique cis-regulatory elements , located 10~30 nucleotides (nt) upstream of the cleavage site, is recognized by the cleavage and polyadenylation specificity factor (CPSF) complex. The U-rich and GU/CU-rich elements, located downstream of the cleavage site, are bound by the cleavage stimulation factor (CstF) dimer. Several other motifs play regulatory roles, including UGUA (bound by cleavage factor I (CFI)) and U-rich elements (recognized by FIP1) around the PAS, as well as G-rich elements (bound by hnRNPh/f) downstream of CstF binding. Although these motifs are known to promote polyA site formation, there is still a lack of quantitative measurements of their position-specific importance and the crosstalk among the motifs. Building a quantitative model would be valuable to study the physiological roles of polyadenylation because genetic variants altering these motifs can regulate human gene expression and disease progression. For example, mutations of the PAS AAUAAA in genes such as TP53 (associated with cancer susceptibility), HBA2 , and INS  can cause the downregulation of protein production and lead to disease initiation14.The mammalian polyadenylation complex is composed of ~20 core and 60 auxiliary proteins16. Efficient polyadenylation is essential for proper transcription termination especially when the tandem intergene distance is short17. Otherwise, the run-on of RNA polymerase II can interfere with the downstream gene expression. Quantitative modeling of polyA site expression which considers the genomic context would provide molecular insights into site optimization across the genome.Besides the motif strength, polyadenylation activity is tightly regulated by the local gene structure. During genome evolution, the localization of putative polyA sites must be optimized within the genomic context. Due to the competition with splicing, intronic polyadenylation happens when the splicing activity is weak )cis-regulatory motifs. Although previous studies have developed several models to predict polyA site occurrence and strength, these models have their limitations. For example, the DeepPASTA model18 calculates the probability of a sequence being a polyA site, but it does not perform nucleotide-level cleavage site prediction. The APARENT20 and PolyApredictors21 models predict the polyA site strength and cleavage profile, but they were restricted to database-annotated polyA sites and anchored with the predefined PAS (e.g. AAUAAA/AUUAAA) for the prediction. To address these technical limitations, here we developed the first deep learning model which performs unbiased identification of putative polyA sites at nucleotide resolution across the human genome and a separate model to calculate the site strength. The models quantitatively revealed the crosstalk among cis-regulatory motifs in determining polyA site formation and cleavage heterogeneity. Furthermore, using logistic regression, we examined genomic parameters determining polyA site expression in introns and terminal exons. Finally, we demonstrated that our models can be used to characterize disease/trait-associated genetic variants regulating polyadenylation activity.In this study, we aimed to develop a computational approach to unbiasedly identify genome-wide putative polyA sites and then examine the local genomic parameters determining their expression. The deep learning approach is well suited to studying the motif “grammar” underlying polyadenylation. Convolutional neural networks can quantitatively capture the dynamic interactions among 23. We chose this method because it resolved the internal priming issue and its polyA site supporting (PASS) reads reliably quantify genome-wide polyA site expression at nucleotide resolution  dataion Fig. . Out of For the PolyaID model training, we used the 240 nt sequences around the 3’READS-identified polyA sites as positive examples, and we included the sequences from each position from −25 to +25 nt surrounding the maximum cleavage site to augment the training dataset. Our negative examples included random genomic sequences, shuffled transcript sequences, and neighboring sequences 50–100 nt distant from polyA sites that did not contain any PASS reads in the middle 50 nt regions. Different from previously published models, we included many site-adjacent sequences in the positive (<25 nt from the cleavage site) and negative (50–100 nt away from the site) sets, respectively. This design was important for our algorithm to robustly identify polyA sequences showing cleavage in the middle region. And the model is not biased to any predefined PAS types.22.The sequence modeling unit of our PolyaID model contained one convolutional layer and one bidirectional long short-term memory (LSTM) layer Fig. . The mod18, APARENT19, APARENT220, and PolyApredictors21), PolyaID showed higher AUROC values in classifying polyA sequences with different PAS signals vs. random nucleotides   vs. random nucleotide sequences  of ~0.974 on the split training, validation, and testing datasets Fig. . The are26. Our PolyaID predicts the cleavage probability at genomic As while APARENT did not allow cleavage happening at As  in the PolyaID-predicted classification probability and the PolyaStrength score. To identify hexamers contributing significantly to the genome-wide polyA sites, we calculated the importance score of a hexamer in a location by summing their –(Δlog-odds) values   Fig. . Next, wNP) Fig. . BesidesNP) Fig. . Sites wNP) Fig. . But genNP) Fig. . Indeed,NP) Fig. . AltogetThe cleavage profiles of different polyA sites can be quite variable. For some sites, the cleavage events are heterogeneous and span a region >15 nt, while for others, the cleavage site is quite precise <4 nt Fig. . CurrentWe next compared polyA sites with high vs. low entropy values. The polyA sites showing higher heterogeneous cleavage tend to have multiple AAUAAA sequences. 12% of high entropy polyA sites contained ≥2 AAUAAA, while only 4.5% of low entropy sites did Fig. . We did cis-element occurrence differences in these subregions, comparing high vs. low entropy groups. For polyA sites showing high heterogeneous cleavage, the AAUAAA PAS and downstream U-rich elements were more likely to be located far away from the cleavage site (>30 nt)  contain only one AAUAAA. Next, we examined the differences between these sites. The high and low entropy groups showed comparable PolyaID classification probabilities Fig. , but thent) Fig. . In addi19 and found that polyA sites containing multiple AAUAAA and those with longer distances between AAUAAA and downstream U-rich or GU/CU-rich elements showed higher cleavage heterogeneity  elongation, the order and spacing between tandem polyA sites in terminal exons is expected to regulate their usage. Here, we aimed to quantify the genomic parameters regulating terminal exonic site usage Fig. . Using 331.Next, we selected proximal polyA sites with comparable absolute and relative strength to the paired distal site and examined other genomic features distinguishing the highly vs. lowly used ones Fig. . First, P = 0; Fig. We built a logistic regression model to classify the highly vs. lowly used proximal polyA sites. The features described above showed significant contributions to the classification  associated with significant differential usage of the sites across human tissues33, the UK BioBank34, and GWAS Catalog35 on polyadenylation activity. If a known disease/trait-associated variant impacted any of the cis-regulatory elements that we identified  is a driver of neuronal disorders such as epilepsy38, and the gene contained a synonymous variant  in the coding region of the terminal exon  contains a missense variant in the coding region  of its terminal exon and creates the PAS AAUAAA. This variant is associated with multiple diseases, such as muscular dystrophy, nail dystrophy, and skin disorders41. The expression of the de novo polyA site would generate the non-stop codon RNA isoform, which cannot produce the full-length protein  and created an AAUAAA PAS, which is predicted to form a de novo polyA site downstream , UGUA recognized by CFI (−37 nt), U-rich elements bound by FIP1 (−15 nt), GU/CU-rich (+10 nt) and U-Rich motifs (+18 nt) bound by CstF, as well as G-rich elements bound by hnRNPs (+37 nt). Small distance changes between the motif pairs (<10 nt) can result in differential importance scores, indicating that subtle variance in subcomplex configuration regulates polyadenylation activity. A published biochemical study focused on positional crosstalk between UGUA and AAUAAA elements27 and showed that UGUA showed the strongest activity promoting polyadenylation when it is located a short distance (20 nt) upstream of AAUAAA. This finding confirms the accuracy of our analysis. Our modeling is not restricted by designed reporters but quantifies all positional possibilities among the motifs without labor-intensive biochemical assays. The characterization of positional effects among motif pairs may enhance the structural understanding of polyadenylation complex formation.Using the PolyaID and PolyaStrength models, we quantitatively measured the positional interactions among 43. Here we showed that the cleavage heterogeneity of the polyA sites is regulated by two different mechanisms: (1) the number of PAS AAUAAA; and (2) the relative distance between the PAS AAUAAA and downstream GU/CU- or U-rich elements. A shorter distance between CPSF and CstF binding can lead to a more uniform/stable complex configuration and results in more consistent cleavage by the endonuclease CPSF7344. Our PolyaID model captured this regulatory mechanism and can be used to predict and control the cleavage events when engineering polyA sites.Studies showed that RNA isoforms generated by heterogeneous cleavage at a polyA site can show quite variable stability levels due to the regulation of RNA secondary structure formation, even when they only have a few nucleotide differences at the 3’-end15. Previous studies showed that disrupting the splice sites or using anti-sense oligonucleotides to block U1 snRNP recruitment can increase intronic polyadenylation46. These results are in line with our findings of the competition between splicing and intronic polyadenylation. Here, we developed quantitative models to capture the regulatory parameters and calculate the probability of a site being used.Importantly, our PolyaID model achieved unbiased identification of genome-wide putative polyA sites at nucleotide resolution for the first time. More than three million putative sites were identified across gene regions based on the nascent RNA sequences with an average of 2 sites per kb. As introns are 30-fold longer than exons, 89.1% of putative polyA sites are located in introns. 98.5% of these intronic sites were not expressed, as aberrant expression of these sites could generate truncated proteins. The optimization of splicing signals is crucial for suppressing intronic polyadenylation47. For example, the CSTF3 gene contains an intronic polyA site associated with a weak 5’SS conserved across vertebrates, which plays fine-tuning roles in full-length protein expression16. The immunoglobulin M gene uses intronic polyadenylation to generate two RNA isoforms encoding membrane-bound and secreted proteins with different C-terminal domains48. It will be interesting to apply our models to examine the coevolution of intronic polyadenylation and surrounding gene structures across species, which may distinguish the functionally conserved vs. randomly occurring sites.Although intronic polyadenylation is generally unfavored, some genes express functional RNA isoforms using this mechanismcis-regulatory elements in 3’UTRs, such as binding sites for microRNAs and RNA binding proteins. This regulation fine-tunes RNA stability and translation efficiency across biological conditions. Besides polyA site strength, our quantitative modeling showed that site usage is correlated with the tandem site distance, the size of the last intron, and the distance to the next downstream antisense gene. Our model achieved the classification of highly vs. lowly used terminal exonic sites with an AUROC = 0.937. The small fraction of misclassification could be attributed to the regulation of RNA stability or cell type-specific regulation. Our models were trained mostly based on mature RNA expression, although a few 4sU-labeled nascent RNA libraries were included. The principles we learned from this study represent general polyadenylation regulation.APA in 3’-terminal exons regulates the occurrence of 49, more genes showed higher usage of proximal polyA sites in embryonic-like cells than neuronal-like cells (1409 vs. 226 genes)  RNA-seq data with our PolyaID/PolyaStrength models could comprehensively identify APA events and reveal molecular mechanisms underlying biological process- or cell type-specific regulation. Using the 3’READS data, we examined APA regulation across five different cell types, including cerebellum tissue, neuronal stem cells (NSCs), embryonic stem cells (ESCs), as well as two placental cell lines HTR8 and JEG3. Using principal component analysis of sites showing significant APA regulation across the cells/tissues (see “Methods” for details), we observed that cerebellum and NSCs  show similar APA profiles, while ESCs, HTR8, and JEG3 cells (named as embryonic-like cells) were clustered together , we used random intergenic sequences, shuffled transcript sequences, and neighboring sequences 50–100 nt distant from a polyA site that did not contain any PASS reads in the middle 50 nt regions. With these site-neighboring sequences in the negative examples, our algorithm only predicts positive polyA sequences showing cleavage in the middle region.Our PolyaID model contains two output branches: the classification probability and the cleavage probability profile. For model training, the classification score for positive sequences was 1, while negative sequences used the score 0. We used 3’READS within +/−25 nt of the positive site to represent the cleavage vector (length = 50). This vector sum is normalized to 1 to show the cleavage probability at each position. Negative controls were assigned a constant cleavage probability vector at a background value of 0.02.For the input to the PolyaID model, we converted the 240 nt sequences (positive and negative examples) into a (4 × 240) matrix using one-hot encoding. Each column of the matrix represented a position in the sequence, and each row corresponded to the nucleotide A, C, G or U. A 1 was marked if the nucleotide was present in the sequence position, and all others were marked with 0. This creates a numerical representation of the sequence fed to the model. Our sequence-modeling unit was composed of a convolutional layer and a bidirectional LSTM layer. The window size for learning the sequence composition was set to 8. The output layer contains two units: the classification probability calculating the likelihood a sequence is a polyA site, and the cleavage probability vector (length = 50). Details of the layers used in this model are described in Supplementary Data We use the binary cross-entropy loss function for the classification branch (Eq. 1):We used the Kullback-Leibler divergence loss function for the cleavage branch (Eq. 2):The loss functions were implemented out-of-box in TensorFlow2. Data were fed into the model in batch sizes of 100 with a learning rate of 0.001 and optimized using the Adam optimizer with Nesterov momentum.The dataset was randomly partitioned into 80% training, 10% validation, and 10% holdout test splits, with positive and negative controls balanced. The loss functions for each of the output branches were equally weighted during training. Training was monitored using the loss function values and the accuracy of predictions. Several models were trained in replicate with different random initializations, and the most representative model was chosen. The final model was trained for four epochs, after which it attained the minimum loss on the validation set.We assessed the quality of our trained model using the performance on a holdout test set. Based on PolyaID prediction, we calculated the classification probability as well as the cleavage probability for each site. Classification performance was shown using the AUROC and AUPRC from the Python library scikit-learn.26. If there are multiple genomic As at the end of mapped reads, here we assigned the reads to the first A. Our approach is different from the APARENT model where they assigned the reads to the last non-A site. As a result, the cleavage site predicted by the APARENT model does not happen at the As, while our PolyaID model predicts the cleavage probability among the As.The PASS reads from 3’READS data revealed near single-nucleotide resolution of cleavage sites with one limitation. We cannot distinguish the last nucleotide As from genome sequences vs. those generated by polyadenylation. Actually, many cleavage events tend to happen within A-rich regions or after a CA dinucleotideFor the cleavage probability vector, we subtracted the background probability of 0.02 from the predicted vector and then renormalized the distribution to the sum of 1. The performance of cleavage probability vector prediction was assessed by comparing the observed and predicted mean cleavage positions (MCP), which were calculated from the following dot product.i from a vector (length = 50), suppose the cleavage probability is pi. The MCP m was calculated as follows (Eq. 3).For position The MCP metric determines the most likely cleavage position weighted by the cleavage probabilities in that region and quantifies the agreement between the shape of observed probabilities from the 3’READS data and predicted probabilities from our model.Using a scanning window across the genome with one nucleotide per step, we performed a PolyaID prediction using the 240 nt nascent RNA sequence. As shown in Fig. We further grouped the putative polyA site based on their genomic locations: intronic, upstream exonic, terminal exonic, and extended terminal exonic sites. We examined whether these polyA sites are expressed (supported by ≥10 PASS reads). To compare the relative usage levels of the site types, we selected genes showing ≥2 expressed sites in terminal exons or extended regions and calculated the usage level of a site as the ratio between the number of PASS reads supporting the site vs. the sum read counts of the top two expressed sites in the (extended) terminal exons.We used the relative expression of APA sites located in terminal exons to represent their usage levels. The relative usage of a polyA site is calculated as the ratio (log-odds transformed) between the number of reads supporting the site and the summed reads from the highest and second-highest expressed sites in the same gene. We did not use the total read number from a gene as the denominator because there are different numbers of expressed polyA sites across genes.i is ni, its relative usage ui and the log-odds transformed value oi were calculated as (Eq. 4):Supposing the supporting read number for site The polyA sequences (240 nt) were converted into a (4 × 240) matrix using one-hot encoding, the same as for the PolyaID model. For the Sequence-Modeling unit, we also used a convolutional layer followed with a bidirectional LSTM layer, but their parameters are different from those of PolyaID and are described in  in classifying polyA sequences vs. random nucleotides. We split the sites from the PolyaID holdout test dataset based on their PAS types: AAUAAA, AUUAAA, or other variants, respectively. And we randomly sampled an equal number of negative controls to balance. The same positive and negative datasets were used to compare the performance among the models. We calculated the AUROC and AUPRC values using the predicted PolyaID classification probability, DeepPASTA classification probability, APARENT/APARENT2 isoform abundance, and PolyApredictors expression to compare classification performance. In Supplementary Fig. We compared the performance of PolyaID vs. previously published models 19. PolyA_DB sites were also defined by 3’READS. We selected 36,701 sites supported by mean reads per million (RPM) ≥ 3 as positive examples and the same number of random nucleotide sequences as negative examples to evaluate the performance of site classification using PolyaID, DeepPASTA, APARENT, APARENT2, and PolyApredictors. We picked 6,871 terminal exonic polyA site pairs with baseline expression RPM ≥ 1.5, with ≥8-fold expression difference, and ≥100 nt apart to evaluate the performance of PolyaID, APARENT, APARENT2, and PolyApredictors in classifying the strong vs weak polyA sites. For the APADB model, the polyA sites were defined by massive analysis of cDNA ends sequencing (MACE reads). We selected 48,860 sites supported by ≥10 MACE reads as the positive examples for evaluate the site classification, and used 8,000 paired terminal exonic polyA sites with baseline expression ≥5 MACE reads, with ≥8-fold expression difference, and ≥100 nt apart to evaluate the performance of classifying strong vs. weak sites.Our above analyses were based on the polyA sites defined in this study. We also evaluated the model performance using polyA sites defined in published databases including PolyA_DB v319 in which they introduced hundred thousands of random nucleotides into the polyA site sequence, and quantify the site expression using deep sequencing. The relative transcript expression levels can be used to measure the relative strength of the polyA sites. We analyzed the “HSPE1” library which was used for their model evaluation in the published paper. To remove the false-positive sites from oligo(dT) internal priming, we only retained sequences with PolyaID-predicted classification probability ≥0.5 for further analyses. We grouped 337,044 sequences into 5 bins based on their supporting read numbers, and evaluated the performance of the algorithms to classify the top highly vs. lowly expressed (group 5 vs. group 1) polyA site sequences.We also analyzed a published MPRA datasetWe employed a motif disruption approach to systematically quantify the importance of a hexamer to the PolyaID and PolyaStrength prediction for a polyA site sequence. For each hexamer occurrence in a polyA site sequence, we replaced it with randomized nucleotides 100 times while keeping the remaining sequences the same. We performed predictions for both the original and modified sequences. Then the effect of motif disruption was quantified using the median change in the log-odds of the PolyaID-predicted classification probability or PolyaStrength score from the 100 replacements.To identify motifs driving genome-wide polyA site formation, we performed the above-described analyses for 32,682 well-expressed polyA sites with ≥100 PASS reads and usage levels ≥5%. The usage level of a polyA site was calculated as the ratio of PASS reads supporting the site vs. the total PASS reads assigned to the gene. For each position from −120 nt to +114 nt surrounding polyA sites, we calculated the sum importance score of a hexamer in that position as the sum change in log-odds classification probability or strength. We also calculated per site importance score for each hexamer by dividing the sum score by the frequency.cis-regulatory elements contributing to genome-wide polyA site identification, including AAUAAA and PAS variants22, UGUA, U-rich, GU/CU-rich, and G-rich motifs. U-rich elements were defined as those with 5 Us in a hexamer, with a U required in the first position. G-rich elements were defined as those with 5 Gs in a hexamer, with a G required in the first position. GU/CU-rich hexamers contained GUGU, UGUG, GUCU, UGUC, CUGU, or UCUG. We reasoned that the vast majority of motifs located >40 nt upstream or downstream of polyA sites are not important for polyA site formation. We used the sum importance scores from these regions as the background values. The 99.99th percentile score from this background distribution was used as the false discovery rate threshold. The hexamers passing this cutoff in any 40 nt window using either PolyaID or PolyaStrength importance were considered important for genome-wide polyA site formation. We merged motifs recognized by components of the polyadenylation machinery into families for further analysis.We next analyzed cis-elements around genomic polyA sites. To this end, we focused on analyzing 13,876 well-expressed polyA sites (supported by ≥100 reads and usage level ≥5%) with an AAUAAA 10-30 nt upstream and either a U-rich or GU/CU-rich motif within 30 nt downstream of the max cleavage site. For these sites, we then determined if they contained any optimally positioned motifs: UGUA bound by CFI 0-40 nt upstream of AAUAAA, U-rich motifs bound by FIP1 between the cleavage site and AAUAAA, G-rich elements recognized by hnRNP F/H within 50 nt downstream of the CstF binding site, or a second CstF motif. We analyzed the frequency of motif combinations, their PolyaStrength scores, the corresponding gene expression calculated by summed 3’READS of the genes, and their site type . We performed gene ontology analyses using the genes containing sites with ≥5 optimally positioned motifs using the DAVID program52.We next examined the differential compositions of n and the read count in position i is mi, we calculated the fraction of reads pi=mi/n. For the PolyaID prediction, we used the predicted cleavage probability pi for position i. We then calculated the entropy value E using the cleavage vector distribution as follows (Eq. 6):We used the entropy value to quantify the 3’READS-observed and PolyaID-predicted cleavage heterogeneity. For the polyA sites identified by 3’READS, we selected highly expressed sites supported by ≥100 reads in order to reliably quantify their cleavage distribution. We obtained the 3’READS distribution from the −25 to +24 nt surrounding the representative cleavage site. Supposing the total read number from the polyA region is The entropy values were used to separate sites with very clear cleavage sites  and those with heterogeneous cleavage .cis-regulatory element occurrence differences between the low vs. high entropy groups using the Chi-squared test. We also examined the motif enrichment by comparing the fraction of sites containing the motif of interest in each region using the two-sided two proportions test in the low vs. high entropy groups.We next examined the regulatory motifs driving the differences between high vs. low entropy sites. The entropy values calculated using 3’READS data and PolyaID-predicted cleavage were well correlated. We selected sites that were in the top and bottom 20% of entropy values for both the observed and predicted cleavage profile for the downstream analyses. We divided the polyA sequences into four different regions: −120 ~ −31 nt; −30 ~ 0 nt; 1 ~ 30 nt; and 31 ~ 120 nt. For each region, we compared N = 676, moving AAUAAA–U-rich closer N = 635, moving AAUAAA–GU/CU-rich farther N = 949, and moving AAUAAA–GU/CU-rich closer N = 658. The increase of AAUAAA and U-rich (or GU/CU-rich) element distances resulted in higher entropy values, and the decrease of distances resulted in lower entropy values.Our above motif analyses showed that the distance between upstream AAUAAA (CPSF binding) and downstream U-rich or GUCU-rich elements (CstF binding) regulates the cleavage heterogeneity. We examined whether the PolyaID model could capture this regulation. To this end, we moved AAUAAA and U-rich or GU/CU-rich motifs farther apart or closer together by sliding them through the surrounding sequence, which was unchanged, and predicted the change in polyA site classification probability and cleavage profile. From sites containing AAUAAA upstream of the cleavage site and either U-rich or GU/CU-rich motifs downstream of the cleavage site, we selected those sites in the low entropy group where the motifs were close together (20–40 nt) or in the high entropy group where the motifs were farther apart (40–100 nt). The number of polyA sites in the analyses: moving AAUAAA–U-rich farther 19. As the study reported the sequencing reads supporting each cleavage position of a sequence, we used the distribution of these reads to calculate the entropy value to quantify the cleavage heterogeneity based on the method we used for the 3’READS analyses. We required the sites should be supported by ≥10 reads and retained only the sequences with PolyaID classification probability ≥0.5 to remove false positives from the Oligo(dT) internal priming. To examine the effect of multiple PAS regulating cleavage heterogeneity, we compared the entropy values of sequences with 1 AAUAAA vs. 2 located upstream of the maximum cleavage sites. Because there are many more sequences with only 1 AAUAAA vs. 2, we randomly sampled an equal number of sites with 1 AAUAAA to balance the data for comparison. To examine the impact of the distance between PAS and the CstF binding site in regulating cleavage heterogeneity, we analyzed the sites from the “Simple” and “AARS” libraries with only 1 AAUAAA. We selected these two libraries for analyses because they contain randomly located downstream U-rich or GU/CU-rich elements, while the distance between the PAS and CstF was fixed in other libraries. We grouped the sites based on the distance between the AAUAAA PAS and the first downstream U-rich or GU/CU-rich elements for comparisons.To validate the genomic parameters regulating polyA site cleavage heterogeneity, we analyzed the MPRA dataIntronic polyadenylation is generally suppressed because of its competition with splicing. We developed a logistic regression model to determine whether an intronic site would be used based on the surrounding genomic context. Based on the gene structure, we classified intronic sites into two categories: composite and skipped types. The skipped intronic sites show splicing between the upstream 5’SS and the polyA site, while the composite sites do not. For each polyA site, we annotated the following features: polyA site strength, distances to nearby splice sites, surrounding 5’SS and 3’SS strengths, sizes of upstream exon, downstream exon and intron, or whether the site is located in the last intron. The detailed calculation is shown in Supplementary Table To characterize the impact of genomic features other than site strength regulating intronic site expression, we controlled for site strength using quantile sampling. PolyA sites were split into equally sized bins based on their PolyaStrength scores and then the same number of used and unused sites were sampled from each bin. This equalizes the variance in PolyaStrength between used and unused sites, removing the influence of strength on other genomic features we are investigating.P-value < 0.05. We also further validated these results using RNA-seq density of surrounding sites, binned by predicted usage probability, using data from the ENCODE Project40 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA30709). We showed the representative results using the RNA-seq from A172 cells, and we obtained the consistent results from other cell types .To train the logistic regression model, we used the genome-wide identified intronic polyA sites as positive examples, and an equal number of negative sites with no supporting reads were randomly selected. For composite and skipped intronic sites, we split the data into training (90%) and testing (10%) sets and then fit independent logistic regression models with the noted features. We used the Python package “scikit-learn” to train the model. The models were evaluated using the AUROC for the testing set to assess the accuracy of the predictions. We tried to incorporate other genomic features into the models , but those features did not improve the prediction. Our final models only included genomic features contributing significantly to the model with To learn the genomic features determining the relative usage of terminal exonic polyA sites, we focused on genes with multiple polyA sites in the 3’-most exon. We selected the strongest polyA site per gene and randomly picked another site in the same gene showing ≥8-fold lower usage than the strongest one. Two sites in each gene were categorized as proximal (upstream) and distal (downstream) based on their relative position. In total, we selected the 9,443 site pairs. For terminal exonic APA sites, we annotated the following features: polyA site strength, distance between two sites, distance to the downstream gene in the opposite strand, and size of the last intron. The detailed calculation is shown in Supplementary Table P < 0.05) in our final models. We also validated our usage predictions by comparing the RNA-seq read density of surrounding the sites, grouped by the predicted usage probability, using data from the ENCODE Project (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA30709). We showed the representative results using the A172 RNA-seq data and obtained consistent results from other cell types .Based on the above-described features, we developed a logistic regression model to predict whether the proximal or distal polyA site would show higher usage. We split the data into training (90%) and testing (10%) sets. These models classified the individual proximal sites in each pair to determine if it is the more highly used one based on the features described above. We use the Python package “scikit-learn” to train the model. We assessed the performance of these models by plotting the AUROC for the testing set. We only included significant features  were clustered together, while ESCs, HTR8, and JEG3 (embryonic-like cells) were grouped. We then identified proximal sites showing higher expression in neuronal-like cells, using the cutoff ≥10% relative usage difference in all neuronal-like cells vs. embryonic-like cells. Likewise, we also defined the sites showing higher usage across embryonic-like cells, and further divided them into two groups based on their relative higher usage levels .We selected five different cell/tissue types to use to analyze tissue-specific APA regulation, including cerebellum, NSCs, ESCs, HTR8, and JEG3. We picked these cell types because they were generated by the same 3’READS protocol. We focused on analyzing the APA genes showing universal expression across the five cells/tissues. For each gene, we picked the top 2 expressed 3’ terminal exonic sites supported by 3’READS to study. We examined the APA regulation using the Fisher Exact test comparing the proximal vs. distal site expression in each tissue vs. all other tissues. We found 3,297 genes showing APA regulation using the cutoff Benjamini-Hochberg adjusted Further information on research design is available in the Supplementary informationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary


Here we used whole genome resequencing and genetic mapping to show that brt-2 is caused by a serine to cysteine (S75C) substitution in the DNA binding domain (DBD) of a heat shock factor class B (HsfB) encoded by SolycHsfB4a. This gene is orthologous to the Arabidopsis SCHIZORIZA gene, also known as AtHsfB4. The brt-2 phenotype is very similar to Arabidopsis lines in which the function of AtHsfB4 is altered: a proliferation of lateral root cap and root meristematic tissues, and a tendency for lateral root cap cells to easily separate. The brt-2 S75C mutation is unusual because all other reported amino acid substitutions in the highly conserved DBD of eukaryotic heat shock factors are dominant negative mutations, but brt-2 is recessive. We further show through reciprocal grafting that brt-2 exerts its effects predominantly through the root genotype even through BRT-2 is expressed at similar levels in both root and shoot meristems. Since AtHsfB4 is induced by root knot nematodes (RKN), and loss-of-function mutants of this gene are resistant to RKNs, BRT-2 could be a target gene for RKN resistance, an important trait in tomato rootstock breeding.The Gene & accession numbersSolycHsfB4a - Solyc04g078770.The online version contains supplementary material available at 10.1186/s43897-022-00025-0. Since AtHsfB4 is induced by root knot nematodes (RKN), and its loss-of-function mutants are resistant to RKNs, BRT-2 could be a target gene for RKN resistance, an important trait in tomato rootstock breeding.The Arabidopsis , possess a twisting tap root, and lateral roots were reported to arise at high density giving a bushy appearance , a transcriptional repressor of auxin signalling impacting leaf morphogenesis and fruit development  technologies have made studies linking phenotype to genotype faster and cheaper. The tomato reference genome based on cv. Heinz 1706  as a spontaneous mutant within an unknown genetic background showing severely stunted growth and possessing dense, bushy, twisted roots. Compared to AC, brt-2 roots exhibited a strongly decreased growth, which is already noticeable at the cotyledon stage  and the leaves were observed to have a tendency to wilt in the glasshouse in well-watered conditions when evaporative demand was high . Despite the decreased root system and wilted shoot, the brt-2 plant produced 7–8 cm sized fruit with high seed set; however they had a high tendency for radial cracking : AC/brt-2, brt-2/AC, AC/AC and brt-2/brt-2. After 9 weeks the shoot and root dry weights (DW) were obtained . The dry weight of brt-2 shoots was increased 3-fold by using AC rather than brt-2 as rootstock, but the AC scion was not able to increase the brt-2 root mass in the AC/brt-2 graft compared to brt-2/brt-2. These data indicate that the effects of the brt-2 mutation are only expressed when the mutation is present in the root; a shoot containing the brt-2 mutation grows normally if its rootstock carries the wild type (WT) allele (brt-2+).In order to investigate the tissue specific impacts of the ned Fig. . Self-grved Fig.  and provbrt-2 mutant line was crossed with AC to create an F2 population that segregated with WT (219 lines) and brt-2 (69 lines) phenotype with an approximate 3:1 ratio , containing approximately 900 genes. Four more KASP markers were designed and scored within the recombinants and the mapping region was reduced to 1.9 Mbp between 62,760,651 and 64,623,394 bp , and one was in a putative promoter sequence (Solyc04g080020). The remaining two SNPs caused amino acid modifications, a serine-cysteine change in Solyc04g078770 and a proline-histidine conversion in Solyc04g080120 , a region only containing three polymorphisms, two of which could be excluded because they were common to other tomato accessions lacking the brt-2 phenotype. So, finally the 63,443,492 SNP in Solyc04g078770 was the only unique polymorphism in the mapping interval, it co-segregated with the brt-2 phenotype, and caused an amino acid change; this evidence indicates very strongly that the mutation in Solyc04g078770 causes of the brt-2 phenotype.This 1.9 Mbp region contains approximately 250 genes, however, the NGS analyses revealed only ten SNP/InDel variations in the 20 Table  and Toma20 Table , only twion Fig. . The othSolyc04g078770, also known as SolycHsfB4a  protein  motif in the first part of the coded protein  function  approach was not feasible, indicating close similarity between parents. The NGS polymorphisms allowed brt-2 to be mapped to ~ 2 Mbp on the long arm of chromosome 4, and the low number of polymorphisms was very helpful in this case as only one clear candidate polymorphism was found.For mapping SolycHsfB4a was a unique allele that displayed 100% co-segregation with the phenotype. No other polymorphisms were detected in the mapping interval that could explain the brt-2 phenotype, leading to the conclusion that SolycHsfB4a was the only candidate and the causative gene for brt-2. The physical distance between SlIAA9 (entire) and SolycHsfB4a is 1.68 Mbp, consistent with the low recombination frequency previously observed between the two loci  consists of three-helix bundles enveloped with a four-stranded antiparallel β sheet , the reported allelic series of loss-of-function mutations are all outside the DBD and recessive  or AtHsfB4 which has its highest expression in root and shoot apices of A. thaliana , with the next highest expression in the elongation zone (5.1 TPM). Similarly, the AtHsfB4 promoter directs GUS expression to root meristem tissue, and is specific for stele, cortex, endodermis and the quiescent centre (QC) , heat stress activation is not the unique functional trigger among the rather diverse members  genes that act as molecular chaperones in a wide range of plant responses to abiotic and biotic stimuli and during plant development.Even though SCZ encodes a nuclear protein regulating cell division asymmetry in A. thaliana roots through coordinated action with SCARECROW (SCR)  lines. AtHsfB4 is normally expressed specifically in ground tissue cells and the QC, so OE of AtHsfB4 using CaMV 35 s promoter would increase expression in these same tissues, but also ectopically in other root tissues  and additional layers of lateral root cap cells were also observed. Thus, the production of extra abnormal layers of cells and cellular detachment is a feature common to both SCZ knock-out and over-expression.The above studies on brt-2 line shows high cell proliferation around the root cap and the meristematic cells  and its ability to form trimers, so preventing it from behaving like other DBD mutants which are dominant through the poisoning of trimers in the heterozygote.The lls Fig. . These eHSFA1a compromised Mi-1.2-mediated RKN resistance by preventing the hypersensitive response . Although it is highly disruptive to root and shoot growth, the report by Olmo et al.  was used as one parent (AC). This was crossed with the bushy root-2 (brt-2) mutant line (TGRC accession LA3206), generating F1 plants that were self-pollinated to produce F2 seeds for use as a mapping population. Seed accessions are given in Fig. Tomato cultivar Ailsa Craig carrying an introgression from − 1 brewer’s pectolase  at room temperature. The seeds were washed thoroughly in tap water and dried at room temperature for at least five days. Before germination, all seeds were sterilised in 0.45% w/v sodium hypochlorite for 30 min and then rinsed in tap water to avoid seed-borne viral transmission. Seed were germinated as described 2; 2 mM MgSO4; 100 μM EDTA Fe-Na; 42.2 μM H3BO3; 9.1 μM MnCl2; 0.76 μM ZnSO4 and 0.32 μM CuSO4, pH 5.8 adjusted with H3PO4), at half strength before flowering and full strength after flowering. Four-week-old plants were phenotyped for the brt-2 trait and young leaf material used for DNA extractions.Seed were extracted from red ripe tomato fruits and separated seeds and gel were incubated overnight after adding 2–3 volumes of 0.12 M hydrochloric acid (Sigma-Aldrich), 1 g Lbrt-2 seeds were sown in 24-module standard seed trays with multipurpose compost and were grown in the glasshouse. Three-week-old plants were grafted in all combinations following the Japanese top-grafting method using silicon tube-shaped clips , according to the manufacturer’s instructions. Both lines were subjected to paired-end sequenced using Illumina HiSeq X platforms. The data comprised 389,240,368 (AC) and 431,168,724 (brt-2) 100 bp reads representing ~39x and ~ 43x coverage of average read depths, respectively. Data is available from SRA accession PRJNA750735 (NCBI). Reads were aligned to the SL2.50 (Heinz 1706) reference genome and variants were called using the “Alpheus” pipeline  to visualise the sequence variations between the parental genomes along the 12 chromosomes  tool was used to predict whether an amino acid substitution would impact on the biological function of a protein  was used with the default values. The BoxShade program used the Multiple Sequence Alignment (MSF) files generated by ClustalW Multiple Alignment feature  of BioEdit version 7.2.5 Hall, . The PRObrt-2 seeds were germinated as described above. Seeds were placed on moistened filter paper in Petri dishes sealed with Parafilm™ to maintain humidity and covered with foil to exclude light. They were left in a growth room at 22 °C for 7 days. For the microscopic studies, approx. 1 cm lengths of root (with root tip) were removed with a razor blade and placed in water on a cavity slide, a coverslip was mounted on top. Microscopy was carried out with a Leica DM6 B Compound Microscope, “Brightfield” and “Differential Interference Contrast” methods were used. Images were captured using 10x and 20x objectives via a Zeiss Axiocam 506 colour (6 Megapixel) microscope camera. The image acquisition and storage software IMS V18Q4 (Imagic Imaging Ltd), was used to capture conventional single images, and extended depth of field images via the software’s “multifocus live” mode to generate composite images.AC and Additional file 1 Fig. S1 Pedigree of the brt-2 mapping population. WSS numbers are the Cranfield seed accessions used for the study. The percentage of WT and brt-2 phenotypes in the F2 population are indicatedAdditional file 2 Fig. S2 Expression pattern of Solyc04g078770 in the Tomexpress RNA-seq database. Expression values were normalised by mean counts per base. Detailed expression values are in Table S3.Additional file 3 Fig. S3 Expression profile of Solyc04g078770 in the CoNekT RNA-seq database. Expression values are given in transcripts per kilobase million (TPM), normalised for read count and gene length. Bars represent mean value; circles represent minimum and maximum values. Bars are colour coded: white, roots; green, vegetative shoot; dark grey, callus; light grey, seeds; red, fruit; yellow, floral reproductive tissues.Additional file 4 Table S1 Shoot and root dry weight (g) of reciprocal grafted plants. Grafts are described as shoot/root, eg. AC/brt-2. SDW, shoot dry weight; RDW, root dry weight. n = 8 or 9. This data was used for Fig. Additional file 5 Table S2 Sequences submitted to LGC Ltd. for design of KBD assays. SNPs were detected by whole genome sequencing of parental lines (AC and brt-2) using SL2.50 tomato reference genome.Additional file 6 Table S3 Expression values for Solyc04g078770 in the RNA-seq database of TomExpressAdditional file 7 Table S4 Expression values for Solyc04g078770 in the CoNekT database