Synthesisexperiments and zeta potential  measurements wereconducted at a fixed initial degree of supersaturation , at circumneutral pH of ∼6, 0.02 M NaCl, and ambient temperatureand pressure. Mixed-mode measurement–phase analysis light scattering(M3-PALS) showed that the particles stayed negative for raq < 1 during barite crystal formation and positivefor raq > 1. At raq = 1, two populations with a positive or negative ζ-potentialprevailed for ∼2.5 h before a population with a circumneutralζ-potential (−10 to +10 mV) remained. We relate the observationsof particle charge evolution to particle size and morphology evolutionunder the experimental conditions. Furthermore, we showed that theζ-potential became more negative when the pH was increased forevery raq. In addition, our results demonstratedthat the type of monovalent background electrolyte did not influencethe ζ-potential of barite crystals significantly, although NaClshowed slightly different behavior compared to KCl and NaNO3. Our results show the important role of surface charge (evolution)during ionic crystal formation under nonstoichiometric conditions.Moreover, our combined scanning electron microscopy and ζ-potentialresults imply that the surface charge during particle formation canbe influenced by solution stoichiometry, besides the pH and ionicstrength, and may aid in predicting the fate of barite in environmentalsettings and in understanding and improving industrial barite (surfacechemistry) processes.The impact of solution stoichiometry on formation ofBaSO On top of that, the barite scale is consideredto be particularly difficult to deal with due to its low solubilityand it contributes to about 80% of the total amount of scale depositsin the oil and gas business, ultimately leading to high treatment,repair, or additive-usage costs. Barite scale formation is often theresult of mixing incompatible waters. For example, sulfate-rich seawateris often injected into offshore reservoirs for pressure maintenance,where it meets connate water, which often contains a high amount ofbarium. Furthermore, due to barite’s high specific gravity,whiteness, inertness, and opaqueness to X-ray characteristics, itplays an important role as filler for plastics, paints, rubbers, andpharmaceuticals.5BaSO21 In both settings, some of that research has been dedicated to theeffect of stoichiometry  on bulk processes, like crystal growth rates,9 induction times,10 and nucleation.9 It was foundthat barite nucleation and growth depend strongly and asymmetricallyon raq at a constant degree of supersaturationand pH ∼ 6. Barite formation was slower at extreme sulfatelimitation than at equivalent barium-limited conditions. It is well-knownthat barite particle charge, in terms of zeta potential ,is affected by various physicochemical conditions  in aqueous solutions at or nearequilibrium,21 but none have investigated the ζ-potential evolution fromnonequilibrium to equilibrium conditions. In addition to the effectof raq on barite nucleation and growthrates, we expect that raq also has a significantimpact on the surface charge development of Barite crystals duringnucleation and growth, altering the crystal’s electrokineticproperties. In the latter properties, we include the isoelectric point(IEP), potential-determining ions (PDIs), potential indifferent ions(PIIs), surface potential (Ψs), Stern potential (Ψd), and ζ-potential among others, of barite crystalsin aqueous solutions. These properties are important as they controlflotation, coagulation, and dispersion characteristics in suspendedsystems,5 which in turn help to understandadsorption activation mechanisms,22 solid–liquidseparation processes, including wastewater treatment systems24 and the optimal conditions of a well dispersed system.25Multitudinous scientific studies have beendedicated to investigatingthe (rates of) nucleation and growth of barite on both the industriallevel as well as from a fundamental perspective.raq by usingmixed-mode measurement-phase analysis light scattering (M3-PALS).26 We explore how the ζ-potential of thenewly forming barite particles changes while the particles grow toward stable crystals at (near-)equilibriumconditions. Moreover, we performed M3-PALS measurements to monitorthe effect of different monovalent background electrolytes and pHon the ζ-potential at different raq in equilibrium and nonequilibrium conditions. We relate the observationsof particle charge evolution to the particle size and morphology evolutionunder the same conditions. This experimental study is complementaryto the investigations done by Seepma et al. (2023),9 and the measurements were conducted under the same conditionsfor which the raq-dependence of baritenucleation and growth was reported.9Therefore, we investigate the ζ-potentialof barite particlesduring formation at different 22.13 and Me = Naor K. Experimental conditions were selected to be identical to thoseof Seepma et al. (2023),9 who defined theoptimal conditions for dynamic light scattering investigations ofbarite particle formation, including aspects of anisotropy, particlemorphology, and sedimentation. Therefore, the target initial supersaturationwas 1000 with respect to barite, i.e., Ωbarite =1000, with Ωbarite defined asKsp is thesolubility product of barite (10–9.99 at 25 °C).29 The propriety of Ωbarite = 1000 is further touchedupon in Supporting Information-I. To calculatethe composition of different sets of growth solutions, with a rangeof raq, pH, and ionic strength (I), Visual MINTEQ30—afree equilibrium speciation model-version 3.1—was used. Stocksolutions of BaCl2, Ba(NO3)2, Na2SO4, K2SO4, NaCl, KCl, NaNO3, HCl, and NaOH, with different concentrations, were preparedby dissolving reagent grade salts into Milli-Q water (ISO 3696 Standardgrade 1–18 mΩ) to create all growth solutions. Growthsolutions of 50 mL were prepared from the stock solutions and keptin centrifuge tubes (Greiner), with a minimum amount of headspace,and were used to perform batch M3-PALS experiments within 48 h. Consideringthe volume of headspace and solution composition, no significant supersaturationwith respect to BaCO3 (witherite) was reached due to potentialCO2 incursion (see Supporting Information-I). The solution pH and I were adjusted by the additionof NaOH/HCl and NaCl/KCl/NaNO3 (Sigma-Aldrich), respectively.The growth solutions were made in sets, where one solution containedthe sulfate salt together with the pH- and I-adjustingsalts and the other solution contained the barium salt. An equal volumeof both solutions was simultaneously added in a beaker, shaken forabout 5 s to mix, extracted with a syringe, and loaded into the cellfor ζ-potential measurements , but low enough that ζ-potentialmeasurements could be conducted where the background electrolyte (BE)did not dominate the measured systems. The Davies equation  was used to calculate the ionicactivities of the growth solutions.31The desired growth solutions’ 2 in the solution aremuch slower under these circumstances .332 dissolution at circumneutral pHconditions  in very dilute systems,34 the pH dropped quickly to ∼5.5–6.0right after mixing the two growth solutions  conditions, a closed atmosphere was assumed because thehydration and hydroxylation of CO2.2The barite particles formed during the ζ-potentialmeasurements were characterized by scanning electron microscopy (SEM)imaging. 500 mL of each of the barite-containing solutions was filteredwith 0.2 μm pore-size polycarbonate filters. Double-sided conductivecarbon tabs were placed on standard aluminum stubs (with a diameterof 12.7 mm), and a piece of each filter, containing the samples,was cut and placed onto the tabs. The samples were coated with an8 nm layer of 80:20 Pt/Pd coating. Subsequently, the samples wereanalyzed using a JEOL JCM-6000 Tabletop SEM. A voltage current of10 to 15 keV was used.2.3ue [m2 V–1 s–1], which is the particle’s velocity dividedby the electric field strength, under an applied electrical field.It is related to ζ-potential by Henry’s equation35 if the particles in the system can be assumedto be sphericalE is the electricalfield [V m–1], U is the averagevelocity of the (charged) particles in the medium [m s–1], εr is the solvent’s relative dielectricpermittivity [-], ε0 is the permittivity of freespace [kg m V–2 s–2], ηis the dynamic viscosity [kg m–1 s–1], f(κa) is the Henry function,with κ [m–1] as the inverse Debye length,and a [m] as the particle radius. It is worth mentioningthat, according to ue is more or less independent of theparticle radius a. The value for the Henry functionvaries between 1 (Hückel approximation) and 1.5 (Smoluchowskiapproximation) and is determined by the product of the (average) particlesize radius a to the inverse Debye length κ.Generally, ζ-potential measurements have a relatively largeerror compared to other techniques. The measurement accuracy for ζ-potentialis at best ±2 mV or ±10%, which means that minor changesand the meaning of those observed behaviors in the ζ-potentialcannot and should not be overinterpreted.36 We therefore focused our discussion on changes in the ζ-potentiallarger than 5 mV.ζ-Potential[V] is a difficult parameter to measure and, for our type of suspensions,is usually acquired indirectly by measuring the electrophoretic mobility 2.438 Based on the work ofSeepma et al. (2023),9 we performed ourM3-PALS measurements in the forward detection angle (FWD) at 17°for our type of suspensions since the dominant particle size formedat our conditions is best measured at this angle. The experimentswere conducted in DTS1080 folded capillary cells.37 In the Zetasizer software, the measurement procedure involveda group that contained a series of measurements that was repeatedthree times. In that group, one size measurement  was performed, followed by ten ζ-potentialmeasurements (in FWD) and ended with another size measurement. Thesesize measurements were conducted using the backscattering detectionangle (BSD) at 174.7° because DLS measurements at this angleare more capable of observing particles at different size classesconcurrently compared to FWD. Therefore, we used these BSD measurementsto investigate if the applied electric field had a substantial (unexpected)effect on the particle sizes themselves. Parallel to the ζ-potentialbatch experiments, DLS batch experiments were conducted in FWD sothat we could assess ζ-potential evolution with respect to sizeevolution 9).ζ-Potentialbatch experiments were conducted with the Zetasizer ULTRA using ZSXPLORER v1.2.0.91 software.I < 0.15 M . Inthis way, we were able to obtain a distribution for ζ-potential. Each ζ-potential measurementconsisted of 25 subruns, with no pause in between those subruns. Adelay period of 300 s between each ζ-potential measurement waschosen in order to avoid excessive joule heating of the sample, whichhas an adverse effect on the gold-coated electrodes on both ends ofthe folded capillary cell.38 Further settingsincluded automatic attenuation and voltage selection, although 150V was consistently selected for the voltage selection by the Zetasizer.The viscosity of the medium was chosen as that of water , corrected for thetemperature of 20 °C, but not corrected for the salt(s) in themedium as this amount was assumed to have a negligible effect in oursamples.40 The relative dielectric constant chosenfor our experiments was 80.4.The Auto Mode approachwas used for ζ-potential measurements. Consequently, slow fieldreversal (SFR) and fast field reversal (FFR) (or electro-osmosis andelectrophoresis) were conducted sequentially as our samples fulfilledthe condition of 33.1raq = 0.01, 1, and 100  crystal surface.57 Generally, at raq > 1, we observedthatparticles have a higher tendency for twinning and “rosette”-likecrystal habit formation compared to raq ≤ 1. However, at pH ∼ 3, we observed that twinningof smaller-sized particles occurred on a broad scale, irrespectiveof raq, while larger-sized particles didnot show such twinning behavior. Lastly, our barite particles formedin the KCl solutions were slightly more elongated than those formedin the NaCl and NaNO3 solutions.For the validity and interpretation of our ζ-potential measurements,we characterized for each condition at  and 100 1, the fo and 100 1. In add3.258 (3) the aggregationand agglomeration behavior, (4) the extent of long-lasting structuringof particles due to packing, and (5) the role of sedimentation inour batch ζ-potential measurements.ζ-Potential measurementsare generally performed under stable (equilibrium) conditions. Contrastingly,our measurements were obtained during the dynamic process of particleformation and growth. Therefore, we first evaluated (1) the validityof the Smoluchowski limit of the Henry function, (2) the contributionof surface conductivity,Figure S1 in Supporting Information-II). Therefore, the Debye length can beassumed to be constant at 2.156 nm. Consequently, the Smoluchowskilimit is valid for our commonly observed particles ≥ ∼100 nm (S3 in Supporting Information-IV). Second, surfaceconductivity was determined non-negligible for the particles ≤10nm . Still,the particle sizes in our systems were generally >200 nm . At the solid volume percentages for our experiments, the (average)interparticle distance was at least 413 nm and became larger as the(average) particle size increased. At such distances, the Gibbs freeenergy field for agglomeration is negligibly low (Supporting Information-VII). Finally,sedimentation of large particles occurred in some cases, demonstratedby a sudden drop in the absolute count rate in the ζ-potentialmeasurements. However, the ζ-potential profiles with time . Under these conditions, most other investigationsreported slightly positive ζ-potential values as well19 and attributed this to the preferentialrelease of SO42– from the barite crystalstructure. Contrastingly, barite crystals in pure water (or with an I up to ∼0.001 M) were reported to carry a slightlynegative charge,20 more comparableto our initial observations at raq = 1.These observations may reflect a different evolution of the surfacestructure of barite crystals. Buchanan and Heymann (1948)11 showed that natural barite has smooth surfaces,and synthesized or precipitated barite has very irregular surfacesand this may cause an absolute difference in ζ-potential aslarge as 20 mV.61 In most of the aforementionedresearch, the barite crystals were carefully selected before measuringζ-potential, while in our measurements, this was not possibledue to the nature of the experiments—we had no control overthe geometry of the formed crystals or the surface roughness. Yet,our SEM images . Note that ithas been shown previously that BEs such as Na+-ions andCl–-ions can also interact with the particle surfaces.71 Under low ionic strength conditions (I < 0.1M), such BE ions only affect the charging of the diffuse double layerand, in that case, are considered to act as indifferent ions, whichmeans that they cannot cause a sign reversal for the surface chargebut may affect and even reverse the sign of the ζ-potential78  in the ionic strength buffercompared to the {Ba2+}:{SO42–} (from 0.01 to 100) so it may be assumed that the impact of sodiumand chloride on ζ-potential was more or less equal over raq. Still, the magnitude of the ζ-potentialat near-equilibrium may have been reduced and even reversed by Na+ or Cl– ions as was observed toward theend of barite formation at raq = 0.01, a negative ζ-potential in excess sulfateand a positive ζ-potential in the initial population in excessbarium. It is likely that this is caused by excess inner-sphere complexesof sulfate or barium, respectively. This supports the fact that bariumand sulfate act as PDIs (Supporting Information-VIII).While the ζ-potentialvalues obtained at near equilibrium were within the range of previousresearch, our ζ-potential changed substantially with time beforereaching equilibrium  that initially became more negative beforebecoming more neutral. Also striking are the two populations at raq = 10 and raq =100, one with circumneutral particles and one population with morepositive ζ-potential, that were present in the first hour orso. How this behavior relates to the different particle size classesobserved will be discussed in raq = 10 (raq = 100 (Supporting Information-VIII).Furthermore, in the first hour at every raq = 10 3d and raaq = 100 3e, the l3.59). The predominant particle size observed forthe different raq conditions was alwaysbetween 100 and 700 nm  using BSDin sequence and in situ, i.e., between sets of ζ-potential measurementson the same suspensions and in the same cell as the ζ-potentialmeasurements and (2) using FWD in parallel experiments at the sameexperimental conditions as those for the ζ-potential measurement.The latter data are shown in d 700 nm 3f–j. In 9 reader isreferred to Supporting Information-X formore background data). The initial measurement at t = 0 showed higher stochasticity (Supporting Information-XI). Most likely, the initial signal was more strongly influencedby nucleation besides particle growth. This is in agreement with thefindings of MacHale and Finke (2023),79 who showed (based on the data of Turnbull (1953)80) that for initial Ωbarite = 361, nucleationdominated growth at t < 90 s. Though our initialΩbarite was higher , the difference in timing of nucleation at these high supersaturationvalues is small,83 i.e., induction time is less sensitive to the degreeof supersaturation at Ωbarite > 316, it is likelythat nucleation dominated growth at roughly t <90 s in our (stoichiometric) systems with an initial Ωbarite = 1000 as well.In the serial BSD size measurements 4, peaks asticity 4a–e, wheasticity 3f–j and asticity 1 in seriraq. However, at t ≫ 0 and every raq, our BSD data are in agreement with our FWDmeasurements that were obtained without imposing an electric field.The slight differences in apparent sizes between Based on 3.6raq = 1), the apparent occurrence of two populationswith different ζ-potential of the two populations. It might be that these two observations arerelated. For example, there may have been an initial preferentialinner-sphere complex formation with either Ba2+ or SO42– between the different groups of particles,balanced over time by the adsorption of the other constituent ion.This process may have been slow and diffusion-limited in our nonstirredbatch experiments. Alternatively, the evolution in ζ-potentialfor both groups of particles may reflect a different (change in) screeningof the surface charge by various electrolyte ions, although such ionscannot trigger surface charge reversals78 , generally a single population was observed in sizeand ζ-potential measurements. The negative ζ-potentialsobserved initially imply that the nuclei that formed likely carriedan excess sulfate due to the adsorption of SO42– anions that formed inner-sphere complexes on the nuclei surfaces.The resulting ζ-potential value evolved to even more negativevalues for another 30–60 min, while the initial rapid particlesize increase slowed down within 15 min  particle sizes compared to larger particle sizes  changes; 85 In addition, the amount of surface charge of the small particlesis relatively larger than that of the large particles.85 Alternatively, it may be that particles thatinitially formed with a charge imbalance recrystallized/ripened and,in that process, approached stoichiometry and thereby charge balance.Lastly, the evolution toward a neutral ζ-potential may reflectan increase of Na+ ions in the diffuse layer that act ascounterions for the SO42– ions absorbedto the crystal surface. Similar experiments to ours, but with CaSO475 and CaCO3,78 showed that “indifferent” ions, like Na+ and Cl–, accumulate as counterions in the diffuselayer over a time of 30–60 min for agitated suspensions61 due to Coulombic attraction. It can be envisagedthat for our non-agitated systems, this period was longer. Therefore,in our experiments (+) after the initial and more rapid adsorption of excess SO42– ions at the crystal surface.In excess sulfate conditions,our ζ-potential experiments showed behavior different from thatat sulfate excess. Initially, two populations of particles with near-neutraland positive ζ-potentials were observed. With time, these convergedtoward a single population of less strongly positively charged particles.In these experiments, it is expected that the potential-determiningion is the Ba2+ ion, causing the positive ζ-potentialvalues observed. The adsorption rate of Ba2+-ions is reportedlymuch slower than that of SO42–-ions,20 most likely because the Ba2+-ion is more strongly hydratedand its dehydration is generally considered the rate-limiting step.91 Based on the observations of Derkani et al. (2019),78 it may be envisaged that, for raq > 1, the adsorption of Ba2+ ions at the crystal surfacesis on the same time scale as the movement of counterions toward thediffuse layer. Consequently, the two populations with initially differentζ-potentials  particles and it may be that thecircumneutrally charged particles originated from nucleating crystalsor even prenucleation clusters, exhibiting a high degree of aggregation.This type of behavior was also found for silica nanoparticles, whichshowed a bimodal ζ-potential distribution.92 Upon adsorbing excess barium after aggregation, these particlesobtained a continuously more positive ζ-potential with time.This may be the cause for the trend observed at ∼2000–4000s in both raq > 1 experiments . In any case, the population of particles, with a positive ζ-potential fromthe start, showed a slight decrease in ζ-potential with time.Similar to the raq < 1 systems, thiscan be explained by the slow build-up of counterion concentrationsin the interface and/or crystallization/ripening toward stoichiometriccrystal composition.Atbarium excess , 9]. Toward equilibrium , barite has grown into more uniformand euhedral tabular crystals (4 at a 1:1 ratio).The adsorption and diffuse layers evolved with growing crystals andlikely include only limited adsorption of SO42– ions, with some of this excess negative charge screened by counterionssuch as Na+ in the diffuse layer. Consequently, the ζ-potentialevolved slowly over the 3 h from more to less negative due to thecontinued re-equilibration of the adsorbed and diffuse layers withthe growing and (re)crystallizing particles.The effect of  raq ≪1 5a, earlycrystals 1a, likelraq ∼ 1 . Contrastingly, the lessdominant population of particles with a positive charge likely adsorbedmore Ba2+ ions and, therefore, more Cl– counterions were likely present in the diffuse layer. It is worthnoting that the particles observed with TEM that formed within a fewminutes at these experimental conditions ,9 their At raq ∼ 1 5b, earlyraq ≫1 ,9 their – concentration increasing.At raq ≫1 5c, simil3.84–NaCl–H2O batch experiments  physicochemicalparameters constant. The barite formation experiments lasted approximately3 h .Note that the batch experiments are thought to evolve toward an equilibrium(Ωbarite ∼ 1) with time, whereby the solutionstoichiometry becomes more extreme for the nonstoichiometric systems,9 while raq = 1 remainsconstant. raq.We investigated theeffect of pH on the ζ-potential evolution for the BaSOraq inour initial measurements. A more negative ζ-potential with increasingpH has been observed in the past by other researchers as well95 and is ascribed to the adsorption of OH– ions onto the positive charge centers of barite crystals and deprotonationof surface hydroxyl sites. Contrary to this, lowering the pH resultedin an increase in ζ-potential, due to OH– desorptionand H+ adsorption on the negative charge centers of baritecrystals.96 Furthermore, other processesthat may contribute to this trend are the distribution of dissolvedlattice ions and/or hydrolytic reactions of H+ and OH– with the adsorbed lattice ions at the solid–waterinterface.93 The point at which the amountof positive charges is equal to the amount of negative charges isknown as the IEP.97 The IEP found by Hanget al. (2009)19 for barite lies approximatelyat pH = 7.8 , andtype of analysis . However, other pH valueshave been found for the IEP. For example, Wierer and Dobiáš(1988)93 found that the IEP lies at pH∼ 5.2, but they used natural barite (with a purity of 99.2%)from England (Cumberland), an ionic strength that was 1 order of magnitudelower than ours  and was measured by a different apparatus . Zhang et al. (2011)94 found the IEP at pH = 6.92, but they grew their barite crystalsin the presence of disodium ethylenediamine tetraacetate (EDTA-2Na)before the crystals were suspended in deionized water. The IEP thatcan be inferred from our results for raq = 1 is comparable to that reported by Hang et al. (2009),19 in particular when considering the usual sigmoidalnature of ζ-potential-pH dependence  negativewith an increase in pH at every pH = 7.8 6a and isesin 19 6a. The p3.8.1raq = 1, the ζ-potential followed the expected trend  formation around theparticles evolved. Due to the low solubility product of barite, theconcentration of Ba2+ and SO42– ions in the solution near equilibrium was significantly lower comparedto H+ ions at raq = 1 and pH= 3. This likely resulted in H+  fillingup the Stern layer/primary adsorption layer at this pH  and were adsorbed onto the negative charge centersof the formed barite crystals. Subsequently, a build-up of predominantlyCl– likely occurred in the diffuse layer/secondaryadsorption layer (cf. Williams (2016)96). Therefore, the ζ-potential (measured at the slipping plane)became more negative with time and its sign was reversed 94). At raq = 1 and pH = 6, the ζ-potential evolved from+6 to +9 mV and we thus did not observe a significant change in theζ-potential between the initial and final measurement. Thiswas likely due to the screening of the surface charge of both particlesize populations with time  charge centers at the surface, and the diffuse layer formed morerapidly with counterions compared to raq = 1 . At raq = 100, the ζ-potentials were somewhat more positivecompared to raq = 1, where a ζ-potentialwas measured at +28 mV at pH = 3 going to −4 mV at pH = 10.The offset to larger positive ζ-potentials most likely reflectsa larger abundance of potential-determining Ba2+ ions thatare at the particles’ surface. At pH = 10, we expected thatthe role of the BaOH+ complex was (still) negligible asthe activity is about 700 times less compared to that of Ba2+ , which mightbe related to the delay of nucleation compared to raq = 0.01 and 1.9 In addition,as nucleation is slowed down, the supersaturation remained higherfor a longer period, and therefore, a larger portion of the experimentinvolved simultaneously occurring processes of crystal nucleationevents and crystal growth at raq = 100.Kuwahara et al. (2016)99 showed that forbarite, spiral growth and nucleation can occur when Ωbarite = 300–1000 (we used Ωbarite = 1000). Thetrends observed at nonstoichiometric conditions agree with Forke etal. (1987)15 and Soccol et al. (2010),20 who previously showed that barium and sulfateare PDIs and therefore offset the ζ-potential-pH dependenceand affect the IEP. For raq = 0.01 and100, the ζ-potential decreased by 8 mV and increased by 2 mV,respectively . Thismay be an indication that “diffusion broadening” playeda role, where (early on) the diffusion of smaller-sized particlescontributed relatively more to the ζ-potential distributionthan the larger particles (which dominate at near equilibrium).100 In particular, the intensity particle sizedistributions for NaNO3 and KCl at raq = 1  were multimodal and more broad compared to NaCl . In addition, we cannot rule out that barite morphologymay have changed in KCl and NaNO3 BE solutions more drasticallycompared to barite in NaCl BE solutions, which in that case couldhave contributed to broader ζ-potential distributions in theformer. Additionally, although less likely due to the low conductivityin our samples ,101 wecannot rule out random factors, including higher local viscosity differencesor increased electric field inhomogeneities, contributing to the initiallymore chaotic ζ-potential evolution for KCl and NaNO3 BE solutions.To investigate the impact of monovalentBE types on ζ-potential in our barite suspensions, we comparedNaCl with KCl and NaNO3.9.1raq = 1, we observed that the ζ-potentialis initially slightly positive for all three BE solutions  andincreased for all three BEs . At raq = 1, for NaCl two distinct populations with a different ζ-potentialpersisted initially  and NaNO3  solutions. However, due to the observed“noisier” initial period for KCl and NaNO3, which lasted for about 30 min, we cannot rule out that there mayhave been two populations with a different ζ-potential presentduring the first 30 min. In addition, we observed that the ζ-potentialin both KCl  and NaNO3  solutions becamemore positive immediately after the start of the experiment, whilein NaCl  between Na+, K+, Cl–, and NO3–. In addition, Na+ has a slightly larger electronegativity than K+  and Cl– has a much largerelectronegativity than NO3– . Therefore, the free Na+ andCl– ions likely form hydrogen bonds more strongly102 and may therefore be slower to diffuse andenter the EDL.103At nitially 3c, whilenitially S17b and 3.9.2raq = 0.01, the general trend wasoffset to more negative values  and,similarly, to raq = 1, the ζ-potentialincreased for all BEs compared to their initial values . Similarly to raq = 1, we observed that the ζ-potential in both KCl and NaNO3  solutions became more positive immediatelyafter the start of the experiment, while in NaCl . However, the trendwas offset for NaNO3, as we observed an (unexpectedly)lower ζ-potential than at raq =1, though, similarly to this observation, Předota et al. (2016)104 found for rutile (TiO2) that negativesurfaces  were overcompensatedby strong adsorption of (inner-sphere) Na+ and (outer-sphere)Sr2+ ions in NaCl and SrCl2 solutions due tothe formation of positively charged fluid layers. The layers in theregion between the bulk (carrying the negative charge) and the overcompensatinglayers carried a negative charge and compensated for the excess adsorptionof cations. Similarly, in our case of raq = 100 in NaNO3 solution, NO3– and SO42– ions may have formed negativelycharged fluid layers, explaining the counterintuitive ζ-potentialresult. Furthermore, the ζ potential increased slightly withtime in a similar way as raq = 0.01 and1 . However,for NaNO3, the ζ-potential increased moderately from−5 to +14 mV, having its sign reversed. We observed, similarlyto raq = 1, that the initial period forKCl and NaNO3 solutions showed a more scattered ζ-potentialdistribution compared to NaCl . Therefore, for raq = 1,two populations may have been present. In addition, in the NaNO3 solution, the count rate dropped significantly after 2 h and, at the same time, theζ-potential decreased significantly. Causative may have beenthe sedimentation of particles, resulting in fewer counts (Supporting Information-VIII). Otherwise, theζ-potential for the KCl and NaNO3 solutions developedsimilarly, while the NaCl solution developed less strongly at raq = 100, which was also observed by the differencein count rates and at which time they reached a maximum .At 3.102+ to SO42– ion activity ratio strongly affects the ζ-potential duringbarite formation, next to pH and, to a lesser extent, the type ofbackground electrolyte. In sulfate-excess conditions, particles initiallydevelop with a negative ζ-potential, and in barium excess conditionswith a positive ζ-potential. In the first case, the particles’ζ-potential subsequently evolves to ∼0 mV within aboutan hour. In the second case, the particles maintain the positive ζ-potentialfor the duration of our experiments. This may have implications forthe particles’ (aggregation and agglomeration) behavior andthe interaction of the particles with any impurity ions and surfactants.Our experimentsshow that the Ba9 At intermediatesupersaturation, the aggregation of uncharged barite particles willlikely be more favorable compared to charged particles as higher chargesmay induce behavior like charge-stabilized colloids.107 This has also been observed in DLS and molecular dynamics simulationsfor CaCO3,108 where aggregatesat {Ca2+}:{CO32–} = 1 grewfaster and larger than at {Ca2+}:{CO32–} ≠ 1.The first moments  of barite crystallization at high supersaturation showstrong signs of aggregation.109 For example, free barium ions may be highly mobile in soil as Bamay be associated with soil colloids by ion exchange.110 Negatively charged sites on soil colloids couldpromote barite dissolution over time, thereby acting as cation exchangesites for barite-derived barium. If, however, the barite particlescarry a negative charge , due to sulfate excess in the Stern layer 111). Lastly, influencing the surface charge via solution stoichiometrymay help to improve processes that take place in the chemical, paint,and filler industries,116 among others, by coordinating the stabilityof suspensions and, ultimately, controlling the time scale and amountof barite deposition.Moreover, solution stoichiometry may improvemineral or nanoparticleinteractions with their surroundings. For example, since barite surfacescan carry a positive or negative charge solely caused by solutionstoichiometry, it can influence the fate of barite in soils, sediments,and other porous media due to differences in interactions betweenbarite particles and grains/pore walls.rn layer 5a, bariu42+}:{SO42–} (solution stoichiometry) during crystalnucleation and growth of barite has a strong impact on the surfacecharge of the barite particles formed. At a constant initial degreeof supersaturation (Ωbarite = 1000), temperature,and ionic strength, barite crystals:raq ≫ 1 and a negative ζ-potential when raq ≪ 1have a positive ζ-potential when nucleateand grow on a shorter time scale than the developmentof the EDLcarry a more negative ζ-potentialat alkalinepH3 solutions(for I ∼ 0.02 M) before stabilizinghave a ζ-potential that evolvesin the first hourin NaCl BE solutions and faster in KCl and NaNOOur experimental resultsshow that the {Ba4 (surface chemistry) processes. Therefore, solution stoichiometryis an additional parameter that can influence the stability of suspensionsof ionic minerals in general, tailor the surface charge of the particlesduring formation, and likely affect their reactivity toward surfactants.The ζ-potential and, therefore, the surface chargecan beinfluenced by the solution stoichiometry during crystal formation.Ultimately, solution stoichiometry may be used to predict the fateof barite in environmental settings and to regulate industrial BaSO


Saccharina japonica at a high concentration and has been proven to be an inorganic antioxidant that plays an important role in oxidative metabolism. Vanadium-dependent bromoperoxidases (vBPOs) and iodoperoxidases (vIPOs), which catalyze the oxidation of iodide, are essential for iodine accumulation and metabolism. Heavy metal pollutant cadmium (Cd) from anthropogenic activities can cause damage to algae mainly by producing oxidative stress. Here, the effects of iodine pretreatment on the stress of S. japonica caused by cadmium were analyzed. The growth experiment showed that iodine pretreatment could reduce the damage of low concentration cadmium on S. japonica young thalli. At the transcriptomic level, gene ontology (GO) enrichment analysis confirmed that cadmium stress could cause a peroxidation reaction in S. japonica. However, the most significant GO term was “photosystem I” in the series with iodine pretreatment. Weighted gene co-expression network analysis (WGCNA) indicated that iodine pretreatment alleviated cadmium stress responses of S. japonica by affecting the photosynthesis process. Analysis of the differentially expressed genes (DEGs) showed that five enzymes from the vBPO family and 13 enzymes from the vIPO family might play crucial roles in this process.Iodide is accumulated by the brown alga  Saccharina japonica is one of the strongest accumulators of iodine among marine macroalgae, with almost 5‰ iodine in its thallus, in an organic or inorganic state. Compared with red algae and green algae, brown algae such as Laminariales (kelps) are more likely to enrich iodine. In general, the iodine contents are 10–100 μmol (g dw)−1 for Laminariales, 1–10 μmol (g dw)−1 for Fucales, 0.1–1 μmol (g dw)−1 for Rhodophyta, and 0.1 μmol (g dw)−1 for Chlorophyta [2O2) to produce hypoiodous acid (HIO), catalyzed by vanadium-dependent haloperoxidases (vHPOs). Hypoiodous acid can further react with iodide to produce volatile iodine molecules which can be released to the surface of algae, thus reducing the content of ROS in cells [orophyta . The accin cells .Saccharina japonica, generally inhabiting cold water regions, is the most consumed commercial seaweed in China. Its annual production was 1742, 378 t in 2021, with Fujian being the major farming province [S. japonica can be used as a food and also as a raw material for the extraction of bioactive chemicals such as iodine, alginate, and fucoidan [S. japonica, inhibit sporophyte formation, and promote the formation of asexual germ lines. However, it is still unknown whether seawater iodine content has effects on the antioxidant capacity of kelp.The brown alga fucoidan ,6. The cSargassum fusiforme, cadmium stress negatively affects metabolic activity by regulating the enzymes involved in carbohydrate and energy metabolism [S. japonica, cadmium was found to inhibit some protein synthesis and lead to enzyme deficiency [2O2 content in the seaweed Nannochloropsis oculata [In the aquatic ecosystem, cadmium (Cd) is one of the most common heavy metal pollutants, which mainly results from anthropogenic activities. Heavy metal pollution can cause serious ecological risks to algae populations, fishery populations, their habitats, and even the whole marine ecosystem . In Sargtabolism . In S. jficiency . Researc oculata . Plants  oculata . Heavy mVanadium-dependent haloperoxidases (vHPOs), which catalyze the oxidation of halide ions, are essential for halogen metabolism in algae . ActivitS. japonica, and that iodide can protect kelp from peroxidation-induced damage. However, whether a higher iodine content can relieve the damage caused by Cd stress remains unknown. The objectives of the present study were to (i) analyze the effects of iodine pretreatment on S. japonica stress caused by cadmium through comparing growth and photosynthetic parameters and (ii) to explore the mechanisms of iodine pretreatment affecting the cadmium resistance of kelp from a transcriptomic perspective.It is known that excess cadmium may generate free radicals and ROS and produce oxidative stress in S. japonica sporophytes of all exposure groups were smaller than those of the control group of the corresponding series  were too low to be detected by PAM.On the fifth day of cadmium stress, g series a. The blg series b. Regardg series c. The di0 series d. The FvS. japonica thalli in the I10 series were much higher than those in the I00 series , the number of trend genes was 4258, and they were clustered to 20 profiles, with eight of them being significant profiles  enrichment analysis showed that “cellular process”, “metabolic process”, “single-organism process”, “localization”, and “cellular component organization or biogenesis” were the most enriched biological process GO terms, while “cell”, “cell part”, “membrane”, “macromolecular complex”, and “membrane part” were the most enriched cellular component terms. In the I00 series, “catalytic activity”, “binding”, “transporter activity”, “structural molecule activity”, and “antioxidant activity” were the most enriched molecular function terms a. In theIntra-series and inter-series pairwise comparisons were conducted to identify DEGs among samples. The number of DEGs was highest in I00C20-vs-I00C00 , which was higher than that in I10C20-vc-I10C00  a. In theKyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis of DEGs in HP1, HP2, and HP3 was performed c–e. The 2(fc) ranged from −5.2 to 13.2 for five vBPOs and seven vIPOs in HP1, from −9.4 to 3.8 for two vBPOs and seven vIPOs in HP2, and from −3.7 to 2.5 for two vBPOs and six vIPOs in HP3. Two DEGs coding for vBPOs were down-regulated, and three DEGs coding for vIPOs were up-regulated in HP1 and HP2. Two DEGs coding for vIPOs were up-regulated in HP2 and HP3. One DEG coding for vBPO and two DEGs coding for vIPOs were up-regulated in HP1, but down-regulated in HP3. One DEG coding for vBPO was down-regulated in HP1, but up-regulated in HP3.The transcription of several vHPO genes was shown to be regulated. Five vBPOs, eight vIPOs, and five putative vIPOs were found in the DEGs of HP1, HP2, and HP3 . Among tFv/Fm module–trait correlation was analyzed based on the identified modules. p = 9 × 10−10) and Fv/Fm . Genes in the “bisque4” module were negatively correlated with these two traits.The WGCNA analysis resulted in 24 distinct modules. R2) of all the selected DEGs were higher than 0.9. Therefore, the expression levels measured by RNA-seq and RT-qPCR were consistent, confirming the reliability of the RNA-seq analysis.Four up-regulated unigenes coding for “DSBA oxidoreductase”, “glutathione S-transferase 4” (two transcripts), and “vIPO”; four down-regulated unigenes coding for “vBPO, partial”, “cellulose synthase (UDP-forming), family GT2”, “superoxide dismutase”, and “short-chain dehydrogenase/reductase SDR”; and two unigenes coding for “putative vIPO3” and “polymorphic Outer membrane protein G/I family” (up-regulated in HP1 and down-regulated in HP3) were randomly chosen for measuring expression levels by RT-qPCR. The fold change levels determined by RT-qPCR and RNA-seq were compared for each unigene . The corS. japonica young thalli from the individual and transcriptome levels. The results of the RGRs and Fv/Fm indicated that iodine pretreatment could relieve the harm of low concentration cadmium in S. japonica. In kelp, the accumulated form of iodine is iodide in an inorganic state, and iodide can scavenge different kinds of ROS, such as hydrogen peroxide and hydroxyl radicals [Ulva compressa even at a low dose (1 µM), and stimulate ROS and deplete nitric oxide (NO) formation [Scenedesmus quadricauda [S. quadricauda were negatively affected by the excessive cadmium uptake. The protective effect of iodine pretreatment on cadmium stress is probably related to the antioxidant process, but this needs further verification at the molecular level.Three concentration gradients  of cadmium were systematically utilized to observe the influences of iodine pretreatment on radicals . Heavy mormation . A simildricauda . The viaS. japonica. In the I10 series, “ligase activity” and “lyase activity” were two of the most significant terms according to the GO enrichment results  were annotated. These two families of enzymes can catalyze the oxidation of iodide ions with hydrogen peroxide as an oxidant. This process may play a protective role against the oxidative stress induced by excess cadmium in entified . The iodFv/Fm, the traits representing the influences of cadmium stress and iodine pretreatment on S. japonica. This result also gave evidence suggesting iodine pretreatment in kelp can positively affect photo-physiology [S. japonhica. The results of trend analysis also supported this inference. Light harvesting  showed that the photosynthesis process was correlated with RGR and ysiology , thus alrvesting  and carbrvesting c were thSaccharina japonica cultivar “E25” were generated by crossing the female gametophyte of A040 and the male gametophyte of B013 [−1 NaNO3 and 10 mg L−1 NaH2PO4 at 12 °C and 50 μmol photons m−2 s−1  in a GXZ intelligent light incubator . Analytically pure KI and CdCl2 were used for iodine and cadmium treatments, respectively. Iodine pretreatment was conducted by transferring a half quantity of the juvenile sporophytes (about 4 cm long) to culture medium plus KI with a final iodine concentration of 10 mg L−1 (designated as I10) for 48 h. The rest of the sporophytes with no iodine pretreatment were designated as I00. Then, the algae were cultured under concentrations of 1, 5, and 20 mg L−1 Cd  = (ln Lt − ln L0)/t, where L0 and Lt are the lengths (cm) of thalli at the initial time and after t days of Cd treatment. The RGRs of fresh weight were calculated by the equation RGR (d−1) = (lnWt − lnW0)/t, where W0 and Wt are the fresh weights (g) of thalli at the initial time and after t days of Cd treatment. After 48 h of Cd treatment, three blades from each group were sampled for RNA extraction. After five days of Cd treatment, the maximum quantum yield of photosystem II (Fv/Fm) was measured using a pulse amplitude-modulated fluorometer  after the thalli were dark-adapted for 20 min. Then, the thalli were sampled for iodine and cadmium quantitative analysis.During the experiment, lengths and fresh weights of the algae were measured. The relative growth rates (RGRs) for length were calculated by the formula RGR  in the presence of serial standard solutions with default parameters. All data were expressed as the mean (±SD) of triplicates. The data were analyzed by one-way analysis of variance (ANOVA) using SPSS ver. 17.0. When significant differences between means were found, a Tukey’s multiple comparison was applied.The algal samples were rinsed three times with sterilized seawater, then oven-dried at 45 °C for about 12 h until a constant weight was obtained. Then, the samples were digested according to methods described by Wang et al. . Iodine −80 °C for total RNA extraction. Total RNA was separately extracted from the above 24 samples with a Trizol reagent kit  according to the manufacturer’s protocol. The concentration of RNA was checked using the NanoDrop 2000 spectrophotometer . RNA quality was determined with an Agilent 2100 Bioanalyzer  and RNase free agarose gel electrophoresis.The samples were rinsed three times with sterilized seawater, then immediately frozen in liquid nitrogen and preserved at The total RNA was purified to enrich mRNA by Oligo (dT) beads. Then, mRNA was fragmented into short segments with fragmentation buffer and used for double strand cDNA (dscDNA) synthesis with random hexamer primers. The dscDNA was purified with a QiaQuick PCR extraction kit  and end-repaired with phosphate; poly(A) was then added and ligated to Illumina sequencing adapters. Then, the products were size-selected and PCR-amplified. Twenty-four cDNA libraries  were established and sequenced on the Illumina HiSeq2500 by Gene denovo Biotechnology Co., Ltd. .https://bioinformatics.psb.ugent.be/gdb/Saccharina/, accessed on 26 May 2020) was built, and paired-end clean reads were mapped to the reference genome using HISAT2.2.4 [The raw reads were filtered with fastp  by removSAT2.2.4  with “-rhttp://www.r-project.org/) were employed for pairwise differential expression analysis between different treatments. Genes with an absolute fold-change ≥ 2 and FDR  < 0.05 were considered differentially expressed genes (DEGs). GO enrichment analysis of the DEGs was performed based on the hyper-geometric test [DESeq2 software (v1.10.1) and edgeR package  package in R , using tβ-actin as an internal control gene [β-actin, and relative gene expression levels were calculated according to the 2−ΔΔCT method [Ten unigenes were randomly selected from the DEGs for RT-qPCR validation. Gene-specific primers were designed via Primer Premier software (v5.0) and are shown in rol gene . The PCRT method .S. japonica. By using the genome sequence of S. japonica as a reference, RNA-seq analyses were conducted to explore gene expression differences under cadmium stress with iodine pretreatment or no pretreatment in the present study. The results confirmed that cadmium stress could cause a peroxidation reaction as “oxidoreductase activity” was the most significant GO term in the I00 series at the transcriptome level. On the other hand, the most significant term in the I10 series with iodine pretreatment was “photosystem I”. WGCNA analysis indicated that iodine pretreatment alleviated the cadmium stress responses of S. japonica by affecting the photosynthesis process. In addition, vBPOs and vIPOs may have played crucial roles in this process.According to the results of the RGRs, iodine pretreatment could partly alleviate damage caused by cadmium stress in the brown algae


Correction to: Plant Reproduction 10.1007/s00497-022-00452-5The article Regulatory dynamics of gene expression in the developing male gametophyte of Arabidopsis written by Božena Klodová, David Potěšil, Lenka Steinbachová, Christos Michailidis, Ann‑Cathrin Lindner, Dieter Hackenberg, Jörg D. Becker, Zbyněk Zdráhal, David Twell and David Honys originally published Online First without Open Access.After online publication the author decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the copyright of the article has been changed to ©The Author(s) and the article is forthwith distributed under the terms of the Creative Commons Attribution.


Marine viruses regulate microbial population dynamics and biogeochemical cycling in the oceans. The ability of viruses to manipulate hosts’ metabolism through the expression of viral auxiliary metabolic genes (AMGs) was recently highlighted, having important implications in energy production and flow in various aquatic environments. Up to now, the presence and diversity of viral AMGs is studied using -omics data, and rarely using quantitative measures of viral activity alongside.In the present study, four depth layers  with discrete hydrographic features were sampled in the Eastern Mediterranean Sea; we studied lytic viral community composition and AMG content through metagenomics, and lytic production rates through the viral reduction approach in the ultra-oligotrophic Levantine basin where knowledge regarding viral actions is rather limited.Our results demonstrate depth-dependent patterns in viral diversity and AMG content, related to differences in temperature, nutrients availability, and host bacterial productivity and abundance. Although lytic viral production rates were similar along the water column, the virus-to-bacteria ratio was higher and the particular set of AMGs was more diverse in the bathypelagic  than the shallow epipelagic  layers, revealing that the quantitative effect of viruses on their hosts may be the same along the water column through the intervention of different AMGs. In the resource- and energy-limited bathypelagic waters of the Eastern Mediterranean, the detected AMGs could divert hosts’ metabolism toward energy production, through a boost in gluconeogenesis, fatty-acid and glycan biosynthesis and metabolism, and sulfur relay. Near the deep-chlorophyll maximum depth, an exceptionally high percentage of AMGs related to photosynthesis was noticed. Taken together our findings suggest that the roles of viruses in the deep sea might be even more important than previously thought as they seem to orchestrate energy acquisition and microbial community dynamics, and thus, biogeochemical turnover in the oceans. Indeed viruses modulate the cycling of nutrients, since organic matter is released from the lysed cells and returns to prokaryotes in readily-available dissolved form rather than being transferred to higher trophic levels . Viral aBut even without lysing their hosts, viruses may reprogram hosts’ metabolism and thus alter nutrients flow, through the expression of viral Auxiliary Metabolic Genes (AMGs). AMGs are expressed by viral genomes when the host is infected and still intact; their expression causes a “shift” in hosts’ metabolism toward viral replication, for instance through enhancement of microbial nutrient uptake under low nutrients availability. This pattern was particularly studied under P limited conditions  and resuProchlorococcus abundance  in relation to the environmental setting of a largely unexplored basin in the Eastern Mediterranean Sea (EMS). The EMS is a semi-enclosed, concentration-type basin where evaporation largely exceeds precipitation and river run-off. It has unique circulation patterns  and phys2.2.1.Sampling was carried out on board the R/V Aegaeo in April 2016, at the onset of seasonal thermal stratification. Seawater was collected between 09:00 and 12:00 am by Rosette-Niskin deployment from 4 stations in the Western Levantine basin (Eastern Mediterranean Sea), encoded as LV3 , LV10 , LV13 , and LV18 . Four depth layers were sampled in each station: 5, 50, 75, and 1,000 m. The collected volume was transferred from the Niskin bottles into acid-cleaned and deionized water-rinsed low-density polyethylene containers, and further processed for the various analyses.2.2.S), temperature (T) and density (D) were obtained using a Seabird SBE 9plus CTD underwater unit connected to a Seabird SBE 11 deck unit (3−), nitrite (NO2−), silicate (SiO4), ammonium (NH4), phosphate (PO43−), dissolved organic carbon (DOC), phosphorus (DOP) and nitrogen (DON), total nitrogen (TN) and phosphorus (TP) and chlorophyll a (Chl) concentrations are presented in the Hydrographic profiles of salinity  were added and used as an internal standard of fluorescence. A FACSCalibur™ instrument (Becton Dickinson) was used at conventional air pressure, with an air-cooled laser at 488 nm and standard filter setup. Data were processed with the CellQuest™ Pro software (Becton Dickinson).The abundance of virus-like particles (VLP) was determined based on the protocol of 2.4.in situ temperature (as determined by CTD) and in the dark for 24 h. Every 0, 1, 3, 6, 12, and 24 h during the incubation, samples were fixed for the determination of viral and bacterial abundances as described above. Lytic viral production  was estimated from the slopes of the relationships between net increases in viral abundance over the respective time period of the net increase.Viral production rates were estimated following the viral reduction approach . Two L oin situ bacterial abundance , and this factor was multiplied to the lytic VP measurement.Lytic VP was further corrected for the bacterial losses due to the filtration; the recovery percentage was determined by dividing the mean bacterial abundance at the onset of the incubations (B0) by the The methodology for the assessment of heterotrophic bacterial production is presented in the 2.5.−1 FeCl3 to achieve viral particle flocculation within the following 6–12 h , kept in the dark and added in the viral flocculate, followed by hand shaking and overnight rotation at 4°C. After resuspension, viral particles in liquid were retained from the 1 μm filter by ultra-centrifugation at 141.000 g . Viral DNA was extracted following a CTAB protocol  following the manual instructions. Size selection was done using AMPure XP beads (Beckman Coulter). PCR cycles were 6 (according to the manufacturer advice with regards to the amount of DNA input). Metagenomic libraries were sequenced in the Illumina Hiseq 4000 platform available at KAUST Bioscience Core Lab using paired-end sequencing.de novo assembly algorithm, using a k-mer of 63 and a minimum scaffold length of 500 bp. Raw reads were deposited on the NCBI with reference PRJNA996089.p < 0.05 and VirSorter categories 1 and 2 were used for further analysis. The abundance and metabolic potential of auxiliary and metabolic potential of AMGs was determined through VIBRANT  and trimmed using a minimum phred score of 20, a minimum length of 50 bp, allowing no ambiguous nucleotides and trimming off Illumina sequencing adaptors if found. The trimmed metagenome reads were assembled using CLC’s  v1.2.1;  using th v1.2.1; . Further v1.2.1;  for macr v1.2.1; . However2.6.S, T, D, and the concentrations of PO43−, NO3−, NO2−, NH4, SiO4, TN, TP, DO, DOC, DOP, DON). The clustering was tested by permutational multivariate analysis of variance (PERMANOVA). Then, patterns in viral diversity and AMG content between the stations were tested for significance by applying one-factor PERMANOVA using the factor “depth.” The null hypothesis was that there are no differences. When 5, 50, and 75 m were not different to each other, they were named as “surface/subsurface samples” for brevity. Bray–Curtis dissimilarity matrices on square-root transformed metagenomics data were constructed  was applied to check for significant differences among the different depth layers in: the percentage contribution of individual genera and families over total viral contigs, the percentage contribution of viral AMGs over total number of AMGs and over their assigned KEGG category, the abundances of bacteria and virus-like particles, and finally in the physical, chemical and biological variables. The significance of the differences was assessed with 3.3.1.p value). The Canonical Analysis of Principal coordinates (CAP) of physical and chemical variables  revealed four significantly different groups, which were confirmed by PERMANOVA (p < 0.001). Samples from the same depth layers were grouped together and distantly from the others , thus, stations LV3, LV10, LV13, and LV18 are considered as replicates. e others .S averaged 38.98 ± 0.13 in the sampling stations with equally high values at surface/subsurface depths . Approx. 0.21 lower S was measured at 1,000 m compared to averaged surface/subsurface values (p < 0.05). T ranged between 13.80°C and 18.30°C (p < 0.05), with the coldest waters seen at 1,000 m .e values  but the  18.30°C . Small vp < 0.05), being higher at 1,000 m . DO concentration followed the opposite pattern . Measurements of these variables as well as of D, DOC, DON, DOP are presented in p < 0.05). Chl concentration was ~4x higher at 75 m than 5 m, and ~3x higher at 75 m than 50 m  nor heterotrophic bacterial (3.2 × 105 ± 8.4 × 104 mL−1) abundances differed within the surface/subsurface layers ; abundances were significantly lower at 1,000 m than at the other depths . The abundance of Synechococcus did not differ between the sampling depths, opposite to Prochlorococcus , which was significantly lower at 5 than 75 m, as well as at 50 than 75 m . Synechococcus overcame in abundance Prochlorococcus by approx. one order of magnitude at 5 m, and the opposite was noticed at 75 m .Neither viral , and it was slightly higher at 5 m (1.9 × 106 ± 1.3 × 106 cells mL−1 h−1) than the other depths  and ranged between 2.35 and 9.28 ng C L−1 h−1 (p < 0.05); significantly lower BP levels were seen at 1,000 m than surface/subsurface .Lytic VP did not differ between the sampling depths . Bray-Curtis distances were plotted using the principal coordinates analysis method  and Siphoviridae  was seen compared to 5 m (27% ± 1%) and 50 m . The same was noticed for Siphoviridae that contributed less at 1,000 m (19% ± 4%) compared to 5 m (22% ± 2%) and 50 m . The third most abundant family was that of Myoviridae, exhibiting a decreasing trend  from surface (23% ± 5% relative abundance) to deep waters . The remaining taxa (<0.5%) were rare families . Most of them were found in few stations each. The families Adenoviridae, Baculoviridae, Herpesviridae, Inoviridae, Iridoviridae, Marseilleviridae, Microviridae, Mimiviridae, Poxviridae were found in all stations  using the VIBRANT method. The general pathways are shown in p < 0.01). AMG content differed between 1,000 m and surface/subsurface samples. In terms of percentage contribution, the most frequently-detected general metabolic pathway was “amino acid metabolism” (34% ± 6%), followed by “carbohydrate metabolism” (18% ± 3%), “metabolism of cofactors and vitamins” (14% ± 4%), “energy metabolism” (12% ± 5%), and then “lipid metabolism” and “glycan biosynthesis and metabolism” . The remaining percentage at 5 m was attributed to “glycine, serine and threonine metabolism” AMGs .“Amino acid metabolism” AMGs were mostly related to “cysteine and methionine metabolism” and “arginine and proline metabolism” contributing 59% ± 8% and 32% ± 7% within the category, respectively . The “cysm” AMGs , involvi3.4.2.ugd, gmd, ascC, ddhC, rfbH, algA, xanB, rfbA, wbpW, psIB, glmS, galE) compared to surface/subsurface samples . Other consistently-high “carbohydrate metabolism” AMGs were those involved in “fructose and mannose metabolism” having significantly higher percentage contribution over all AMGs at 1000 m than the rest depths  and “riboflavin metabolism,” “folate biosynthesis” and “ubiquinone and other terpenoid-quinone biosynthesis” with highest contribution of “folate biosynthesis”  in all 5-m samples  and “oxidative phosphorylation” (CYTB and petB) and “methane metabolism”  being higher at 1,000 m  being higher at 1,000 m  in viral community composition and AMG content during the early stratification season in the Eastern Mediterranean Sea. The vertical environmental variability of this oligotrophic basin was considered in order to uncover, and propose viral actions that lead to energy acquisition and lysis sustenance. The depth-dependent patterns were linked to differences in temperature, nutrient availability , host productivity and host population density. While viral productivity levels did not change significantly along the water column, the numbers of free viruses per bacteria (VBR) were significantly higher at 1,000 m, resembling a similar peak in VBR in shallower mesopelagic depths in the same study area . InteresPodoviridae and Siphoviridae members but more unassigned reads, which is reasonable as a more complex community may sustain and optimize ecosystem functioning under low-oxygen and low-nutrients conditions.Differences in viral communities with temperature were highlighted in the Mediterranean and globally  and diffAdditionally, at 1,000 m we found that the AMG content was different than at the other depths. The main distinctive features of the deep layers in comparison to the shallower ones were (a) a higher contribution of genes involved in the sulfur relay system, the synthesis and metabolism of glycan and the metabolism of fructose, mannose, fatty acids, cofactors and vitamins and secondary metabolites, (b) a lower contribution of genes involved in energy metabolic pathways, and the metabolism of arginine, proline and galactose, (c) different AMG content within the categories of metabolism of amino sugar and sugar nucleotide, fructose and mannose, lipopolysaccharide, sulfur and methane, lipids and purine, and finally (d) the absence of genes related to pentose phosphate pathway, and the unique finding of genes related to pyruvate and biotin metabolism, and prodigiosin biosynthesis. At this point, we urge the readers to take into consideration the low number of replicated samples when deepening to our findings. Consideration of seasonal and inter-annual changes in irradiance, temperature and nutrient levels with depth would have also improved our exploration, since it is widely known that these changes largely impact viral activity .In the energy-limited bathypelagic zone, it is expected that viruses, as their hosts , need to4.2.pps, ppsA) was seen in the deepest samples, suggesting that the use of alternative carbon sources for the synthesis of glucose and polysaccharides is positively selected at 1,000 m. In deep waters, hexoses are preferentially removed from high-molecular weight DOC by bacteria  was seen in the surface/subsurface samples (but not in the deep) where light and energy availability is not a limiting step for host and viral productivity, similar to recent observations for surface coastal waters of the Mediterranean , while at 5 and 50 m these genes were significantly less. Indeed, comparing with in situ primary production measurements  and assimilatory  sulfur metabolism genes support the auxiliary metabolism of sulfur in the host cells  in various pelagic and benthic samples. The authors proposed that viruses assist their sulfur oxidizer hosts through the dsrC-like genes under nutrient limiting conditions , subsurface (50 and 75 m) and deep  off-shore waters of the EMS was achieved by combining whole viral metagenome sequencing, flow cytometric and productivity analyses, assuming that such a combination is the foundation of meaningful viral ecology discussions. The findings confirm our hypothesis that a more diverse repertoire of AMGs is present in bathypelagic than shallow EMS waters, diverting hosts’ metabolism toward energy production and thus, viral replication too. Further work is needed to generalize our implications to other ecosystems and seasons with regards to the diel, seasonal and annual variations in irradiance, temperature and other physicochemical characteristics of the water masses, since the sampling power in this study was limited by field-work constraints and analytical costs. AMG content was substantially different between 1,000 m and the shallower layers, revealing novel implications for the energy-production mechanisms in the resource-depleted deep waters of the EMS. The lysis rate was, however, similar along the vertical profile. Altogether, our findings advocate that viral interference with hosts’ metabolism is affected by the particular environmental setting, and must be carefully considered for deciphering the biogeochemical turnover in the oceans.The metagenomic datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article Section 2.5.AT: Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review & editing. GM: Data curation, Software, Writing – review & editing, Methodology. DD: Funding acquisition, Supervision, Writing – review & editing. SF: Methodology, Writing – review & editing. AG: Methodology, Writing – review & editing. DM: Methodology, Writing – review & editing. AP: Methodology, Writing – review & editing. EP: Methodology, Writing – review & editing. SP: Conceptualization, Data curation, Methodology, Project administration, Writing – review & editing. IS: Data curation, Methodology, Writing – review & editing. CZ: Methodology, Writing – review & editing. PP: Funding acquisition, Supervision, Writing – review & editing.


However, unlike strong acids and bases,their importance and influencing mechanisms in a given aerosol orcloud droplet system remain to be clarified. Here, we investigatethis issue with new insights provided by recent advances in the field,in particular, the multiphase buffer theory. We show that, in general,aerosol acidity is primarily buffered by NH3, with a negligiblecontribution from CO2 and a potential contribution fromorganic acids under certain conditions. For fogs, clouds, and rains,CO2, organic acids, and NH3 may all providecertain buffering under higher pH levels (pH > ∼4). Despitethe 104to 107 lower abundance of NH3 and organic weak acids, their buffering effect can still be comparableto that of CO2. This is because the cloud pH is at thevery far end of the CO2 multiphase buffering range. ThisPerspective highlights the need for more comprehensive field observationsunder different conditions and further studies in the interactionsamong organic acids, acidity, and cloud chemistry.Acidity is one centralparameter in atmospheric multiphase reactions,influencing aerosol formation and its effects on climate, health,and ecosystems. Weak acids and bases, mainly CO The different roles of carbon dioxide, organic acids, andammonia in influencing the acidity of aerosols and clouds are illustrated. For speciesassociated with nonvolatile strong monoacids or bases , the mechanism is the simplest, i.e., merelythrough neutralization reactions, and the efficiency can be consideredas one. For nonvolatile weak acids or bases, they can influence theacidity through neutralization reactions and buffering effects. Forsemivolatile acids or bases, the mechanisms are more complex, whereboth gas–particle partitioning and aqueous-phase dissociationplay a role in determining their efficiencies. Moreover, the nonideality,precipitation equilibrium, etc. would also influence the final acidity,especially in aerosol water.Traditionally, acidity is thought to be determined by the relativeabundances of atmospheric acidic versus alkaline species.2/HCO3–/CO32– system,organic acids, and ammonia, are long considered to play a role inregulating atmospheric acidity. Their quantified importance and majorinfluencing mechanisms, however, seem confusing at a glance. For example,CO2 determines the pH of “pure” raindrops can “buffer” the aerosolacidity, the recently proposed multiphase buffer theory7 suggests that this effect is usually negligiblecompared with the ammonia multiphase buffering.30 Incomparison, while the importance of ammonia in buffering the aerosolswas well illustrated recently,7 its rolein fogs/clouds is less understood. How and to what extent these speciesinfluence atmospheric acidity need to be clarified.Atmospheric weak acids and bases,mainly the CO7 The importance and mechanisms of CO2, organic acids,and ammonia are discussed for different types of atmospheric water, and key uncertainties andfuture studies needed are also discussed.Here, weexplored this issue with the recent research advances,especially the multiphase buffer theory.213 As illustrated in + reaches ∼20% of the initial amount of anions), which is insharp contrast with the behavior of nonbuffered system like the Na2SO4 solutions.The acidity of aerosols at variouslocations worldwide is buffered,and ammonia is proposed to be the dominant buffering species.+ or OH– through dissociationequilibrium. Take a weak acid HA with the acid dissociation constant Ka for illustration, upon the addition of strongacids/bases, it can buffer the pH changes throughThe buffering effectis the process when the buffer agents, namely,the conjugate acid/base pairs that differ only by one proton, partiallyabsorb the added HKa. Outside these buffer pH ranges, the buffer agent exists predominantlyeither as [A–] or [HA], and the buffering effectis negligible, as detailed below.Note that the buffering effect is significantonly within the bufferingrange, i.e., a certain pH range around p2.1nacid and nbase refer to theamount of added strong acidsor bases.The influenceof buffering effects on acidity can be characterized by the bufferingcapacity β, which represents the resistance of pH changes uponaddition of strong acids/bases, i.e.,Kw is thewater dissociation constant, and Kia, and [Xi]tot represent the acid dissociation constant and total molality of thebuffering agent Xi , respectively. Note that for nonbufferedaerosol systems, Xi = 0, and β is7For bulk aqueous systems, that is (see detailed derivationprocesses in refs  and 31)2–1 of (i) strong acid like HCl  or (ii) weak acid with a pKa of 4 . For the nonbufferedstrong acid solution, the pH changes abruptly from ∼0 to ∼14around the midpoint, i.e., when the added strong base equals the existingstrong acid with the solution pH 7. For the buffered system with theweak acid, however, the pH changed slowly around the pKa of 4 upon the addition of strong base within the bufferingpH ranges, indicating a strong buffering effect. Correspondingly,the β of the buffered system (βbulk) differsmuch with that of the nonbuffered system (βnonbuf) in this pH range . When pH and pKia, differ too much , the bi is so small  thatthe βi is significant only when[Xi]tot is extremely large. Accordingly, the moreabundant the buffering agent is, the larger buffer pH ranges it wouldinfluence.According to 2.2bulk in the bulk aqueous solutions  of species X in mol L–1 atm–1, Lw is the liquid water content in (gwater)/(m3 air), ρw is the liquid waterdensity (∼106 g m–3), R is the gas constant (8.205 × 10–2 atm L mol–1 K–1), and T is the absolute temperature in K.The above analysiscan be easily applied to multiphase systems like aerosols, when wereplace βolutions 2a by βmpi in multiphase systems isbi* and [Xi]tot*, where bi* dependson |pH–pKia,*|, while pKia,* depends further on Kia,, Hi, Lw and T  typically varies between 10–6 and 5 × 10–4 g m–3, while for clouds and fogs it can range between 0.05 and 3 g m–3 but is usually from 0.1 to 0.3 g m–3 . Note tm–3 (ref ). Therefr as 1%, and ε as the changes in particle-phase anion/cationmolality corresponding to 0.001 μmol m–3 ofchanges in atmospheric concentrations, i.e.,–3 is the unit converterfrom (μmol g–1) to (mol kg–1). This is roughly the smallest measurement uncertainty of typicalatmospheric species  and wouldrepresent the perturbation of interest for most studies. The εmp thus represents the minimum buffer capacity that would providethis kind of minimum resistance of interest. Therefore, Similar to the bulk aqueous phase, the importance ofthe multiphasebuffering effect can be judged by 33.135 withthe pKa* increasing from ∼0.4 to∼7.5 at 298 K in the Lw range ofinterest (i]tot*) and the general agreement between pKa* and pH , the NH3/NH4+ pair appears to be the dominant buffering species of aerosolsfor most populated continental areas 2 Meanwhile, the pKa* of ammonia is elevated considering the higher Lw range of fogs/clouds, which is 5.4–7.5 at 298K and even higher (6.7–8.9) at 273 K . Therefore, its buffering capacity is much lower than inaerosols, which gradually exceeds εthr only at higherpH levels , seeminglyto support the strong buffering effect of CO2 on rain.As carbonic acid is a weak acid with a p2 is negligible for aerosolsand limited for fog, cloud, or rain (Lw rangeof aerosols (10–6 to 5 × 10–4 g m–3), the corresponding pKa* of CO2/HCO3– at298 K is 15.8–18.4 . This is much larger than thetypical pH ranges of aerosols. Even if we assume a fresh sea saltor dust aerosol with pH of 7–8, the |pH–pKa*| gap is still over ∼8, which corresponds toa bi* of <1 ×10–8 and renders βmp,CO2 negligible even considering itshigh abundance . Although the |pH–pKa*| gap is still large (>4), the corresponding bi* of <1 × 10–4 may be compensated by its high abundances when the cloud pH is higher.As shown in the example cloud case42  when pH is over 5. This is consistent with the findingthat for some fog samples in California’s San Joaquin Valley,the measured internal buffering intensity can be nearly accountedfor by the carbonate system, especially in the pH ranges of 5–6.5.For fogs, clouds, and rains, the higher nd 11–13S1. Altho6.5(ref ). Nevert2 mixingratios of ∼350 ppm  of ∼5.6. This is usuallyattributed to the acidification by the naturally produced sulfateand weak organic acids.49 However, based on thetraditional buffer theories of bulk aqueous solutions, the “pure”raindrop pH of ∼5.6 just falls in the pH range when the bufferingeffect of the CO2 is the strongest (6.4 ± 1). In thiscase, it was hard to imagine that the high peak β associatedwith the abundant CO2 (∼410 ppm) could be readilyovercome by the trace amount of naturally produced acids so that therain pH is acidified from ∼5.6 to 4–5 ,–5 (refs  and 49).3.33–/CO32– buffer pairis nonvolatile, and its pKa is alwayskept at ∼10. Following the above analysis procedures, we canconclude that its buffering effect is negligible in all atmosphericwater, from aerosol water to clouds or rains. In comparison, boththe bicarbonate and the carbonate salts, widely existing in dusts,etc., can neutralize the acids and therefore decrease the acidity.This neutralization process is sometimes termed as “buffering”to indicate that the existence of dusts can alleviate acidifications.57 We call for the use of “neutralization” instead of“buffering” for this process to avoid confusion in thefuture.The HCO44.159 of precipitations and therefore acid rains.61 Especially in remote areas, their contributions can be dominant(up to 80%)65 and are still increasing.66 These indicate important contributions of organicacids to acidity through neutralization reactions .While the acidity of samples collected in bulk solutions , as characterized by indicatorslike free acidity of precipitations, is of interest in terms of theacidification of ecosystems, it is the in situ acidity that mattersduring the atmospheric chemical processes. The importance and mechanismsof organic acids in influencing the in situ acidity of atmosphericwater, however, are still under debate. Some studies suggested a largepotential of organic acids to buffer the pH of aerosols and fogs andtherefore influence the atmospheric processing,29 while some others suggested negligible bufferingeffects.68The organic acids, mostly carboxylic acids,are found to contribute significantly to both the free acidity  and total acidity Kia,*|. Based on Ka*, differsfrom Ka byHi and Lw ata given temperature . When ρw(HiRTLw)−1 ≪1,ΔpKa ≈ 0, and Ka* is roughly the same as Ka. At 298 K, this is roughly when the product of Hi and Lw isover 5 × 105 (mol kg–1 atm–1)(g m–3). That is, if one species is more soluble(with higher Hi), its Ka* will approach Ka and become insensitive to Lw at lower Lw levels.Here, we examined the potential contributionof organic acids to system buffering effects with the methods outlinedin Table S3 lists the thermodynamic propertiesof commonly observed water-soluble organic acids. For these organicacids, the pKa mostly ranges 3–5,and the Hi mostly rangesfrom 103 to 1012 mol kg–1 atm–1 (Table S3). Therefore,for most of these species, the ΔpKa will be <3 in clouds (Lw > 0.1gm–3),and thus would be buffering at the appropriatepH ranges of <7. For aerosol water, however, the potential contributionto system buffering would differ greatly with the pKa and Hi.i]tot. However,full-spectrum measurements of all atmospheric organic acids in bothgas and particle phases are unlikely considering their wide varietyand low concentrations of some certain species. Therefore, equivalentconcentrations of representative species may provide a good first-orderestimate. As formic, acetic, and oxalic acids are the most abundantand most widely measured organic acids,73 they can serve as good representative species, as detailed below.Another concern is the influence of species abundances, i.e., theinfluence of tot). Second, pKa* values need to be within the typical pH range of aerosols/cloudsso that bi is not too small. Third, pKa* values should differ with that of ammoniaat the given Lw and temperature conditionsin that region/periods; otherwise, the ammonia buffering would betotally overwhelming. Especially, for most continental aerosols, theammonia buffering is so strong that the organic acids often play onlya negligible or minor role (Ka* of ammoniais relatively high (As illustrated by the discussions above, the important organicacids in influencing the system buffering should meet the followingcriteria. First, they need to be with enough abundances (i.e., relativelyhigh [Xnor role 4.2. Undeely high 4, and bu85 while the size-dependent measurements are also rare.As the contribution of organic acids to cloud acidity can be quiteimportant, we encourage such campaigns in the future.Currently,the measurements of chemical compositions including both inorganicspecies and organic acids in both gas and particle phases are scarce,especially for fogs and clouds. In addition, the pH of individualcloud drops can vary with drop sizes, etc. within a given cloud,–1 in severe urban hazes.8 This kind of high nonideality can shift the pKa* of ammonia by up to ∼1 unit.36 Moreover, the influence of nonideality depends not only on the ionicstrength, but also on the aerosol composition and the specific ionpairs.36 Different thermodynamic modelsdisagree with each other even for the nonideality for inorganic species,87 not to mention the organic acids.As discussed in Ka*gaps between ammonia and organic acids and therefore enhance or weakenthe contribution of organic acids in buffering the aerosol pH.The deliquescentaerosols are highly nonideal, with high ionic strength up to ∼43mol kg3COOH,can potentially exert strong buffering effects in fogs and clouds.On the other hand, in-cloud reactions are shown to be an importantsource of organic acids,89 while the efficiency of which can be susceptible to acidity.2 In addition, unlike aerosols, the gas–liquidequilibrium times for bigger droplets like clouds are longer, andthe time scales may differ between the buffering effects and the in-cloudreactions. The feedback among these processes under different conditionsneeds further exploring.Asdiscussed in


Based on the known ligands of RORγ, we selected candidates and calculate their predicted activities with the best-performing models. We chose two candidates that were experimentally verified. One of these candidates was confirmed to induce the biological activity of the RORγ receptors, which we consider proof of the efficacy of the proposed methodology.In this work, we developed and applied a computational procedure for creating and validating predictive models capable of estimating the biological activity of ligands. The combination of modern machine learning methods, experimental data, and the appropriate setup of molecular descriptors led to a set of well-performing models. We thoroughly inspected both the methodological space and various possibilities for creating a chemical feature space. The resulting models were applied to the virtual screening of the ZINC20 database to identify new, biologically active ligands of ROR  •Application of Machine Learning to the virtual screening of nuclear receptors ligands was applied.•Publicly available ZINC20 data base was screened.•γ/RORγT agonist was found.Biologically active ROR•Presented approach is applicable for searching of novel active compounds targeting other proteins. Staphylococcus aureus, Bacillus anthracis, and Candida albicans) primarily at mucosal barriers γT protect against autoimmune diseases in mouse models, similar to RORC knockdown in mice γT, inhibiting the activity of this transcription factor, and consequently, diminishing the pathological function of Th17 cells in autoimmune disorders γT LBD that displaces the coactivators and recruits corepressors γT can no longer bind the ROR response element (RORE) γT LBD, that prevent the binding of coactivators γ and RORγT receptors. We employed six commonly applied machine learning methods, i.e.: extreme gradient boosting γ and RORγT receptors and to determine the probability of their interaction followed by an expected biological effect. This approach provides a tool for much faster prediction of potential ligands and increases the efficiency of drug discovery compared to tedious and time-consuming screening performed in a laboratory. This methodology enables the analysis of potential interactions of various molecules with any proteins acting as receptors. Interestingly, the potential specificity of a given compound toward multiple proteins can also be examined, which may be crucial for the investigation of drug-drug interactions or the prediction of potential side effects of a new drug candidate. The efficiency of the method has been experimentally proven. Developed machine learning models have been applied for the virtual screening of the ZINC20 database, and two unknown RORγ and RORγT ligands have been identified thus far.Nuclear receptors are a superfamily of transcription factors whose activity is regulated by the binding of a specific ligand 22.12.1.1Homo sapiens RORγ receptor from this database. These entries reflect various measured quantities, such as IC50, EC50, efficacy, and inhibition , max_depth (maximum tree depth), gamma (minimum loss reduction required to make further split on a leaf), rel_alpha (L1 regularization term), reg_lambda (L2 regularization term), min_child_weight (minimum sum of instance weight needed in a child), colsample_bytree (subsample ratio of columns when constructing each tree), eta (learning rate), subsample (subsample ratio of the training instance)•Multilayer perceptron: MLP architecture (predefined architectures with a certain depth and number of neurons on each layer), activation function , batch size, initial learning rate, minimum learning rate, regularization weight, optimizer, dropout rate•Random forest: n_estimators (number of trees), max_depth (maximum depth of tree)•Bagging: n_estimators (the number of base estimators in the ensemble), max_features (the number of features drawn to train each base estimator), max_samples (the number of samples drawn to train each base estimator)•Support vector machines: C (regularization parameter), kernel , shrinking (binary flag determining the usage of shrinking strategy)•Ridge: alpha , solver type  Each methodology has been carefully investigated in the context of the regression and classification predictive capabilities. By applying the well-established method of hyperparameter optimization mentioned above, the impact of their random (or default) selection has been largely mitigated. Thus, the final models exploited the majority of their predictive capabilities.Each predictive methodology involves a certain number of hyperparameters. The particular choice of hyperparameters has a great impact on the overall predictive model capabilities; therefore, it is crucial to determine them systematically. Here, we have applied the dedicated framework Hyperopt 2.1.4γ receptors as good similarity-search candidates. The search space was spanned by approximately 880 million compounds available in the ZINC20 database. For all these species, we calculated the 1024-bit Morgan fingerprints with the chemfp library γ ligands and ZINC20 members. We have assumed here that a potential candidate must be at least 70% similar to a known RORγ ligand. This approach resulted in fewer than 2000 compounds for which the predictive models were applied. To estimate the applicability domain of our models, we designed the chemical space as two T-distributed stochastic neighbors embedding (t-SNE) The final, best performing models were applied to search the ZINC20 database 2.1.5γ receptor, we performed molecular docking calculations. We applied Smina software 4WPFγ receptor.To qualitatively and quantitatively assess the interaction between the two hit molecules and the ROR2.22.2.12-Chloro-N-(2-isopropyl-6-methylphenyl)-5-nitrobenzamide (cat. AMB2552929) and 2-chloro-N-(2-chloro-3-methylphenyl)-5-nitrobenzamide (cat. AMB3369132) were purchased from Ambinter .2.2.2γ and NHRTOX-HepG2, which were previously described The HepG2 cell line (human hepatocellular carcinoma) was purchased from American Type Culture Collection  and cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum . The reporter cell lines: HepG2-ROR2.2.3Cell viability after treatment with the analyzed compounds was determined using the CellTiter-Glo® Luminescent Cell Viability Assay from Promega  according to the manufacturer's instructions. Luminescence was measured using Infinite® 200 PRO .2.2.4γ cells were seeded on 96-well white plates at a density of 15,000 cells per well. Twenty-four hours later, the cells were transfected with control CMV-XL5 or an expression vector encoding the RORγ transcription factor, RORα  or RORβThe reporter HepG2-ROR2.2.5β and beads coated with anti-CD2, anti-CD3, and anti-CD28 antibodies  for 5 days in the presence of increasing concentrations of the analyzed compounds. Cytokines were purchased from PeproTech .Naïve CD4+ lymphocytes were isolated using CD4 M-pluriBeads® anti-hu  from buffy coats of healthy, anonymous donors. Buffy coats were purchased from the Regional Center for Blood Donation and Blood Treatment, Łódź, Poland, as waste material. To obtain fully differentiated Th17 lymphocytes from naïve CD4+ cells, we used a protocol that was previously described by Wilson et al. 2.2.6IL17A, 5′-AAACAACGATGACTCCTGGG-3′ (forward) and 5′-CTTGTCCTCAGAATTTGGGC-3′ (reverse), as previously described IL17F, 5′-CTTTCTGAGTGAGGCGGC-3′ (forward) and 5′-TGGGAACGGAATTCATGG-3′ (reverse), as previously described G6PC, 5′-TCCATACTGGTGGGTTTTGG-3′ (forward) and 5′-GAGGAAAATGAGCAGCAAGG-3′ (reverse), as described in CYP3A4, 5′-TTCAGCAAGAAGAACAAGGACAA-3′ (forward) and 5′-GGTTGAAGAAGTCCTCCTAAGC-3′ (reverse) described in ∘C for 5 min, followed by 45 cycles of 95 ∘C for 10 s, 60 ∘C for 10 s, and 72 ∘C for 20 s. The expression of the cognate gene was standardized against the expression of the three housekeeping genes HPRT1, HMBS and RPL13A, as previously described by Vandesompele et al. First, total RNA from cells treated with the analyzed compounds was isolated using TRI Reagent . Second, 5 μg of RNA was reverse transcribed with a Maxima First Strand cDNA Synthesis Kit for RT-quantitative PCR . Quantitative PCR was conducted using SYBR Green I Master Mix on a Roche LightCycler 480 system . The following primer pairs were employed in this study: 2.2.7The results obtained from cell lines were analyzed using one-way ANOVA followed by Tukey's post hoc-test. The results from primary human cells were analyzed with Friedman repeated-measures ANOVA on ranks followed by Fisher's LSD method for all pairwise multiple comparisons. A 33.1For each of the methodologies, we have prepared a family of predictive models solving the classification and regression problem. The former models predict the activity of the molecule expressed as a binary value depending on the IC50 value. When the IC50 value is below the threshold value, here set to be 1000 nM, then the molecule is considered active; otherwise, it is considered inactive. For some methodologies, the returned value is only an active/not-active category; for other methodologies, the logit value is provided. The latter is the result of sigmoid transformation and might be considered as the probability that the molecule will fall into the “active” category. The probability value, in addition to the information about the activity, also delivers information about the confidence of the model. Ideally, if the model worked perfectly on a nonconfusing dataset, the predictions would be 0 and 1 for the nonactive species and active species, respectively. However, in real-world applications, when both the methodology and data are imperfect, the prediction is the value from the •Methodology acronyms: “RF”, “SVM”, “XGBoost”, “Ridge”, “MLP” and “Bagging” for random forest, support vector machines, XGBoost, ridge, multilayer perceptron and bagging•–Eight molecular descriptors: molecular weight, octanol/water partition coefficient, number of hydrogen bond donors, number of hydrogen bond acceptors, molecular polar surface area, number of rotatable bonds, number of aromatic rings and molar refractivity,–Three drug-likeness indices: QED [63] If “FP” is present (Fingerprint), then the circular Morgan fingerprint Molecular features components: “MD”, “FP”, and “FPMD”. In the case of an “MD” entry (molecular descriptors), the feature space is spanned by:•Principal component analysis (PCA) features. In the case of molecular fingerprints (“FP”), we have provided a PCA-transformed version with a differing number of principal components. This approach results in four additional variants of the predictive models accounting for 128, 256, 512 and 1024 principal components denoted as “PCA128”, “PCA256”, “PCA512” and “PCA1024”, respectively. The PCA components were sorted according to the variance gain; thus, by extending the PCA space, we incorporated additional components with decreasing variance gain. The base variant, i.e., the variant that avoids the PCA transformation is noted as “NoPCA”.•–“median100”: the threshold value of 100 nM and the median as an aggregation function,–“mean100”: the threshold value of 100 nM and the arithmetic mean as an aggregation function,–“meanNoLim”: no standard deviation threshold and arithmetic mean as an aggregation function.Raw data aggregation strategy. Raw biological data are ambiguous for some compounds. Thus, multiple IC50 entries are generated for the same molecule. However, for the final predictive model creation, a single value is expected. For some compounds, the experimental value spread was significant. In extreme cases, the same compound could be concluded to be active based on certain experimental values, i.e., the IC50 values were well below 1000 nM, and inactive based on experimental values, i.e., the IC50 values were well above 1000 nM. Therefore, we introduced the standard deviation as a measure of experimental value spread and a threshold value of 100 nM above which the compound was disregarded. The remaining multiple entries were aggregated as the mean or median. Three possibilities exist:•–“test”: the test set of 10% of the data extracted prior to the training (unseen data),–“train”: the training set .The dataset used for calculating the quality measures. Here we have two options:Within each methodology, we considered multiple variants of the predictive models. All results are collected in Each row in the tables is related to a carefully determined predictive model with optimized hyperparameters according to a certain strategy. The hyperparameter space was defined according to the nature of the methodology; the section 3.1.1The quality of each classifier is summarized by seven quality measures: precision, recall, accuracy, F1-score, area under the receiver operator curve (ROCAUC), average precision (AP) and Matthew correlation coefficient. All detailed results are presented in 3.1.22 coefficient, mean squared error (MSE), mean squared logarithmic error (MSLE), mean absolute error (MAE) and mean average percentage error (MAPE). The results are presented in 2 coefficient. In The quality estimation of the regression predictive models was established based on the R3.1.3γ ligands were compared with the ZINC20 database, and the most similar species were retrieved. ZINC20 virtual screening led to 1673 compounds. For each of these compounds, we applied the leading predictive models, i.e., those for which the best quality measures were achieved -5-nitrobenzamide (2C3MP) of the reporter and confirmed the involvement of this protein in the biological action of the considered compound -5-nitrobenzamide induced the expression of IL17A and IL17F in a concentration-dependent manner -5-nitrobenzamide) has the potential to promote ex vivo cell proliferation and to improve Th17 cell phenotypes. Recently, Lai et al. showed that the improvement in the generation of Th17 lymphocytes is beneficial in adoptive cell therapy in a murine model The identification of new RORSuccessful experimental validation has proven that the developed predictive models are correct. The combination of open source resources, including the experimental data retrieved from the ChEMBL database as well as all applied libraries of the Python ecosystem, led to an efficient and robust computational protocol in the form of a Python program. This tool covers 6 commonly available machine learning methodologies and a set of auxiliary functionalities supporting data retrieval, cleaning and final processing. The workflows are managed by dedicated configuration files that allow for large-scale calculations.γ/RORγT receptors but with already known nitrobenzamide, γ/RORγT receptors, not specifically agonists or inverse agonists, which are sometimes distinguished by only one functional group In summary, our cheminformatics approaches that used machine learning techniques allowed us to identify a new ligand of ROR3.4γ ligands and ZINC20 species, we selected ca. 1700 candidates, which were at least 70% similar to biologically active RORγ ligands in terms of the Tanimoto coefficient based on Morgan fingerprints. Second, these species were inspected with the best performing regressor and classifier, which led to a shortened list of candidates. Last, we have taken into account the commercial aspects and selected 2 species for final experimental validation, which showed that, despite the high similarity, only one of the two selected compounds demonstrated activity toward the RORγ/RORγT receptors.Within the current study, we have introduced an efficient methodology that allows the creation of well-performing QSAR models based on commonly available machine learning methods. We have carefully examined 6 methodologies, and within each of them, we considered selected alternative approaches oriented on the experimental raw data processing and the way the chemical features were created. Among all considered predictive models, we selected the best performing classifier and regressor, which were later applied to the preselected compounds. The selection was oriented on the ZINC20 database containing ca. 880 million commercially available compounds. First, based on the molecular similarity between known RORThe percentage of success, in this case, is very high and indicates that the use of our methodology based on machine learning in conjunction with the experiment significantly improves the search for substances with the expected activity against various proteins.As a potential avenue for future efforts, we observe the development of generative approaches combined with multicriteria optimization techniques for molecular optimization. The two species, selected and experimentally verified within the current study, might be considered good starting structures for further research. In addition to biological activity, we also intend to take into account other objectives, such as solubility, toxicity, and organic synthesis difficulty. As a result, we will obtain the Pareto-optimal solutions, and similar to the current study, some of these solutions will be experimentally verified.4γ, nuclear receptor ROR-gamma isoform 1; RORγT, nuclear receptor ROR-gamma isoform 2; RORE, ROR response element; RPL13A, ribosomal protein L13a; Th17, T helper 17 cell.CYP3A4, cytochrome P450 3A4; G6PC, glucose-6-phosphatase catalytic subunit; HPRT1, hypoxanthine phosphoribosyltransferase 1; HMBS, hydroxymethylbilane synthase; PXR, pregnane X receptor; RORC, RAR related orphan receptor C; ROR10.13039/501100004281National Science Centre project number: 2019/33/B/NZ7/00795.This work was supported by the Rafał A. Bachorz: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing. Joanna Pastwińska: Methodology, Investigation. Damian Nowak: Investigation. Kaja Karaś: Investigation. Iwona Karwaciak: Investigation. Marcin Ratajewski: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.