Salmonella genus), in terms of number of analytes  and potential cross-reactivity with similar analytes (other bacteria strains). Indeed, the biosensing of Salmonella required the development of sera and serum mixtures displaying homogeneous specificity for a large set of strains showing broad biochemical variety , which partially overlaps with the molecular features of other class of bacteria (like specific serogroups of E. coli). To achieve a trade-off between specificity harmonisation and maximization, we have developed a strategy based on the conversion of the specificity profiles of individual sera in to numerical descriptors, which allow predicting the capacity of serum mixtures to detect multiple bacteria strains. This approach does not imply laborious purification steps and results advantageous for process scaling-up, and may help in the customization of the specificity profiles of antibodies needed for diagnostic and therapeutic applications such as multi-analyte detection and recombinant antibody engineering, respectively.The control of antibody specificity plays pivotal roles in key technological fields such as diagnostics and therapeutics. During the development of immunoassays (IAs) for the biosensing of pathogens in food matrices, we have found a way to rationalize and control the specificity of polyclonal antibodies (sera) for a complex analytical target (the  In the therapeutic field, this means the development of macromolecules, such as antibodies (Abs) or other biomolecules, able to bind endogenous or exogenous epitopes , in order to elicit a therapeutic action2, food safety4, and environmental pollution7.In the diagnostic field, it means the realisation of receptors able to bind analytes that are important for the monitoring of human and animal health8, such as Abs9, aptamers11, and molecularly imprinted polymers13, among others.As receptors for diagnostic purposes, several class of molecules have been identified and developed., which were able to strongly increase the analytical sensitivity of ELISAs against the human chorionic gonadotropin14. In their studies, different monoclonal antibodies (mAbs) were mixed, resulting in cooperation to form circular complexes between mAbs and antigen, and thus sensitivity and specificity enhancement17. These and other studies paved the way for the concept of using oligoclonal antibodies , which are nowadays commercialized for diagnostic applications 18, and under consideration for therapeutic applications 21.The possibility of modulating Abs sensitivity and specificity upon mixing multiple constituents was long ago explored by Ehrlich et al22 or multiple bacteria strains23) and to the inherent complexity of the food matrices25. For instance, there is a continuous interest in developing diagnostic assays for the detection of the bacteria strains belonging to the Salmonella genus27, linked with salmonellosis, one of the main bacterial gastroenteric syndromes in industrialized countries, and typhoid fevers. The genus Salmonella comprises two species (S. enterica and S. bongori), the species S. enterica itself being divided into 6 subspecies  based on phenotypic criteria.One particular domain where specify modulation of Abs results critical and challenging is food analysis, due to the need of targeting multiple analytes at a same time  and flagellar (H) antigens, allows the classification of subspecies into serotypes; those belonging to the subspecies enterica are assigned a name frequently corresponding to a geographical place , the others are designated by their antigenic formulaSalmonella in food matrices, we have studied how to control sera specificity toward increasingly complex analytes (bacteria strains) in terms of number of analytes and analyte similarity. Furthermore, this multi-analyte system  requires sera displaying harmonised responses toward very different targets , while keeping low cross reactivity for other pathogens (e.g. E. coli and Citrobacter sp). To achieve a trade-off between these challenging specificity requirements, we have developed a multistep strategy that allows managing serum mixtures composition , which maximize and harmonize the specificity towards the selected targets , and minimize the cross-reactivity against the unwanted ones, with the minimal experimental effort.During the development of immunoassays (IAs) for the multiplexed detection of Salmonella strains) and their characterization ; b) The use of different descriptors to rank the sera; c) Specificity harmonization of serum mixtures ; and d) Serum mixtures optimization, in terms of number of sera and fine tuning of the specificity profiles.The general workflow of this study is based on the four stages of development summarized in Scheme Salmonella (54 serovars), the family of Enterobacteriaceae or belonging to other genus or species such as Staphylococcus and Pseudomonas sp . The rationale is to exploit rabbits immune repertoires to generate a library of reagents (the pAbs in the hyperimmune sera), and to verify which one of the following scenarios is the most suitable to address the problem of custom specificity profiles generation: (1) use of individual sera obtained upon immunization against multiple strains (specificity modulation via “serendipitous/stochastic” means); (2) use of multiple sera obtained by immunization against individual strains : or (3) combinations between sera belonging to the two aforementioned categories, where elements from (1) will serve as a “base” and elements from (2) as further constituents for finer specificity tuning against selected strains. In this, study 21 different immunogens and two biological replicates were used, yielding 14 sera against Salmonella pools and 28 sera against individual Salmonella strains. The strains used as immunogens were injected into rabbits by intradermal route, since this administration path resulted more efficient in eliciting higher (p < 0.01) and more homogeneous titres compared to the intravenous route  when a given sera is interfaced with its immunogen . We then expressed them by the arbitrary descriptor α (1/dilution X10E03), which normally represents a dilution 3 to 4 times lower compared to the one associated with the titer determination. During specificity determinations against Salmonella, the maximal colorimetric responses were around 1.5–2.5 OD units  and up to 2–3.5 . We used the 42 sera at their α dilutions to generate a dataset  in a matrix-like fashion  and the columns the Salmonella strains (coded from A to BC). The results of the background signal assessment of the secondary antibody  according to Eq. , one can run ANOVA pairwise comparisons either considering the individual sera as independent entities . The α values are reported in Fig. 38) and 192 (serum 14). Such 96-fold difference in the dilution to attain similar MRs can be explained by five factors, namely: (1) qualitative and quantitative differences of epitopes in the Salmonella strains used as immunogens; (2) rabbit’s immune system diversity; (3) different number of individual antibodies binding the Salmonella strain31; (4) different ranges of antibodies affinity; (5) a combination of all of these factors. Factor (1) accounts for the fact that immunisations against different species (S. bongori or S. enterica), subspecies , and serovars (either isolated or combined in pools) expose rabbit’s immune response to antigens that differ for structure (proteins and O-lipopolysaccharides)30, localisation and immunodominance 32. While the α values resulting from the use of the 21 immunogens do not differ statistically after ANOVA and pairwise comparisons , in the range 20–50% for 7 instances, and up to 85–105% for 8 instances  and biological stochasticity (Factor 2) combine together to determine a multitude of antibodies (Factor 3), which differ for their variable region sequences, thus specificity and sensitivity towards individual Salmonella strains (Factor 4).Factor (2) contributes to a lesser extent, because in the 21 sets of biological replicates, the CV% values are 0 for 6 instances  and the presence/absence of shared antigenic properties (e.g. S. bongori).Another important arbitrary descriptor emerging from the specificity assessment is ues Fig. b starts β% values allow for a rapid identification of the strains that generate weak NRs%, thus highlighting those needing specificity engineering to ameliorate the immunoreaction of the sera.The γ%, which is the average of the NRs% of a sera used at the α dilution against the 54 individual strains, and its standard deviation (γ%SD).We then introduced the arbitrary descriptor β% and can be related to the intrinsic specificity properties of the sera and therefore their capacity to recognize one, few, or multiple strains in a defined range of NRs%. γ% ± γ%SD is therefore a measure of sera “inclusivity” against the set of Salmonella strains , and “exclusivity” against the set of E. coli and the other bacteria species .This descriptor is thus “orthogonal” to γ% ± γ%SD values as a function of the possible distributions of NRs%, let’s consider a hypothetical and simplified dataset with 54 sera, each one yielding NRs% of either “0” or “100” for every one of the 54 Salmonella strains , to 100 ± 0 (the most inclusive sera).In that theoretical scenario, the distribution of this descriptor Fig. b will raγ% value of every serum is reported in Fig. 36) to 61 ± 20 (serum 31), and the distribution of the values of such descriptor is confined within the ranges imposed by the theoretical distribution , multiple-strain specific (30 < γ% > 50) and those with high inclusivity (γ% > 50). We rapidly recognized that a ranking based only on γ% values can consider good sera also those having poor α values (e.g. serum 34 having γ% 53 ± 14 and α of 4) compared to the most performing ones (e.g. serum 7 having γ% 53 ± 29 and α of 128). We finally introduced the arbitrary descriptor δ, which is the result of an equation that links α (sera dilution) and γ% (sera specificity) values, in a way to promote sera with high inclusivity and α values. To select such equation, one has to consider that α has a broader numerical range (2–192) than γ% (12–59), and limited “steps” not equally spaced . Therefore, the simplest way of computing δ  would give too importance to the titer values, and provide same δ for sera showing “symmetry” between α and γ% values , the sera showing better combinations of α and γ% are promoted in the ranking  the one based on the cube of γ% was selected to generate the δ values. One can see in Fig. δ values show the same profile as the α ones  or good for their inclusivity but with scarce α  are “filtered” off by such equation, while sera with the best inclusivity and α  and the E. coli strains and the other bacteria listed in Supporting Information Salmonella antigen , and the colorimetric responses against a strain well recognized by the majority of the anti-Salmonella sera (S. Virchow), instead of MR, were used to yield the NRs%. The KPL pAb is prepared via a proprietary purification procedure that yields good inclusivity and excellent exclusivity, according to the manufacturer, and is thus an excellent benchmark for our strategy regardless of any subsequent purification step.By accounting for descriptors values, we selected sera NRs%, such as more than 50 for some S. Montevideo, S. Enteritidis, S. Typhimurium and S. Meleagridis subspecies (which are among the most important serovars to detect), and lower than 50 for the majority of the other strains. This result in γ% ± γ%SD values of 36 ± 34 for 12, 70 ± 50 for 14 and 44 ± 26 for the KPL pAb, which means a greater inclusivity of 14 compared to 12 and KPL for the selected set of strains , followed by serum 12 (13 ± 11) and serum 14(22 ± 21), the latter being significantly less exclusive than the KPL pAb (p < 0.01).The comparison of these three inclusivity/exclusivity heatmaps , and low NRs% towards the strains belonging to the exclusivity set (E. coli and the others).Taken together, these observations prompted us to find a rational and potentially general solution to select sera with great MIXs by selecting a certain number (n) of sera among the 42 available are 861 (n = 2), around 112,000 (n = 4) and about 120 million (n = 8), thus rapidly reaching too large experimental spaces to be screened. Even by reducing the library size to 32 sera , or down to 21 , there are still hundreds to thousands of MIXs to be prepared and screened. Furthermore, one wants to use the least possible number of sera, firstly because a single polyclonal response (serum) has already a large number of antibodies in it, and secondly because the simpler the mixture, the higher the chances to reproduce the results and the easier the characterizations by the current protocols of recombinant antibodies production involving mass spectrometry34.From the combinatorial point of view, the possibilities for creating MIXs by taking advantage of sera ranking by δ, which suggest the use of 7, 11, 12, 13 and 14 as “bases” for specificity engineering, and by using simple algorithms in an electronic spreadsheet to identify the sera to be used to increase the NRs% against given strains. It is indeed sufficient to determine the mean NRs% for a subset of sera against a given strain, to generate “artificial heatmaps” representing the predicted specificity profiles of a given MIX , 64 (serum 5) and 32 (serum 30) will mean the use of 1 volume of 12 , 2 volumes of 5 , and 4 volumes of 30 .As an example, a mixture of three sera with MIX#1 and MIX#2 with 11 sera, MIX#3 with 7 sera, and MIX#4 with 8 sera, were prepared , while MIX#2 and MIX#3 have twice that value (40%).By this way, γ% ± γ%SD values of the four MIXs  are 107 ± 21 (MIX#1), 111 ± 27 (MIX#2), 97 ± 30 (MIX#3), and 110 ± 20 (MIX#4), result significantly higher than the best individual sera,  but do not differ from each other statistically  thus showing reduced NRs% improvements for the subspecies houtenae, salamae and the species S. bongori.The inclusivity heatmaps of these NR% values, resulting in artificial and experimental datasets that does not show statistically-significant differences between them  at the final dilution.There is a good agreement between the experimental and predicted see Fig. b, and thMIX#3 produced from diluted constituents or from neat sera (MIX#3(exp. α=8) and MIX#3 neat(exp. α=16), Fig. MIX#3 neat(exp. α=32) see Table S6b, Supporting Information Indeed, the comparison of γ% compared to the most inclusive sera  as a way to rank sera that, upon rational mixing, were suitable to engineer MIX specificity and achieve a 50%-100% increase of MIX, it should be pointed out that there could be some “physiologic” cross reactivity towards E. coli serotypes depending on the immunogen composition, namely the presence Salmonella strains that share antigenicity with an E. coli strain (e.g. S. bongori subspecies)35.For the exclusivity assessment of this set of four MIXs against 18 E. coli strains. In this case, there is no need of colorimetric response normalization against a specific strain, because there is no E. coli strain that act both as analyte and immunogen . The exclusivity γ ± γSD values of the four MIX are 1.77 ± 0.75 (MIX#1), 1.55 ± 0.63 (MIX#2), 1.28 ± 0.68 (MIX#3), and 1.31 ± 0.53 (MIX#4) with no differences from the statistical point of view  γ values obviously decrease  as a way to control cross-reactivity.Upon MIX#4 seems the best candidate for further optimizations in view of its γ% values (highest inclusivity), narrow γ%SD (harmonised colorimetric response), and number of constituents (8 sera), we determined the exclusivity of its constituent sera and of closely related ones  and the ones against S. salamae (36) and S. houtenae (37) show such cross-reactive profiles.Since E. coli strains37.The colorimetric results from both the inclusivity and exclusivity assessments account for the presence of a multitude of antibodies belonging to different sera, which may participate in both “homologous” (from the same serum) or “heterologous” (from different sera) fashion for avidity against the antigens, which are in some instances shared with 38 a recent summarization on the complexity of polyclonal sera avidity assessment, which has been developed especially in the fields of vaccine research to try finding correlations between thermodynamic parameters of binding and the resulting neutralizing effects. It is out of our scope to get such thermodynamic characterizations and molecular insights, but rather to use a multitude of antibodies belonging to different sera, and an incubation conditions (1 h at room temperature) gentle enough to allow reaching binding equilibria with the Salmonella antigens exposed onto the ELISA wells. In these conditions, a simple macroscopic reporter (colorimetry) is an index of the antibody density in every well containing a Salmonella serovars, because similar colorimetric responses imply similar numbers of antibodies immunosorbed. Sera selection and dilutions are the variables modulating the chemical spaces for the occurrence of such equilibria, towards the theoretical limit of 100 ± 0 γ% ± γ%SD , and tailoring their dilutions. We produced MIX#5 with 3 sera, , MIX#6 with 5 sera , and MIX#7 with 4 sera . See Supporting Information Some considerations prompted us to investigate whether we could further optimize MIXs#5-6-7 display harmonized specificity profiles, with MIXs#7 showing γ% ± γ%SD greater than those of the best sera (p < 0.001), while MIXs#5 and MIXs#6 performing better than 12 and KPL (p < 0.001), as revealed by pairwise comparisons .Even though MIX#5 as the candidate for the optimization of the dilutions of its individual constituents . To this aim, we planned and executed a complete factorial design with the factors being the three individual constituents of MIX#5  and the levels being four dilutions for each constituent in the range 1/32,000 (around 200 ng/mL IgGs) to 1/384,000 , while 5 strains were selected because they were source of cross-reactivity . The resulting dataset of 64X8 colorimetric responses was visually inspected , greatest exclusivity against E. coli (those with low volumetric ratios of 27) or greatest exclusivity against the other genus (those with low volumetric ratios of 5).Three ted Fig. , by havi12 at α = 256 and 5 at α = 192, but slightly differing for 27 dilutions, , but MIX#8 has the same inclusivity as the parent mixture MIX#5, while MIX#9 display lesser performances . Therefore, these two MIXs show an improvement of the α values of their “limiting reagent” (serum 27), which can be used at a much greater dilution (4X – 8X more) compared to MIX#5.Both Salmonella detection has to be maximized whilst possible cross-relativities towards E. coli are less concerning, MIX#7,MIX#6 and MIX#5 would be the best solutions  that share different degrees of similarity among them and between closely related analytes (E. coli strains).We have presented a rational strategy to develop mixtures of antibodies able to maximize and harmonize the specificity profiles against 54 targets . This process allows the design of mixtures with custom specificity profiles rather than their mere identification by large combinatorial screenings. In our case, it meant the design and screening of only a dozen of serum mixtures to maximize and harmonise specificity with the least possible constituents, thus requiring feasible characterisations (103–104 ELISA wells), instead of screening among the multitude of possible combinations (106–107 ELISA wells).This strategy is based on the production of a sufficient number of sera to cover the different features of the analytes (antigenic properties), and on the evaluation of their relative specificity profiles emerging from indirect ELISA assays. Rational parametrization of the resulting datasets allows predicting the responses of mixtures of different sera towards the selected analytes, where few sera resulting from immunisation against pools of NR% determinations , chemical resources (reagents for the screening of the immunoassay properties), human resources (for data production and interpretation), and time. Most importantly, it represents a general strategy to generate custom specificity profiles by addition of easily available constituents, rather than the use of more elaborated and case-specific protocols (such as chromatographic purifications). The limits of this strategy rely mostly on the technical reproducibility of the ELISA system available, because in the generation of the descriptors, the precision on both titer and Salmonella case, there are different possibilities to exploit the optimized mixtures, such as in competitive- or sandwich-based immunoassays. In the former case, bacteria pools can be immobilized on suitable surfaces (coating/coupling on ELISA wells or onto fluorescent µbeads), while in the latter the IgG fraction needs to be noncovalently  or covalently (carbodiimmide chemistry/ periodate oxidation) bound to those surfaces. Every aforementioned combination has its own “pros” and “cons”, but the combinations that does not imply (competitive indirect) or limit (sandwich indirect) pAbs chemical modification are the most likely to “translate” the specificity profiles obtained by our optimization strategy. In general, this strategy is very suitable for the development of bioanalytical assays where a multitude of analytes displaying molecular similarity need to be discriminated, thus requiring a signal threshold. These are the typical requirements of ELISA- and flow cytometry-based detection of drugs belonging to different class of actions, or proteins having immunomodulatory effects such allergens and gluten fractions. This strategy generates datasets that are in between what starts to be too large to be analysed by mere man-made operations in a spreadsheet software, and what is too little for the mathematical models and software necessary for big-data handling (e.g. next-generation sequencing).In the specific 39, by taking advantage of very rapid problem description, computational prediction, and experimental verification.Nonetheless, for this size of datasets, there are several mathematical and statistical models that can be employed to optimize and find trade-offs between the conflicting objectives of inclusivity and exclusivity maximization of a given mixture, such as multi-objective optimization in closed loop fashion41. Due to the progressive reduction of NGS costs, and presence of very high performance mass spectrometers, specific IgG isolation from the optimized mixtures will therefore be a convenient tool to expand the  repertoire of variable sequences to be paired and recombinantly expressed for the generation of “cocktails” of recombinant antibodies targeting complex analytes42.At last, this strategy fits well with the recent advances on rabbit immune response exploitation by combined Mass-spectrometry-Next generation sequencing (NGS) protocols. Those protocols require on the one hand to obtain the genetic information behind antibody production (whole antibodies “genotyping” by NGS), and on the other hand to obtain the variable sequences (“phenotyping”) from the specific antibodies after their isolation (immunoaffinity chromatography) and proteomic determination (enzymatic digestion plus high –resolution mass spectrometry), to allow for variable sequences alignment by bioinformatics tools and recombinant antibody expression9 CFUs of inactivated bacteria .The blood was centrifuged, and the collected serum was stored at − 20 °C until used. An aliquot of such serum was diluted 1/10 in a solution of 50% assay buffer  and 50% ethyleneglycol, yielding diluted sera solutions that were kept at -20 °C prior to their use for titer, specificity assessment and MIX production.All of the experimentation involving animals was done under the frame of the ethical protocol CE/Sante/E/001  approved by the ethical committee of CER Groupe (agreement nb. LA1800104). The agreement LA1800104 was bestowed by the Federal Public Service of the Walloon Region (Belgium). The experimentation respected the legislation in force at the moment of the studies, thus following the guidelines established at the European level , Belgian level , and Regional level . Polyclonal antibodies were raised in rabbit by subcutaneous injection of 107 CFUs overall) in a 50 mM carbonate buffer (pH 9.6). The wells were washed three times with washing buffer (0.15 M NaCl and 0.05% Tween 20 in distilled water), and blocking was performed with saturation buffer  for at least 2 h at 37 °C before washing again. Wells equilibration at neutral pH was done by incubating at least 10 min in conservation buffer . The test sera were diluted in the assay buffer , and the wells incubated for 1 h at room temperature. BacTrace Anti-Salmonella CSA-1 Antibody  produced in goat was reconstituted according to the instructions furnished by the supplier and used as primary antibody upon dilution 1:16,000 in assay buffer. After three washes, anti-Rabbit IgG (whole molecule)–peroxidase antibody produced in goat  diluted 1:10,000 in assay buffer (for the primary rabbit sera) or a anti Goat IgG-Fc Peroxidase antibody produced in rabbit (Bethyl Laboratories reference#A50-104P) diluted 1:20,000 in assay buffer (for the primary goat pAb) were added and the plate was incubated for 30 min at room temperature. After another three washings step, the chromogenic substrate 3,3′,5,5′-tetramethylbenzidine (TMB) was added, and the wells were incubated at room temperature for 30 min. The colour-producing enzymatic reaction was stopped by adding 1.8 N H2SO4, and the absorbance was measured at 450 nm in a Skanit plate reader (Thermo Fisher Scientific), which declares linearity within 0–4 units of optical density, then nonlinearity occurs . During titer assessment, at very low dilutions some responses reached saturation, but were never considered . During specificity determinations against Salmonella, the maximal colorimetric responses were around 1.5–2.5 OD units  or up to 2–3.5 OD units , while against some E.coli strains some sera showed high colorimetric responses  were coated overnight at room temperature with a solution of inactivated bacteria either isolated or as bacteria pools  against wells coated with the strain or the pool of strains used as immunogen. Two types of nonspecific binding (NSB) were evaluated: (1) the appearance of colour in coated wells in the presence of peroxidase antibody but in the absence of sera, , and (2) the appearance of colour in noncoated wells blocked with saturation solution 1/2000  and interfaced with sera at the lowest dilution . The titer is expressed as the dilution yielding the colorimetric response closer to 1 unit of optical density.α. Only one type of nonspecific binding (NSB) was evaluated .The general procedure for the indirect colorimetric ELISA was used, by interfacing diluted sera with different strains (see the example in Supporting Information  α dilution (MIX) or the neat sera (MIX(neat)).Mixtures were produced according to the volumetric ratios derived from Eq. , by blen(neat) with different strains (see the example in Supporting Information (neat).The general procedure for the indirect colorimetric ELISA was used, by interfacing MIX or MIX12, 27, and 5) and four levels of dilution  into a Microsoft Excel (2016) file, imported in the Tecan D300 Digital Dispenser Software . The mixtures were thus instantaneously “printed” inside each well, which was coated according to the plan illustrated in Fig. We generated a complete factorial design by encoding three factors (sera Supplementary Information.


A simple budget approach showed that this bubble-mediated transport has the potential to maintain a relevant part of the water-column methanotrophs in the seep field. The bubble-mediated link between the benthic and pelagic environment was further supported by genetic analyses, indicating a transportation of methanotrophs of the family Methylomonaceae and oil degrading bacteria of the genus Cycloclasticus from the sediment into the water column. These findings demonstrate that the bubble-mediated transport of microorganisms influences the pelagic microbial abundance and community composition at gas-releasing seep sites.Benthic microorganisms transported into the water column potentially influence biogeochemical cycles and the pelagic food web structure. In the present study six gas-releasing vent sites in the Coal Oil Point seep field  were investigated, and the dislocation of microorganisms from the sediment into the water column via gas bubbles released from the seabed was documented. It was found that the methanotrophs transport efficiency was dependent on the volumetric gas flow, with the highest transport rate of 22.7 × 10 In addition, bacteria located at shallow sediment depths can be mobilized by infiltrating seawater and then carried to deeper sediment strata, from where they can be injected into the water column by submarine groundwater discharge (through-beach transport)3. Another bentho-pelagic transport mechanism in shallow waters is sediment resuspension triggered by wind-induced mixing, breaking waves, tides, bioturbation, and gas bubble transport5. Microorganisms in the sediments usually are associated with fine particles (<60 µm)6 at concentrations that exceeds those in the water column by up to four orders of magnitude7. Resuspension by tidal currents can increase bacteria concentrations especially within the benthic boundary layer, that is thought to be characterized by a distinct microbial community with high activity10. Together, these exchanges and transport mechanisms may influence the coastal planktonic microbial food web13 and alter biogeochemical cycles14.The exchange of nutrients and organic matter between benthic and pelagic compartments affects the composition of the respective microbial communities and thus their functional capabilities in shelf seas. Microorganisms transport to and from the sediment and water column also has been described and occurs by a variety of exchange processes. For example, the suspension of benthic bacteria from beach material within the splash zone can facilitate cell transport into coastal water (over-beach transport)16. Here, methane migrates from the seabed into the water column via pore water diffusion, advective fluid flow, and/or bubble ebullition17. Such gas bubbles also transfer trace gases (e.g. short-chain hydrocarbons or hydrogen sulfide)18 from the sediment to the water column together with mineral particles19, organic matter20, and microorganisms20. Surfactants attached to the bubbles’ gas/water interfaces can be released by shear stress, bubble dissolution21, or bursting at the sea surface24. Bubble transport is the most efficient pathway of methane transfer between the sediment and atmosphere and bypasses the very effective anaerobic and aerobic microbial methane oxidation17. After methane is released from the sediment, several processes determine the amount that reaches the sea surface, especially water depth25, ocean currents and mixing26, water-column stratification30, and microbial methane oxidation31. Microbial methane sink mechanisms reduce the annual seabed methane emissions to the atmosphere from 8–65 Tg CH417 to ~1–35 Tg CH4, respectively32.Shallow marine areas characterized by methane-containing sediments contribute significantly to atmospheric methane input36, but how these slow-growing microorganisms37 achieve a significant population density in waters that are renewed continuously by local currents is unclear38. The findings of previous studies conducted in areas surrounding seep sites40 and mud volcanos35 suggested that anaerobic methanotrophic archaea and MOB are transported by methane seep bubbles into the water column36. This hypothesis was based on the higher abundance of MOB in the vicinity of gas bubble flares than in nearby sites not affected by gas bubble emissions from the sediment. In a pilot study at one gas bubble releasing vent site at a shallow nearshore seep field in California, Schmale et al.7 found first indications of a direct bentho-pelagic transport of MOB, by sampling escaping bubbles directly at the gas-releasing vent hole. A comparison showed that the natural bubbles released from the sediment transported more MOB cells than the bubbles that were released from an engineered gas outlet above the sediment surface. Although the factors controlling bubble-mediated transport were unknown, Schmale et al.7 hypothesized that transported MOB can inoculate overlaying waters, thereby enhancing the efficiency of the pelagic methane sink.Methane-oxidizing bacteria (MOB) are frequently found in the water column above methane seep sitesIn this detailed mechanistic study, we conducted a series of bubble-catcher experiments at two different seep sites to assess the influence of the parameters seep intensity, bubble size distribution, and vent density on the efficiency of the bubble-mediated transport of benthic microorganism into the water column. Selected genetic community comparisons of sedimentary, pelagic, and transported microorganisms enabled us to further verify the existence of the Bubble Transport Mechanism and to discuss its impact on biogeochemical transformations in the water column. Finally, a simple budget calculation was applied to assess the impact of the Bubble Transport Mechanism on the water column MOB abundance above the Coal Oil Point (COP) seep field.−2, and at IV Super Seep (SS) from 10 to about 700 vents m−2  ranged from 5 to 20 vents m−1 at the sediment surface (0–1.5 cm b.s.f. = centimeters below the seafloor) to 1.5 mmol L−1 at 10 cm b.s.f.  to ~0.5% at 10.5 cm depth . The water in the vicinity of both seep sites was enriched in methane  at both seep sites . Throughout the sampling campaign, bottom currents followed the coast, consistent with typical northern Santa Barbara Basin currents, which are driven by the Davidson Currentane Fig.  which va−1, Fig. . By cont−1, Fig. . Total ctes Fig. . Total c42. Emissions were as a bubble pulse of bubbles that lasted ~3.5 s and comprised 774 bubble images. The volumetric gas flow (Q) was 1.0 mLgas s−1 and the bubble surface area flux (A) was 10.9 cm2 s−1, dominated by the larger mode.A Bubble Measuring System (BMS) collected seep bubble videos for the Rostocker Seep and IV Super Seep where the Bubble Catcher samples were taken. Analysis of the bubble size distribution (the number of bubbles per second per radius increment passing through a plane) for a Rostocker Seep vent revealed two emissions modes at r = 1,660 and 2,750 µm  was from the main area of IV Super Seep  were similar, ~18 cm−1  of ~4 cm s−1, which agreed well with the field data-derived relationship between Vup and Q in Leifer (2010)45, in which Vup ~ 4 cm s−1 corresponded to Q ~ 1.2 mLgas s−1. For the IV Super Seep minor vent, Vup ~ 1 cm s−1, in good agreement with the Vup of ~ 1.05 cm s−1 as reported previously45, corresponds to Q ~ 1.6 mLgas s−1. Interestingly, Vx(r) for the main vent followed the shape of Vup(r) for dirty (coated by oil or other surface-active substances) bubbles whereas Vx for the minor vent followed Vup(r) for clean bubbles.The BMS data from the IV Super Seep’s major vent showed an upwelling flow  to 2.20 (±3%) mLgas s−1 at IV Super Seep  at the lowest volumetric gas flow (0.07 mLgas s−1) than at the maximum investigated flow . Total cell counts showed a similar trend, with an initial count of 9.96 × 105 cells mLgas−1 at 0.07 mLgas s−1 that declined to 6.2 × 103 cells mLgas−1 at 2.20 mLgas s−1. Cell transport per unit surface area followed the same inverse trend, ranging from 2.2 × 103 MOB cm−2 (RSV1) to 1.0 × 101 MOB cm−2 (SSV2) at a transported bubble surface area (A) of 0.8 cm2 s−1 and 39.6 cm2 s−1, respectively  – a difference of three orders of magnitude. Note that the observed gas flow spanned over two orders of magnitude  by sampling gas bubbles escaping 1) a natural vent (BC vent) and (2) an engineered gas outlet without any sediment contact (BC engineered). The volumetric flow of the examined vents ranged from 0.07 (±8%) to 0.73 (±2%) mL a naturaep Table , volume ep Table , transpoep Table , and m2 rea Fig.  to deters−1 Fig. . The nums−1 Fig.  decreaseely Fig. . The dis3 cells mL−1, such that mean cell counts in the “BC vent” experiments were ~17 (SS) to 94 (RS) times higher. MOB abundance in the “BC vent” was 24 (SS) to 62 (RS) times higher than in the controls  to eight (SS) times higher and four (RS) to six (SS) times higher for MOB.DAPI analysis showed that the total cell abundance in the “BC control” experiments was ~3 × 10Methylomonaceae, Marine Methylotrophic Group 2, see Supplementary Table Methylomonaceae were selected for visualization  and amplicon sequence variants (ASVs) were searched with respect to previously known methanotrophic families. All extracted OTUs were mostly associated with unclassified methanotrophic genera  analysis, 650 ASVs were assigned, of which 42 were found in the Bubble Catcher and in sediments  and lowest at a high volumetric gas flow  with a mean of 8 × 103 cells mLgas−1  were highest at a low (RS1: 0.07 mL53 (including waves and tides)54 and temperature55, likely affect the transportation rate of MOBs though these were beyond the scope of the data collected.The overall impact of the Bubble Transport Mechanism on the abundance of MOB in the overlying water column was determined by extrapolating the numbers of cells transported per volumetric gas flow over the number of active vents per square meter of seabed. Our data show that even vents with high volumetric gas flows and accordingly low numbers of transported cells per gas bubble  probably resulted from the scuba-diver operations with the Bubble Catcher at the seafloor. Additional microorganisms may have been introduced through sediment that was resuspended by wind and wave action5. Air-borne contamination during sample handling and filtration were likely low, due to the differences in cell concentration of air (103−107 cells m−3)56 and sample water (1010–1011 cells m−3).We conducted two additional types of Bubble Catcher experiments to allow for the transportation of water-borne microorganisms (“BC engineered”) and the introduction of contamination during Bubble Catcher handling (“BC control”) and “BC vent” experiments. The “BC control” experiments without gas bubble transport into the sampling cylinder featured low numbers of microorganisms, indicative of minor contamination of our samples by air- and/or water-borne microorganisms. The majority of this contamination . If the engineered bubbles were indeed more efficient in sparging microbes from the water column into the Bubble Catcher, then MOB transport rates, and total cell numbers calculated for the “BC vent” experiments would be underestimated.Although in the “BC vent” experiments, bubbles escaping into the water column were likely loaded with bacteria, fine particles, and surfactants collected from the sediment, the “BC engineered” experiments were designed to assess the contribution of water column-borne microorganisms to the Bubble Transport Mechanism. Previous studies have shown that particles and microorganisms in the sediment adsorb to the bubble interfaces due to the surface activity of bacterial cellsin situ hybridization (CARD-FISH) was supported by genetic analyses. Based on methanotrophic OTUs, some benthic MOB were transported from the sediment into the water column while others remained in the sediment. A bubble-mediated bentho-pelagic transport of methanotrophs was indicated by the detection of methanotrophic OTUs, in the sediment, in the “BC vent”, and in the water column samples . Four of these OTUs were also found in the samples from the “BC engineered” experiments , which confirmed the bubble-induced stripping of bacteria from the water column into the Bubble Catcher sampling cylinder. This mechanism would have the potential to dislocate microorganisms within the water column, even across boundary layers such as pycnoclines. The detection of 18 methanotrophic OTUs in both sediment and “BC vent” samples , which were absent in the water column, suggests that some MOB cells transported into the Bubble Catcher either quickly sank back to the seafloor or perished in the pelagic environment. The transport patterns from 8 of 20 methanotrophic OTUs were similar at the two seep sites 60. Also, the cell position in a biofilm can influence their anchorage on particles and their likelihood of detachment by external forces61. In addition, a recent study on the aerosolization of bacteria and viruses at the ocean-atmosphere interface suggested that cell morphology is a critical parameter that influences bubble-induced transport between these two environments62.The quantitatively observed transport of MOB with catalyzed reporter deposition fluorescence pmoA analysis supported the 16S rRNA gene-based transportation pattern. The six ASVs found in the sediment, “BC vent” experiment, and water column provided evidence of the bubble-mediated transport of benthic MOB across these habitats. Among the transported ASVs, 37 were assigned to the family Methylomonaceae, which agreed with the 16S rRNA gene results. However, the identified ASVs probably belonged to unknown marine species, because the only assignments that could be made using the reference database was to a species isolated from a freshwater lake (Methyloglobulus morosus), whose growth is reduced already at a very low salinity63, as well as to a species isolated from a Kenyan soda lake, which grows optimum is at pH > 9 (Methylomicrobium kenyense)64. Based on current knowledge, neither species was likely to have existed in the sampled environment.Our et al.65 used pmoA sequences from sediment and water column (3 m above sea floor) samples to investigate MOB community structure in two methane vent environments at water depths between 500 and 700 m, at the Eel River Basin and the Santa Monica Basin, respectively. A comparison of the 16 pmoA isolates in sediment and water samples from the two sites showed distinct methanotrophic communities with very little overlap between the two environments. In addition, the 16S rRNA gene analysis did not recover methanotrophic lineages. By contrast, we detected 40 OTUs belonging to methanotrophic lineages, of which eight were shared by the seep sediment and overlaying water column. However, using next generation sequencing methods resulted in a higher sequencing depth than achieved with the clone-library-dependent sequencing technique used by Tavormina et al.65. The difference in techniques and the respective sensitivities might explain the differences in the observed community overlap. Another possible explanation for the absence of benthic MOB in the water column in the Tavormina et al.65 study could have been the sampling depth (3 m above the seafloor). During their travel through the water column, ascending gas bubbles released at greater water depth continuously exchange gases with the surrounding water via their surfaces, resulting in a general decrease in bubble size with increasing distance from the seabed. It is expected that the constant shrinking of the gas bubbles surface during ascending25 increases the shear stress on the attached microbial cells leading to a preferential release of MOB at shallower water depth. For the time of our study the elevated shear stress at the bubble surface provoked by wake eddies66 support the detachment of microorganisms already near the seabed. Differences in environmental factors, such as oxygen concentration in the water column, seep characteristics, and volumetric gas flows, between the COP seep field and the seep sites investigated by Tavormina et al.65 may also explain the lack of MOB in the water column.A previous study by Tavormina −1 d−1) were three orders of magnitude higher than “BC engineered” (~0.6 nmol L−1 d−1) and five orders of magnitude higher than “BC control (~0.005 nmol L−1 d−1) rates. Thus, at least some of the transported MOB were active after their dislocation into the water column.Although the 16S rRNA gene analysis indicated an overlap between the methanotrophic communities, no information could be obtained about the metabolic activity of the transported cells. The fact that some of the transported OTUs were found in the water column while others occurred only in the “BC vent” experiments suggests different survival rates of sediment-borne bacteria in the water column. However, Bubble Catcher water samples from the “BC vent” experiments provided direct evidence of methane oxidation in incubations performed over a 3-day runtime directly after Bubble Catcher subsampling. Averaged methane oxidation rates of “BC vent” , from the underlying Monterey Formation through faults and fractures in the overlying Sisquoc Formation78. Methane originates from thermogenic sources underlying the Santa Barbara Channel, at sub-seafloor depths of 3–4 km80. In a 1995 estimate, seabed emissions of ~1.0–1.5 × 105 m3 d−1 gas (88% methane) were reported77. Approximately half of this methane dissolves in the water, the other half reaches the atmosphere76. A dissolved, submerged methane plume then is transported down-current remaining detectable for 50 km81.The Coal Oil Point (COP) seep in southern California, USA, extends from the coastline to 3–4 km offshore, with seepage from water depths of a few meters up to 80 m Fig. 41,78,79.7, a high methane content (>91%)83, and no oil emissions. The Isla Vista Super Seep, located at 16-m-deep water, has an intense, focused seep area surrounded by extensive dispersed small seep vents, and emits gas bubbles with >95% methane83. Previous studies in the COP seep field demonstrated methane oxidation in sediment46 and water column84.The COP field allows perfect access for divers and equipment to a variety of gas vents that was a prerequisite for our investigations. Field experiments were conducted between 1 August and 15 September 2017 at two scuba-diver-accessible seep sites in the COP seep field Fig. , chosen et al.7, but the sampling cylinder (volume 12.7 L) was made of glass. A funnel and a stopcock directed and opened/closed the bubble flow, respectively, and a relief pressure valve prevented overpressure during surfacing  used herein was similar to that in Schmale gas s−1). After gas collection the Bubble Catcher was returned to the boat and the BMS was deployed from a second vessel and positioned at the exact same vents. Push cores were taken last as they are disrupting the sediment. Water column sampling was conducted during the runtime of the Bubble Catcher experiments. Bubble Catcher subsampling occurred within 1 h after return to shore.Prior to its deployment, the cylinder was filled with sterile-filtered seawater subsampled for total cells and MOB cell abundances to check for possible contamination. During the experiment, the captured bubbles (4–6 L) displaced Bubble Catcher water. Particles and microorganisms associated with the seep bubbles were released into the sterile-filtered water in the sampling cylinder after the bubbles had burst. Experiment durations and collected gas volumes were used to calculate the volumetric gas flow , the Bubble Catcher was placed above an engineered gas bubble source, created by air released from a pressure tank, positioned at least ~1 m from any gas vent. The bubble stream was adjusted to resemble volumetric flow and bubble size of natural vents in the vicinity. In “BC control” experiments, no bubbles were introduced into the Bubble Catcher, which was positioned as described for the “BC engineered” experiments. During subsampling, residual water in the Bubble Catcher was homogenized using a sterilized mixing rod, inserted through a small opening at the top of the Bubble Catcher, and filled into prepared sampling containers. The water sample was transferred via a sterilized glass funnel that was placed on the bottom of the respective container. Samples for methane oxidation rates determination and methane concentration measurement were subsampled bubble free without sample overflow, due to sample volume limitation. Total sample volume was determined by incremental filling. Methane concentration, MOB abundances, MOB community, and methane oxidation rates (only determined in the water column and Bubble Catcher) were analyzed from: (i) residual Bubble Catcher water, (ii) the sediment, and (iii) the water column.To parameterize the bubble-mediated transport process, gas bubbles were sampled from different gas vents of the Rostocker Seep and IV Super Seep sites. The sampling process is shown schematically in Supplementary Fig. Sediment samples were collected as push cores  to determine sediment methane depth profiles and confirm the presence of MOB that could be transported into the water column.Consistent with the high vent density at IV Super Seep the cores SSV1 and SSV2 were taken from sites of active venting. Other cores were taken at a distance of ~15 cm from any active vents. Cores for methane determination were processed on land directly by subsampling sediment plugs horizontally through openings pre-drilled in the core-liner at 1.5 cm depth increments to a maximum depth of 10.5 cm, using 3 mL cut-off plastic syringes. Cores for MOB abundance analyses were cooled (4 °C) during transport to the laboratory and kept refrigerated  until subsampled in 1.5 cm increments.The physical, chemical, and biological parameters of the water column were monitored to characterize sea water in proximity to Rostocker Seep and IV Super Seep. Salinity, temperature, and depth were monitored periodically over the course of a fieldwork day using a handheld sensor . Water samples were collected with a 2 L handheld water sampler  at depths of 1, 5, and 9 m at Rostocker Seep site and 1, 5, 10, and 15 m at IV Super Seep site.2 solution/mL of sample and stored inverted85. The samples were analyzed for methane using the headspace method86, by manually injecting 30 µL of headspace gas with a gastight syringe (Hamilton 100 µL) into a gas chromatograph . Each sample was analyzed twice. If the mean deviation between the measurements was >1%, a third measurement was conducted. For calibration, two standard gases were used with (1) 97.7 ppm CH4, injection volume 50 µL, and (2) 3950 ppm CH4, injection volume 20 µL, in syn. air (Linde) with an uncertainty of ± 2% by manufacturer. The standards were measured five times with a mean deviation of <1% at the beginning and end of each day.To determine the methane concentration in the water column and Bubble Catcher, 125 mL of sample water was filled bubble-free directly into crimp vials from the Bubble Catcher and handheld water sampler, respectively. All samples were cooled  until processed in the lab. The crimp vials were injected with 10 µL of saturated HgCl3 of sediment from each depth was transferred into 10 mL crimp vials filled with 5 mL of 2.5% NaOH. The vials were stored inverted at 4 °C. Methane concentrations were determined by injecting 30 µL of headspace gas from each vial as described above.For sediment methane concentrations, 2 cm87. All samples were cooled (4 °C) until further processing in the lab. Methane oxidation rates were determined according to Bussmann et al.88 with slight modifications. Thus, 20 µL of gaseous 3H-CH4 tracer  was added to every sample. Radioactivity was measured by liquid scintillation counting . Oxidation rates were calculated using the determined rate constant multiplied by the methane concentration. For “BC vent” samples, methane saturation was assumed and calculated after Wiesenburg and colleagues89 because methane gas bubbles purged permanently through the sample during the experiment time.Methane oxidation rates were measured in the water column and to ensure the activity of the collected MOB after Bubble Catcher sampling. 100 mL of sample water was filled bubble-free directly into crimp vials and sealed with non-toxic PTFE-coated chlorobutyl rubber stoppers et al.7, using the ultrasonic probe . An aliquot of 3 mL (dilution 1:3000) was filtered on 0.2 µm filters. All sample filters were prepared and analyzed according to Pernthaler et al.90. For hybridization, the probes M(γ)84 and M(γ)705, specific for type I MOB, and M(α)450, specific for type II MOB, were used as described previously7, except that they were pre-labeled with horseradish peroxidase. Filters were counterstained with 4′,6-diamidino-2-phenylindole (DAPI) to determine total cell counts.MOB and total cell abundances were analyzed in sediment, water column, and Bubble Catcher samples to quantify the transport of benthic MOB into the water column. For the CARD-FISH analysis, 100 mL of Bubble Catcher sample was filled into sterile centrifuge tubes (50 ml), for water column samples the volume was adjusted to 20 ml, cooled  until further processing in the lab. Sediment samples were processed as described in Schmale −1 was multiplied by the water volume of the residual sample to determine the number of transported cells per Bubble Catcher and then divided by the volume of captured gas to calculate the transported cells mLgas−1  was determined from bubble size distributions measured by the BMS , a combination of two established sets92, was used. LGC Genomics GmbH  performed library preparation and sequencing on an Illumina MiSeq V3 . Sequences were deposited in the European Nucleotide Archive (ENA) under accession number PRJEB34318 (16S rRNA gene) and PRJEB34319 (pmoA). 16S rRNA gene amplicon sequences were grouped into OTUs based on a similarity >97% and classified to the genus level.For DNA analysis, Bubble Catcher and water column samples were filtered on hydrophilic polycarbonate membrane filters , which were stored at −80 °C until analysis. To ensure sufficient biomass, 0.5–1 L (water column) and 1–2 L (Bubble Catcher) sample water were filtered. For sediment samples, the sediment’s upper 1.5 cm was homogenized and a subsample was transferred to a cryogenic vial that was stored at −80 °C until extraction. DNA was extracted from one-quarter of the membrane filter (for water column samples) or 0.25 mg of wet sediment with the DNeasy PowerSoil Pro kit (Qiagen). The V3-V4 region of the 16S rRNA gene was targeted with the primer set 451f-805r 93 (MiSeq SOP: accessed on 02.04.2019). Taxonomic annotation was accomplished using the Silva database (release 132), including the taxonomic changes proposed by Parks et al.94. For pmoA sequences, the raw sequences were processed with DADA2 v. 1.12.195, which uses ASVs instead of OTUs, and taxonomically annotated according to the pmoA reference database96. The phylogenetic analysis was performed with R v. 3.5.197 and phyloseq v. 1.26.098.16S rRNA gene amplicon read processing and annotation were conducted using Mothur v. 1.39.599.OTUs  were selected based on their taxonomic relationship to described MOB. ASVs present in at least two sample groups were selected. OTUs and ASVs present in control samples were removed from the analysis. Samples were grouped regarding their sample origin . The mean relative abundance per group and per OTU/ASV was calculated and visualized with ggplot2 v. 3.1.0103. Video was digitized at 60 fps. All bubbles in a sequence were identified and tracked manually due to high particle and detritus concentrations in the samples. Tracking assistance was provided by the algorithms in ImageJ v. 1.52102. Once a bubble was selected in two sequential frames, the algorithm predictively attempts to select the same bubble in the next frame based on the expected vertical displacement. If the predictive attempt fails, then the bubble was selected manually. The bubble equivalent spherical radius, r, is determined from a best-fit ellipse of the bubble outline, both with and without a convex hull. Further analysis was conducted using custom routines in MATLAB v. 2018b . The velocity of tracked bubbles was derived and a polynomial was fit to the vertical velocity (accounting for camera tilt) as a function of r, Vx(r). Vx(r) then allowed determination of the observation frequency of bubbles of each size class in the field of view to derive a multiple count correction for the flux (number of bubbles µm−1 radius s−1), which was calculated as time-resolved. Where Vx(r) varied significantly with time, an error is introduced since the algorithm uses a single Vx(r) which is calculated from all bubbles measured. This error was estimated at 30% for pulsing plumes which showed the largest variation in Vx.The bubble measurement system and analysis algorithms are described elsewhere42. For minor plumes, the radius of the peak of the Gaussian function, termed the mode, is determined solely by sediment grain size if the emission flux (Q) is below a critical value44. For Q above the critical value, the mode also depends on Q. The characteristic mode radius was determined from a least squares linear regression fit of a Gaussian function (or functions if multiple modes) using the MATLAB curve fitting tool. Integration of the emission size distribution over r at each time provides the time trend in total bubble volume and surface area as well as the rates of emissions of plume bubbles Q (mLgas s−1) and surface area (A) (cm2 s−1).Bubble plumes can be characterized as major or minor, where major plumes are described by a power law and are formed by bubble fragmentation, and minor plumes are described by a Gaussian functionx) needed to achieve the detected average water column MOB cell concentration (c) was calculated from the current velocity (v), the water depth (z), and the sediment-born MOB flux (F) as shown in Eq. The transportation distance (ΔCalculations were performed with the following variables:Supplementary information.


The ePix10ka2M is a new large area detector specifically developed for X-ray free-electron laser applications. Here the detector parameters and performance are presented, as well as the detector nonlinearity as characterized by diffuse scattering measurements at the Linac Coherent Light Source. −1 pulse−1 at 8 keV) makes it suitable for a large number of different XFEL experiments. The ePix10k replaces the large CSPAD in operation since 2011. The dimensions of the two detectors are similar, making the upgrade from CSPAD to ePix10k straightforward for most setups, with the ePix10k improving on experimental performance. The SLAC-developed ePix cameras all utilize a similar platform, are tailored to target different experimental conditions and are designed to provide an upgrade path for future high-repetition-rate XFELs. Here the first measurements on this new ePix10k detector are presented and the performance under typical XFEL conditions evaluated during an LCLS X-ray diffuse scattering experiment measuring the 9.5 keV X-ray photons scattered from a thin liquid jet.The ePix10ka2M (ePix10k) is a new large area detector specifically developed for X-ray free-electron laser (XFEL) applications. The hybrid pixel detector was developed at SLAC to provide a hard X-ray area detector with a high dynamic range, running at the 120 Hz repetition rate of the Linac Coherent Light Source (LCLS). The ePix10k consists of 16 modules, each with 352 × 384 pixels of 100 µm × 100 µm distributed on four ASICs, resulting in a 2.16 megapixel detector, with a 16.5 cm × 16.5 cm active area and ∼80% coverage. The high dynamic range is achieved with three distinct gain settings  as well as two auto-ranging modes (high-to-low and medium-to-low). Here the three fixed gain modes are evaluated. The resulting dynamic range (from single photon counting to 10000 photons pixel An example of such an experiment is the study of chemical dynamics through X-ray diffuse scattering (XDS) of molecules in solution, where a liquid jet is excited with an optical laser pulse and probed at different delays with diffuse hard X-ray scattering  have opened the possibility to investigate ultrafast processes using extremely bright femtosecond X-ray pulses. The Linac Coherent Light Source [LCLS The new SLAC-developed ePix10k detector [10 000 photons pixelc)b)–1(d)et al., 2019The outer dimensions of the ePix10k are very similar to the CSPAD making it straightforward to replace in existing setups [Fig. 1(3.a)a)b) and 2(c)et al., 2016b)c)et al., 2016The ePix10k is integrated into the LCLS data acquisition system (DAQ) and the gain mode of the individual ASICs can be configured as seen in Fig. 2 for a typical diffuse scattering experiment on a 50 µm water sample with the ePix10k placed 6 cm downstream from the interaction point. The high dynamic range hereby allows simultaneous detection of bright pixels flooded by 1000s photons pixel−1 pulse−1 near the liquid ring as well as weakly illuminated pixels near the edges of the detector exposed to 1s photons pixel−1 pulse−1 with high fidelity.Table 13.1.a) and 3(c)d)et al., 2015b) and 3(d)d)e)The pedestal  was measured by recording a number of shots  while not exposing the detector. In addition a pedestal scrip exists that records the pedestal for all three gain modes for use with auto-ranging. The pedestal was determined by averaging such unexposed shots and the standard deviation is also calculated to be used to mask out statistically noisy pixels. The pedestal is temperature dependent, so giving the detector time to cool down  and monitoring the temperature and cooling can be beneficial. When running the detector at 15°C, it stabilizes in minutes once the temperature is reached and the pedestal shows good stability over time. Fig. 3As with the CSPAD, the pedestal should be recorded whenever the detector is turned on, the gain mode is reconfigured or the temperature changes, so typically pedestals are recorded at least at the beginning and end of each shift (12 h) but typically pedestals are recorded a few more times during the shift, since it can be done without X-rays, and provides additional points of reference in case the pedestal changes over time. In addition to dedicated pedestal runs, the X-ray pulses are dropped periodically, and these shots can be used as internal pedestals throughout experiments to monitor the pedestal stability and as described below can help identify non-ideal detector behaviour.3.2.i.e.a)b)c)Upon inspecting the unexposed frames recorded when periodically dropping the X-ray shots, a remnant of the previously exposed frame was observed. This effect was observed during an actual experiment where the ePix10k detector was exposed to the diffuse liquid scattering from a water sample. In the case of diffuse scattering from water, the intensity distribution remains constant for a stable setup and the presence of a ‘liquid ring’ in the dropped shots indicates that a ghost image’ is present. Upon further investigation this ghost image’ contains approximately 0.9% of the previous shot. The residual image originates in the readout circuit as it is incompletely cleared after each readout. For future ePix detectors this will be remedied in the ASIC design. The current ePix10k with the ghost image problem is designed to run at 360 Hz. A firmware fix has been implemented that reads out the detector at double the repetition rate (240 Hz) and therefore reduces the ghost image to 0.9% times 0.9%  were evaluated and all showed good performance in agreement with the previously tested single module (Blaj et al., 2016Based on the charcaterization presented here, the ePix10k is well suited to replace the large CSPAD in a multitude of applications to supply better noise performance and larger dynamic range. The ePix detector architecture also provides a pathway to producing such detectors for future higher=repetition=rate facilities such as LCLS-II and LCLS-II-HE (Blaj


This article contains (i) a set of spreadsheets with data compiled from municipal sanitation or solid waste plans, and (ii) data of the individual and aggregate performance indicators. These indicators have been published in the Journal of Cleaner Production in the article entitled “A municipal solid waste indicator for environmental impact: assessment and identification of best management practices.” The data contained in the spreadsheets are divided as follows: worksheet 1 includes the municipal solid waste generation data from the Brazilian municipalities studied; worksheet 2 presents the individual indicators that form the aggregate indicator; worksheet 3 presents the aggregate indicator and the classification of the municipalities; worksheet 4 provides data correlation; worksheets 5 to 10 depict boxplot graphs of the data; and worksheets 11 to 14 present graphs of individual indicators on a per capita basis and the ranking of municipalities. The datasheets also include data for the calculation of individual and aggregate indicators, which were collected from government and non-governmental organizations datasets. Timespan for the data is 1 year.•Worksheet 1 (entitled “WASTE DATA”): Data about municipal solid waste generation and the average distance to the landfill and/or recycling/composting plant of 150 small municipalities  in the state of São Paulo, Brazil;•Worksheet 2 (entitled “INDICATORS”): Socioeconomic and individual indicators data resulting from the survey and modeling in the Waste Reduction Model (WARM);•Worksheet 3 (entitled “MULTIPLE INDICATOR”): normalized individual indicators, aggregate environmental performance indicator and the classification of municipalities. This worksheet also contains two graphs, one referring to the general classification of municipalities, and the other referring to the composition of the normalized indicators of each municipality;•Worksheet 4 (entitled “SPEARMAN CORRELATION”): Spearman correlation matrix between indicators. This worksheet also contains a graph of the correlations between variables;•Worksheet 5 (entitled “ENERGY GRAPH”): descriptive summary of energy consumption (kWh) data and their outliers. This worksheet also contains a boxplot graph of the data;•2e) data and their outliers. This worksheet also contains a boxplot graph of the data;Worksheet 6 (entitled “CO2E GRAPH”): descriptive summary of carbon dioxide equivalent (CO•Worksheet 7 (entitled “POPULATION GRAPH”): descriptive summary of population data. This worksheet also contains a boxplot graph of the data;•Worksheet 8 (entitled “GDP GRAPH”): descriptive summary of Gross Domestic Product (GDP) data and their outliers. This worksheet also contains a boxplot graph of the data;•Worksheet 9 (entitled “WQI GRAPH”): descriptive summary of Waste Quality Index (WQI) data and their outliers. This worksheet also contains a boxplot graph of the data;•Worksheet 10 (entitled “WASTE GENERATION GRAPH”): descriptive summary of waste generation data and their outliers. This worksheet also contains a boxplot graph of the data;•Worksheet 11 (entitled “ENERGY RANK”): annual per capita indicator data and the ranking of municipalities for energy consumption. This worksheet also contains a graph of the ranking;•2e. This worksheet also contains a graph of the ranking;Worksheet 12 (entitled “CO2E RANK”): annual per capita indicator data and the ranking of municipalities for CO•Worksheet 13 (entitled “WQI RANK”): annual per capita indicator data and the ranking of municipalities for WQI. This worksheet also contains a graph of the ranking;•Worksheet 14 (entitled “WASTE GENERATION RANK”): annual per capita indicator data and the ranking of municipalities for waste generation. This worksheet also contains a graph of the ranking;The article contains a spreadsheet data file (.xlsx format) with the following data tabs:22.1This study adopted a secondary data extraction method to obtain data from small municipalities  in the State of São Paulo. The inclusion criteria consisted of all necessary data for the performance of life cycle assessment, such as waste generation, composition, recycling and composting rate, being available for the municipality. Of a total of 645 municipalities in São Paulo State, 273 are small municipalities, and of these, a total of 123 municipalities were excluded from the sample due to unviable, corrupted, vitiated, unrealistic, or partially available data, resulting in 150 municipalities being analyzed .2.2Each municipality considered was analyzed based on the waste management practices included in its municipal plans of basic sanitation or solid waste plans. Data was gathered in structured spreadsheets and they present the total amount of each type of waste and their destination, the amount of waste that is recycled and composted, and the distance traveled by the waste to the landfill, composting, and/or recycling units. The general gravimetric composition of municipal solid waste was collected from solid waste plans for small municipalities in São Paulo State.2.3For assessment of individual indicators data, the Waste Reduction Model (WARM), Version 14, from the United States Environmental Protection Agency, a widely applied model in many different cities around the world , was use2.42 equivalent, which comprises the set of gases that contribute to global warming, and the energy consumption [At the final stage, the environmental impacts data of municipalities are presented in the spreadsheets as COsumption .2.52e, the per capita energy consumption, and the Waste Quality Index (WQI), developed by CETESB [An aggregate data performance indicator is presented in the spreadsheet. The components of the aggregate indicator are the per capita waste generation, which is widely used , the pery CETESB . The WQI2.6i,max and xi,min are the maximum and minimum values based on the data, for each indicator (i), respectively.Each indicator (I) was normalized using the relative maximum and minimum values Equation  from theI is the normalized indicator of solid waste generation (kg inhabitant−1 year−1), GHGI is the normalized indicator of CO2e emission, ECI is the normalized indicator of energy consumption and WQII is the normalized indicator of Waste Quality Index (WQI).After normalization, the aggregation was performed by computing the geometric mean of the normalized indicators, according to Equation All collected and processed data are presented in the spreadsheet.


Evolutionary relationships between prodomains in the TGF-β family have gone unanalyzed due to a perceived lack of conservation. We developed a novel approach, identified these relationships, and suggest hypotheses for new regulatory mechanisms in TGF-β signaling. First, a quantitative analysis placed each family member from flies, mice, and nematodes into the Activin, BMP, or TGF-β subfamily. Second, we defined the prodomain and ligand via the consensus cleavage site. Third, we generated alignments and trees from the prodomain, ligand, and full-length sequences independently for each subfamily. Prodomain alignments revealed that six structural features of 17 are well conserved: three in the straitjacket and three in the arm. Alignments also revealed unexpected cysteine conservation in the “LTBP-Association region” upstream of the straitjacket and in β8 of the bowtie in 14 proteins from all three subfamilies. In prodomain trees, eight clusters across all three subfamilies were present that were not seen in the ligand or full-length trees, suggesting prodomain-mediated cross-subfamily heterodimerization. Consistency between cysteine conservation and prodomain clustering provides support for heterodimerization predictions. Overall, our analysis suggests that cross-subfamily interactions are more common than currently appreciated and our predictions generate numerous testable hypotheses about TGF-β function and evolution. SECRETED TGF-β family members perform a myriad of tasks during development and homeostasis, while mutations disrupting TGF-β pathways can lead to disease. The mouse genome encodes 33 TGF-β family members, the fly encodes seven, and the nematode encodes five . StructuOne means of generating hypotheses for multigene families is to ascertain evolutionary relationships via phylogenetics. This approach has successfully predicted new mechanisms of TGF-β regulation twice. First, Smad linker phosphorylation was predicted  then valTwenty years ago the first phylogenetic study of TGF-β ligands employed fly, mouse, and nematode proteins, before all three genomes were available , but an increase in resolution of prodomain trees. In our view, cystine knot clustering suggests common receptor binding and common function, while prodomain clustering suggests heterodimerization and common regulation.The implementation of the consensus cleavage site led to the movement of a highly degenerate region between the cleavage site and the first cysteine out of the prodomain and into the ligand. This resulted in a reduction in the resolution of our ligand trees  LTBP association region” upstream of the straitjacket and in β8 of the arm in 14 proteins belonging to all three subfamilies. In the prodomain trees, eight clusters across all three subfamilies were present that were not seen in the ligand or full-length trees, suggesting prodomain-mediated cross-subfamily heterodimerization. Consistency between cysteine conservation and prodomain clustering provides support for our heterodimerization predictions.Caenorhabditis elegans , Drosophila melanogaster , and Mus musculus  was identified. Two species are coelomates with three germ layers and a digestive tract with two openings: M. musculus is a deuterostome (blastopore becomes the anus) and D. melanogaster is a protostome (blastopore becomes the mouth). C. elegans is a pseudocoelomate with three germ layers and a digestive tract with one opening. The split between deuterostomes and protostomes was roughly 964 MYA and between coelomates and pseudocoelomates 1.298 billion years ago  was based on identifying the site analogous to the consensus Furin cleavage site in Dpp  in Figures S1–S3. A cystine knot tree for all family members, where the ligand begins at the first cysteine, is included for comparison to the cleavage site defined ligand tree in Figure S2.https://www.ebi.ac.uk/Tools/msa/clustalo/). Alignments depicting sequence conservation were generated in BoxShade3.21 (ch.embnet.org/software/BOX_form.html) as described  plus mouse GDNF. The prodomain alignment was 983 amino acids including gaps, and there were 185 informative sites without gaps. The ligand alignment was 204 amino acids including gaps, and there were 76 informative sites without gaps. The full-length alignment was 1147 amino acids including gaps, and there were 262 informative sites without gaps.We analyzed 12 sequences  plus mouse GDNF. The prodomain alignment was 838 amino acids including gaps, and there were 230 informative sites without gaps. The ligand alignment was 167 amino acids including gaps, and there were 101 informative sites without gaps. The full-length alignment was 929 amino acids including gaps, and there were 358 informative sites without gaps.We analyzed 23 sequences  plus mouse GDNF. The prodomain alignment was 1116 amino acids including gaps, and there were 24 informative sites without gaps. The ligand alignment was 214 amino acids including gaps, and there were 76 informative sites without gaps. The full-length alignment was 1302 amino acids including gaps, and there were 101 informative sites without gaps.We analyzed 22 sequences  plus mouse GDNF. The prodomain alignment was 787 amino acids including gaps, and there were 335 informative sites without gaps. The ligand alignment was 166 amino acids including gaps, and there were 108 informative sites without gaps. The full-length alignment was 870 amino acids including gaps, and there were 415 informative sites without gaps.We analyzed 45 sequences  plus mouse GDNF. The prodomain alignment was 1265 amino acids including gaps, and there were 554 informative sites without gaps. The ligand alignment was 229 amino acids including gaps, and there were 142 informative sites without gaps. The full-length alignment was 1414 amino acids including gaps, and there were 641 informative sites without gaps. Cystine knot alignment was 168 amino acids including gaps, and there were 114 informative sites without gaps.Trees were created in MrBayes3.2  and the For alignments with >150 informative positions  a posterior probability of 0.95 is statistically significant. For alignments with fewer informative positions, simulation studies . We started with alignments of three sets of recent mammalian duplications that always cluster together and are always distinct from others representing the TGF-β, Activin, and BMP subfamilies . Note that the phrase “recent mammalian duplicates” indicates only that these duplications are not present in flies and nematodes.Then, we added sequences one at a time to each subfamily alignment using the most current version of Clustal Omega  and TGF-β subfamilies (Maverick and Dawdle). For mice, Nodal is firmly placed in the BMP subfamily yet our trees will suggest a hypothesis to explain its ability to signal through the Activin pathway receptors ActRIB and ActRIIA/B and signal transducer Smad2 reviewed .Employing a Bayesian approach . Only two of the 46 sequences (45 TGF-β family members plus the mouse GDNF outgroup) did not contain a region with strong similarity to the consensus RX[R/K]R . Nematodes showed minor differences in three cleavage sites . An examination of the consensus divergence times between each pair revealed that the fly estimate is 4.7 MYA, the mouse estimate is 4.8 MYA, and the nematode estimate is 60.2 MYA . Length variation spanned the range from 2 residues  to 80 residues . However, in hypervariable regions any conservation likely is functional or evidence of recent duplication. For example, 8 of 11 Activin subfamily members have an acidic residue (D/E) immediately upstream of the first cysteine in the ligand (72% with 10% expected by chance). Only 1 of the other 35 sequences has a glutamic acid at this position. The BMP subfamily has no obvious amino acid conservation but length identity is visible in the recently duplicated Gbb and Screw as well as the mammalian duplicates BMP6, 7, 8a,b. The TGF-β family is home to the two newest duplications as revealed by the presence of both sequence and length identity for Lefty1 and 2 and TGF-β1–3. Overall spacer hypervariability is consistent with structural data showing it sits outside the prodomain-ligand complex  and expanded alignments for each subfamily .This subfamily  is builtKnown features such as α-helices and β-sheets were located on the alignment revealing pockets of structural conservation in the annotated Activin alignments  show the most conservation. There is a set of nine I/L/V residues, of which four are universal. There is also a universal proline in the Latency Lasso. β1 contains two conserved I/L/V residues and a phenylalanine. In the arm domain, the helices β2–10 and α4 show less conservation. Most notable are three I/L/V residues in β4, a near universal tryptophan in β6 and near universal I/L/V residues in α4 and β7. This correlation of amino acid conservation with structural features had not been demonstrated rigorously in the Activin subfamily.This subfamily  is builtAreas of structural conservation are evident in the annotated TGF-β alignments (vs. CxC). β10 is modestly conserved. The distinct patterns of conservation in the Activin and TGF-β subfamilies support the idea that they are separate.At the 5′ end of the arm domain, there are places within helices β2–6 and α4 that show more conservation than the Activin subfamily. β2 contains a near universal F/W, while β3 contains a near universal Alanine and I/L/V. β6 and α4 each have a near universal tryptophan and a near universal I/L/V. At the 3′ end of the arm, unexpectedly in β8 (part of the distinctive bowtie) Maverick and Dawdle have a pair of cysteines that align with those in TGF-β1–3, although the spacing is not the same .The combined Activin + TGF-β subfamily  containsIn the prodomain, one cross-subfamily cluster contains three of the four fly family members . Further, as a group they are tightly tied in a second cross-subfamily cluster to TGF-β1–3 and Myostatin/GDF11. The group of four Inhibin-β proteins is the next closest cluster. Dawdle ends up as a solo next to Inhibin-α. The clustering of the three fly proteins with Dawdle as an outlier is reminiscent of the Inhibin-β group’s relationship with Inhibin-α, proteins that are known to heterodimerize. The analogy is that Dawdle can bind to Activin, Maverick, and Myoglianin and that these heterodimers have a distinct function (possibly inhibition) from the four homodimers (possibly activation).On the other hand, in the ligand and full-length trees there are no cross-subfamily clusters, the Activin subfamily and TGF-β subfamily relationships are simply recreated. For example, Activin is with the four Inhibin-β proteins and Myoglianin is with Myostatin/GDF11. Overall for the Activin + TGF-β subfamily, similarity between the ligand tree and the full-length tree indicates that functional relationships of the ligand are driving its evolution.The combined Activin + TGF-β subfamily alignment contains four features where the subfamilies differ, further supporting the conclusion that these are distinct . In all eight proteins the first cysteine is aligned. The fact that Activin, Dawdle, Maverick, Inhibin-βa, and Inhibin-βb have a pair of similarly spaced cysteines in β8 that mediates dimerization in TGF-β1, is consistent with the prodomain clusters that suggested cross-subfamily heterodimerization of Activin with Dawdle and Maverick. Importantly, the cysteines suggest a biochemical mechanism by which heterodimerization can be achieved.Again unexpectedly in β8, Maverick and Dawdle in the TGF-β subfamily are joined by Activin, Inhibin-βa, and Inhibin-βb in the Activin subfamily, with a pair of conserved cysteines having the same spacing  have names that do not fit their association with GDF proteins. The prodomain tree suggests that perhaps each major cluster has a distinct mode of regulation.Interestingly, the overall topology of the BMP prodomain tree is different from the others. In the ligand and full-length trees there are two asymmetric secondary clusters, everyone In the ligand tree Nodal is significantly paired with nematode DBL-1 and then loosely with the mammalian pair BMP10/GDF2. The tight association between mouse Nodal and DBL-1 in the absence of any fly protein is curious. Nodal’s distinct ligand and prodomain associations lead it to be a loner in the full-length tree. Otherwise, the full-length BMP tree largely shows previously established clusters. Overall for the BMP subfamily, similarity between the ligand and full-length trees indicate that functional relationships of the ligand are driving its evolution.The BMP subfamily appears more homogeneous than the Activin or TGF-β subfamilies in the annotated alignments  are not conserved. β9′ is a unique BMP feature containing a moderately conserved arginine and an I/L/V. β10 contains two near universal I/L/V residues and a modestly conserved phenylalanine. Overall, in the BMP subfamily the 5′ ends are more highly conserved than the 3′.Trees of the whole family  includinvs. 0.95 for significance). What this modest shortfall means is that this is the best, but not the only possible placement of Activin in the tree. This unexpected cluster forms a second , larger cross-subfamily cluster with Nodal and its closest GDF relatives in the BMP subfamily. While not conclusive, the first cluster suggests the hypothesis that the prodomains of Activin, Gbb, and Screw are similar as a result of the shared ability to heterodimerize with Dpp. The inclusion of BMP5–8a,b and Nodal with its closest GDF relatives in the larger second cluster suggests a level of similarity not found in the wider family. The most parsimonious hypothesis for this similarity is intracluster heterodimerization, implying numerous functional heterodimers yet to be identified.In the prodomain trees, there are six cross-subfamily clusters. One has Activin in a cluster with Gbb and Screw that heterodimerize with Dpp, although the node is just short of significant (0.78 vs. 0.95 for significance). This group contains the BMP subfamily members GDF3/GDF1 joined with absolute confidence (node = 1.0) to the TGF-β subfamily members Inhibin-α/GDF15. Together these form a fourth cross-subfamily cluster with significant, or just below significant, nodes to the TGF-β proteins and five Activin subfamily members . This cluster suggests that these prodomains are similar to Inhibin-α as result of the ability to heterodimerize. This prediction is supported by Nodal heterodimers with GDF1/GDF3  with heterodimerizing Inhibin-β proteins. A sixth cross-subfamily cluster is TIG-3 (Activin subfamily) with heterodimerizing Gbb/Screw. These clusters suggest possible heterodimerization for these nematode proteins, the only prediction of this type for this species.For the biochemically defined ligand tree, aside from recent duplications such as Lefty1/2, only three small secondary and three small tertiary clusters are seen. These are all composed of the most conserved proteins such as Activin/Inhibin-β group, Dpp/BMP2/4, and Gbb/Screw/BMP5–8a,b. Ten proteins are solos and Nodal is again paired with DBL-1.By comparison, the cystine knot tree shows better resolution with only five proteins as solos. Unsurprisingly, secondary clusters of the same highly conserved proteins are visible. Surprisingly, Nodal is again paired with DBL-1. This is because the spacer of Nodal and DBL-1 shows unexpected conservation. The 11 amino acids closest to the first cysteine in DBL-1 contain seven of the 10 amino acids in the Nodal spacer (Table S3). This likely explains their consistent pairing in the biochemically defined ligand and cystine knot trees.In the full-length tree, the Activin subfamily members BMP3/GDF10 are not quite significantly associated with the expanded group of BMP subfamily members. However, they show the same level of association with the TGF-β subfamily in the prodomain and are solos in the cystine knot and ligand trees. This combination of placements suggests that for BMP3/GDF10 their regulation and function share features of distinct subfamilies that will need to be identified by experiment. The pair of BMP subfamily members GDF1/GDF3 that heterodimerize with Nodal also have features of multiple subfamilies. They are significantly clustered to the TGF-β subfamily members Inhibin-α/GDF15 in the prodomain tree, solos in the cystine knot and ligand trees, and not quite significantly associated with the expanded group of BMP subfamily members in the full-length tree. As noted above, the association of GDF1/GDF3 prodomains with Inhibin-α/GDF15 may explain why Nodal heterodimers with GDF1/GDF3 can signal through ActRIIA.The placement of Nodal and DBL-1 as solos in the full-length tree is distinct from the ligand and cystine knot trees where they are a significant pair and the prodomain tree where they are essentially unlinked. These two proteins likely have homologous receptors but distinct regulation and it is a specific combination of regulation and function not found in any fly protein.In the full-length tree, four fly proteins Activin and Myoglianin in the Activin subfamily, plus Dawdle and Maverick in the TGF-β subfamily, are solos . This contrasts with the prodomain tree where Activin is in a weak BMP cluster and Myoglianin in a weak TGF-β cluster. Alternatively, these two are weakly linked to an Activin + TGF-β cluster in the cystine knot and ligand trees. Their distinct placement in these trees suggests the four fly proteins have mechanisms of regulation yet to be identified. Overall, the many solos in the full-length trees result from dissimilarity between the cystine knot, ligand, and prodomain trees. This indicates that functional and regulatory relationships are equally driving the evolution of the TGF-β family.The conservation pattern in the subfamilies is reiterated in the annotated All family members alignment  and a tyrosine. These are not conserved in the TGF−β subfamily or the Activin + TGF-β subfamily. Alternatively, two to four of these amino acids (VL_LY) are nearly universal in the BMP subfamily. BMP conservation has driven the alignment of All family members to identify these amino acids in α2 of the other subfamilies. In other words, the All family members alignment erases the distinction in the location of α2 in the Activin and TGF-β subfamilies. In β1, one I/L/V and a phenylalanine present in the Activin + BMP subfamilies are absent in the TGF-β subfamily, maintaining the prior distinction in β1 location and conservation.The 5′ end of the arm is not well conserved. β2 contains only a modestly conserved phenylalanine. β3 is conserved in an Activin + BMP pattern that draws in several TGF-β subfamily members, although not TGF-β1. The previously identified stretch of seven conserved residues in β3 is reduced to six having lost the glutamic acid. β4 conservation is also reduced, with its stretch of seven residues now only three: two I/L/V residues and an F/Y. β4 is absent in Nodal and seven other BMP subfamily members. The middle region of the arm, β6 to α4 is the best conserved part, yet it too shows a reduction. The highly conserved stretch of seven consecutive residues is at most four and often just two or three. The near universal tryptophan that was previously the first of the seven conserved residues is now separated by two or more nonconserved amino acids from the core of two to four residues . The aspartic acid is well conserved. The near universal tryptophan at the distal end of α4 is still present.At the 3′ end of the arm conservation is also limited. β7, like β3, is conserved in an Activin + BMP pattern that draws in several TGF-β subfamily members, although not TGF-β1. β7 retains two moderately conserved I/L/V residues of three previously. β10 is the best conserved feature in the region, perhaps anchoring the 3′ end of the protein. There are two near universal and a modestly conserved I/L/V plus a modestly conserved proline. Overall, the 5′ end of the straitjacket  plus the central β6 to α4 and 3′ end of the arm (β10) are the six best conserved features of the 17 prodomain features in the TGF-β family.Within β8 of the bowtie of TGF-β1 is the previously noted conservation of a pair of cysteines in eight Activin + TGF-β subfamily members . Unexpectedly, three proteins in the BMP subfamily  have a single cysteine in β8 that aligns with the second cysteine of the CxxC  or protein–protein interactions (“LTBP-Association region”). Many of these cysteine containing proteins are predicted by prodomain clustering to heterodimerize such as Activin and Dawdle. Interestingly, both of these proteins have conserved cysteines in β8 and the “LTBP-Association region”, suggesting the possibility of multiple heterodimerization partners.Across the prodomain alignments, distinctions in the conservation of structural features between the subfamilies are seen in both the straitjacket and arm domains. In the straitjacket there are discrepancies between the Activin and TGF-β subfamilies in the locations of α2 and β1. At the boundary of the straitjacket and arm, a third distinction between the Activin and TGF-β subfamilies is the order of α3 and β3 (Activin has β3 first and TGF-β has α3 first). In the arm there are three additional differences. β3 and β7 show dissimilarities between the Activin + BMP subfamilies and the TGF-β subfamily. β9′ is distinct between the BMP and Activin + TGF-β subfamilies. If any functional differences are engendered by these structural distinctions, then they are unknown at this time.The discovery of a conserved pair of cysteines in β8 in five Activin + TGF-β subfamily members and a single conserved cysteine in three BMP subfamily members is exciting. From an evolutionary perspective two points can be made. First, the presence of conserved cysteines in β8 in all subfamilies suggests that prodomain participation in protein–protein interactions is an ancient mechanism. The closed-ring conformation of TGF-β1 employing a bowtie in β8/β9 to mediate dimerization is a recent innovation built upon this foundation. Second, the nonuniversality of β8 cysteine conservation suggests significant within-subfamily structural variation between cysteine-bearing and noncysteine-bearing proteins. Structures are known only in the Activin subfamily for Inhibin-βa that has these cysteines and in the BMP subfamily only for BMP9 that does not have them. Analysis of additional family members may reveal additional conformations.Similar excitement is generated by the discovery of a conserved pair of cysteines in the “LTBP-Association region” in eight Activin + TGF-β subfamily members and a single conserved cysteine in two additional members of the TGF-β subfamily. Interestingly, no BMP subfamily members have conserved cysteines here. One caveat is that prodomain length upstream of the straitjacket in the BMP subfamily is highly variable . There might be functional cysteines that are not close enough to the Activin + TGF-β subfamily cysteines to be captured in the alignment.The presence of conserved cysteines in all Inhibin-βs and Inhibin-α suggests the obvious hypothesis that they participate in cross-subfamily heterodimerization. A structural analysis of these heterodimers should be fruitful A logical extension of this hypothesis is the heterodimerization of Activin and Dawdle. For the latter, beyond the “LTBP-Association region” this hypothesis is supported by three pieces of evidence: LTBP does not exist in flies and cannot utilize this cysteine, Activin-Dawdle heterodimerization was predicted in numerous prodomain trees, and these proteins also share β8 cysteine conservation. When four lines of computational evidence converge, confidence in the hypothesis is very high.a priori that we considered prodomain clustering as evidence of heterodimerization and thus common regulation, our review of existing literature in light of the identified clusters suggests that heterodimerization can also influence function. There are a number of examples where heterodimers function distinctly from constituent homodimers, with the Inhibins being the most prominent. Here we consider prodomain clustering, whether within or between subfamilies, to suggest new hypotheses for common regulation and/or distinct function via heterodimerization.Although previously we noted One hypothesis for a distinct function of heterodimers suggests a mechanism for TGF-β ligands’ ability to stimulate their receptors to activate non-Smad pathways such as the MAP-kinase, Rho-like GTPase, and PI3-kinase/AKT pathways. Currently, cell type–specific accessory proteins such as Par6 are considered responsible for a receptor’s choice of signal transduction pathway [reviewed in In addition to non-Smad pathway activation, functional discrepancies for subfamily dedicated receptors and receptor-associated Smads have been noted. In flies, the Activin/TGF-β dedicated Type I receptor Baboon can signal through the BMP Smad protein Mad . Also inOverall, we identified six cross-subfamily and two within-subfamily clusters that suggest previously unsuspected heterodimers. In every cross-subfamily cluster at least one protein with an unexpectedly conserved cysteine is involved. For example Activin, that has both association region and β8 cysteines participates in multiple cross-subfamily clusters.To date, there is no evidence in the literature that consideration has been given to the possibility that Activin functions as a heterodimer. This is surprising since Activin owes its name to its closest relatives, the heterodimerizing Inhibin-β proteins (Inhibin-βa synonym is Activin-A). The prodomain trees contain clusters that suggest multiple heterodimer partners for Activin.First is the prodomain cross-subfamily cluster of Activin, Myoglianin, and Maverick in the Activin + TGF-β tree. This cluster has strong statistical support. In the same tree Dawdle is adjacent to Inhibin-α, the heterodimerization partner of Activin’s Inhibin-β relatives. The Dawdle and Inhibin-α relationship is statistically significant. This pair of clusters suggests that Activin as well as Myoglianin and Maverick can form heterodimers with Dawdle. The heterodimerization predicted by this cross-subfamily cluster is strongly supported by structural conservation: conserved cysteines in β8 of Activin, Maverick, and Dawdle; and conserved cysteines in the “LTBP-Association region” of Activin and Dawdle.e.g., Second is the prodomain cross-subfamily cluster in the All family members tree of Activin with Gbb and Screw, two proteins known to heterodimerize with Dpp. While not quite at statistical significance, the explanatory power of this cluster is welcome. Recently Dpp from imaginal tissues was shown to circulate in the hemolymph to reach the prothoracic gland where it influenced steroid hormone biosynthesis via its typical pathway  exceeds that of documented homologs Dpp/BMP2  and Dpp/BMP4 , notwithstanding the 30% longer divergence time between nematodes and mammals than between flies and mammals .A new hypothesis for DAF-7 is provided by two sets of cross-subfamily clusters in the All family members prodomain tree: DAF-7 with the Activin subfamily heterodimerizing Inhibin-β group and TIG-3 with heterodimerizing Gbb/Screw. Together these two clusters suggest the possibility of cross-subfamily heterodimerization between DAF-7 and the unstudied TIG-3.In summary, the prodomain alignments revealed that six structural features are well conserved: three in the straitjacket and three in the arm. Alignments also revealed unexpected cysteine conservation in the “LTBP-Association region” upstream of the straitjacket and in β8 of the bowtie in 14 proteins from all three subfamilies. In prodomain trees, eight clusters across all three subfamilies were present that were not seen in the ligand or full-length trees, suggesting prodomain-mediated cross-subfamily heterodimerization. Consistency between cysteine conservation and prodomain clustering provides support for heterodimerization predictions. Overall, our analysis suggests that cross-subfamily interactions are more common than currently appreciated, and our predictions generate numerous testable hypotheses about TGF-β function and evolution.