Background: Pregnant women are at an increased risk of hospitalisation, admission to the intensive care unit, mechanical ventilation, and death from SARS-CoV-2 infection. The aim of this study is to determine the predictive factors associated with COVID-19 vaccine uptake during pregnancy over time in a population with a high background uptake of maternal influenza and pertussis vaccination. Methods: This is a population-based, cohort study of all pregnant women who gave birth in Victoria, Australia between 1 July 2021 and 30 June 2022. Data from the Victorian Perinatal Data Collection were analysed using univariable and multivariable logistic regression. Results: This study reports on 77,719 women who gave birth over a 12 month period, of whom 49,281 (63.4%) received a COVID-19 vaccine, 54,887 (70.6%) received an influenza vaccination and 63,594 (81.8%) received a pertussis vaccine by the time of delivery. Pregnant women aged >30 years , who had >=8 antenatal visits , and those who received influenza vaccine  were more likely to have received a COVID-19 vaccine. Those who smoked , were First Nations  and those who gave birth in public hospitals  were less likely to receive COVID-19 vaccine in the first 12 months of the rollout. Conclusion: Maternal age, smoking, parity and Indigenous status were factors associated with delayed and sustained lower coverage, even in a population with background maternal influenza and pertussis coverage of 70.6% and 81.8%, respectively. The World Health Organization (WHO) declared the novel coronavirus (SARS-CoV-2) a worldwide pandemic on 11 March 2020. By this time, it had spread rapidly to almost every country, with the first known case in Australia arriving in Melbourne on the 25 January 2020 [Vaccine development during the COVID-19 pandemic was swift and utilised novel technologies, resulting in several vaccines using different vaccine platforms . In AustAs vaccine development progressed, many reports emerged during the first year of the pandemic suggesting that pregnant women were at an increased risk of severe disease ,6,7. It On 9 June 2021, the Australian Technical Advisory Group on Immunisation (ATAGI) and the Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG) released a joint statement acknowledging the increased risk of severe disease and routinely recommended vaccination in pregnancy . From 18In Australia, two other maternal vaccines are routinely recommended in every pregnancy: inactivated Influenza Virus (IIV), diphtheria, tetanus and acellular pertussis (dTpa). IIV is recommended with each pregnancy at any gestational age, whereas dTpa vaccine is recommended in each pregnancy from 20 weeks gestation . The aim of this study was to track the uptake of a new maternal vaccine (COVID-19) over the first 12 months from the time of national recommendation across a population with high coverage of routine maternal vaccinations (influenza and pertussis) to determine the predictive factors associated with early adoption. Furthermore, we sought to compare the coverage of COVID-19 vaccines with the coverage of influenza and pertussis vaccines over the same time period. The study was a population-based, cohort study of data on births from 1 July 2021 to 30 June 2022 (the first 12 months of the COVID-19 vaccine rollout that included pregnant women). Data were collected using the Victorian Perinatal Data Collection (VPDC).The VPDC is a mandated data collection system under the Public Health and Wellbeing Act 2008, whereby all public and private hospitals and private-practising midwives provide data to the VPDC on all births in Victoria. Over 160 data items are collected in compliance with the National Perinatal Minimum Data Set. The VPDC is held by the Consultative Council on Obstetric and Paediatric Mortality and Morbidity. The purpose of the VPDC is to provide population-wide data for the improvement of maternal and infant health outcomes. The data in the VPDC is required to be reported within 30 days of the birth event. This requirement exists for all births, including hospital and at-home births .Four items related to COVID-19 vaccination status during pregnancy were added to the VPDC for all births from 1 July 2021: whether the woman had ever received a COVID-19 vaccine; whether she had received one or more COVID-19 vaccines during pregnancy; the gestation of the first dose received during pregnancy; and the gestation of the second dose received during pregnancy. Gestational age of first or second dose were grouped into trimesters . Australian-born status was determined based on the maternal country of birth and was defined as either Australian-born or not Australian-born. Maternal age , gestational age , parity  and the number of antenatal visits were categorised. The number of antenatal visits was categorised as <8 or >=8. This cut-off was derived from the WHO guidelines indicating that women should have a minimum of eight contacts/visits during pregnancy . AustralA variable was generated to enable a comparison of women who received a vaccine during pregnancy in the first 6 months of availability (“early adopters”) with those who received it after the first 6 months of availability (“late adopters”) and those who had not received a COVID-19 vaccine by the time of birth. All women who gave birth between July and December 2021 were included in the first category, along with those who gave birth between January and June 2022, but had received at least one COVID-19 vaccination in 2021. All others who received a vaccine during pregnancy but whose first dose was in 2022 were allocated to the second group. Women with missing data and those who with only COVID-19 vaccinations before pregnancy were allocated to the ‘other’ category.p-values < 0.05 were considered statistically significant.Each birth is a separate record in the VPDC, meaning that women appear twice when they have multiple births. We excluded cases with birth order >1 to include women only once in the maternal analyses. Those who declined to answer were considered as “missing”. We used descriptive statistics to analyse the relationship between maternal and maternity care characteristics, receiving at least one COVID-19 vaccination before birth, and the relationship between these characteristics and early adoption of the vaccine. To explore whether the demographic factors associated with delayed and low uptake of COVID-19 vaccines were similar to an existing vaccine or whether this was specific to COVID-19 vaccines, we examined the factors associated with influenza vaccination during pregnancy. We chose to compare it to influenza as historically coverage in pregnancy has been lower than that in pertussis. Proportions were compared using the Chi-square test, and Univariable and multivariable logistic regressions were used to describe the odds ratio of receiving at least one COVID-19 vaccination before birth after adjusting for several maternal and maternity care characteristics. Odds ratios, adjusted odds ratios and 95% confidence intervals were reported. Covariates included in the multivariable analysis were maternal age, whether the woman was born in Australia, Aboriginal status, whether the birth was in a public or private hospital, parity, maternal smoking during pregnancy, number of antenatal visits (<8 versus 8 or more) and whether the woman received influenza vaccine during the pregnancy. Characteristics known to be associated with the uptake of other vaccines during pregnancy were included in the multivariable model. Where collinearity was found, one of the collinear variables was selected for inclusion in the model. Pertussis and influenza vaccination could not both be included due to collinearity, so influenza was selected given historically lower coverage rates, and it is a vaccine directed against a respiratory virus for both maternal and infant benefits.This project was approved by the Monash University Human Research Ethics Committee (project ID 30927).Between 1 July 2021 and 30 June 2022, 77,719 women gave birth to 78,822 babies in Victoria. A total of 49,281 (63.4%) received a COVID-19 vaccine at the time of delivery, 26,099 (33.6%) were not vaccinated and 1828 (2.4%) declined to answer. Vaccination status was unknown for a small fraction of women (excluding those who declined to answer) with only 511 (0.6%) not known. In total, 46,281 women (59.3%) had at least one COVID-19 vaccination during pregnancy, and 2731 were vaccinated before pregnancy only. The overall coverage for influenza vaccination for the same period was 70.6% , and for pertussis, it was 81.8% . p < 0.001). Maternal age, smoking, parity and First Nations status were factors associated with delayed and sustained lower coverage a–d. We compared this uptake of COVID-19 vaccines with the influenza vaccine to examine if there were similar trends according to maternal factors . Women younger than 20 years, First Nations women, women pregnant with their third or subsequent child and smokers were also more likely to have low uptake of influenza.The key findings in this study that analyses the uptake of COVID-19 vaccines in pregnant women over the first 12 months of the rollout are as follows: 1. The overall uptake was significantly lower compared to other recommended vaccines during pregnancy (IIV and dTPa). 2. Uptake was slower in younger age groups, those who smoked, First Nations populations and women who were pregnant with their third or subsequent child. 3. Uptake was higher in women aged >30, who received influenza vaccine, who had =>8 antenatal visits, who were pregnant with their first baby or who delivered in the private system 4. The overall coverage of influenza was lower in the same groups, who were also slower to take up the COVID-19 vaccine. The background maternal vaccine coverage of influenza and pertussis in the setting where this study took place is relatively high compared to the remainder of Australia  . This suOur results demonstrate that after approximately six months of the vaccine being available, uptake plateaued and remained lower in groups known to be at a higher risk of severe disease . These findings have also been reported in non-pregnant populations with these risk factors . In JanuSo, how do we interpret these ‘factors’ associated with either slow or low uptake of maternal vaccines and how do we use this information to inform public health policy? For the identification of groups with delayed and sustained low coverage in the antenatal setting, we need to consider targeted public health messages and information working in partnership. For vaccines recommended during pregnancy, this may include delivery via Aboriginal Health Services rather than the antenatal care provider if they are not the same. For smokers, the messages may need to focus on strategies to maximise health rather than just focusing on tobacco consumption, and given the differences found between first and subsequent pregnancies in uptake, messages may need to be tailored to explain the importance of repeat vaccination in each pregnancy. The strength of healthcare provider recommendations and motivation from peers ,22,23 caWhat does this mean for future maternal vaccines such as respiratory syncytial virus (RSV) and group B streptococcus (GBS)? These two new maternal vaccines have either completed phase 3 clinical trials or are about to start, with one maternal RSV vaccine recently licensed and available for use in some countries ,27. ThisOur study has strengths and limitations. The main strength is that the perinatal data collection system used in Victoria, Australia, introduced mandatory reporting of COVID-19 vaccination status to its perinatal data collection from 1 July 2021. This allowed us to observe and report on the trends over the first 12 months of the rollout correlating with when pregnant women were recommended and able to access free COVID-19 vaccines. The other strength of this dataset is that it includes every pregnancy, not restricted to live births, across the private and public healthcare settings. This allowed for the timely analysis of COVID-19 vaccine coverage based on demographics. The limitation of this study is that it is observational in nature; therefore, not all possible confounders contributing to uptake can be accounted for, such as occupation, educational level, comorbidities and socioeconomic status. As an example, the number of antenatal visits was categorised into <8 or >=8 as a surrogate for the opportunity of vaccination and engagement in healthcare systems. However, a pregnant woman may have had more visits because she was at a ‘high-risk’ pregnancy, and therefore may have had a healthcare provider recommendation or other risk factors for severe COVID-19 disease leading to earlier vaccination, or she may have had fewer visits because she gave birth preterm. Public health messages need to consider investing greater effort in younger women who may perceive themselves as “healthy”;Providers need to understand that women in their third or more pregnancy may need more information about “why” they are still recommended to receive vaccines when they may have received vaccines in prior pregnancies;Women with risk factors for respiratory illness such as smokers may need targeted messaging-, e.g., “if you are unable to stop smoking during pregnancy then there are measures you can consider to improve your health and that of your baby”;English proficiency, rather than country of birth, influences maternal vaccine uptake.The considerations for maternal immunisation programmes based on factors associated with low uptake in this study are outlined below:Maternal age, smoking, parity and Indigenous status were factors associated with delayed and sustained lower coverage. Identifying these groups will help in directing public health messaging not just for existing maternal vaccines but new maternal vaccines on the horizon.


Heterotrophic microbes play an important role in the Earth System as key drivers of major biogeochemical cycles. Specifically, the consumption rate of organic matter is set by the interaction between diverse microbial communities and the chemical and physical environment in which they reside. Modeling these dynamics requires reducing the complexity of microbial communities and linking directly with biogeochemical functions. Microbial metabolic functional guilds provide one approach for reducing microbial complexity and incorporating microbial biogeochemical functions into models. However, we lack a way to identify these guilds. In this study, we present a method for defining metabolic functional guilds from annotated genomes, which are derived from both uncultured and cultured organisms. This method utilizes an Aspect Bernoulli (AB) model and was tested on three large genomic datasets with 1,733–3,840 genomes each. Ecologically relevant microbial metabolic functional guilds were identified including guilds related to DMSP degradation, dissimilatory nitrate reduction to ammonia, and motile copiotrophy. This method presents a way to generate hypotheses about functions co-occurring within individual microbes without relying on cultured representatives. Applying the concept of metabolic functional guilds to environmental samples will provide new insight into the role that heterotrophic microbial communities play in setting rates of carbon cycling. However, we currently have limited knowledge of what sets the rate of organic matter cycling  control the rate at which organic compounds are cycled Pomeroy, , which hGlobal ecological models, which are used to study large-scale carbon cycling, typically consider the impact of microbial heterotrophy to be a constant or a bulk approximation acting on a generic organic carbon pool . Analysis of functional diversity has either relied upon ‘omics analyses Venter,  or closeTara Oceans circumnavigation expedition  . In addition, 967 isolate genomes and 980 genomes with unresolved provenance  were obtained from the MarDB  (accessed 31 May 2018). A composite genomic dataset was generated using the Tara Oceans MAGs, isolates, and MarDB genomes . To compare and contrast the guilds derived from different methods of genome reconstruction, two additional datasets were used. The 1,859 known MAGs from the composite dataset were separated out into a second dataset, and the 1,7333 high-quality SAGs from the GORG-Tropics database were separated out into a third dataset.Three different sources of genomes were used for this analysis, MAGs, isolate genomes , and SAGs. Specifically, we used 1,859 MAGs  . fastAAI also used Pyrodigal  and average amino acid identity (AAI). ANI values were computed on the whole genomes using fastANI v1.33 . It is important to note that the AB method presented here is not restricted to this number of functions and can be extended to include as many functions or hypothetical proteins as the user desires. Furthermore, genome annotations can be performed in any manner the user desires so long as the resulting data matrix is binary. However, we emphasize that the choice of annotations is paramount in determining the types of metabolic signals the user can receive when running this method. This is a discovery-based dimension reduction method and as such can only directly identify patterns based on the data presented to it.This large binary dataset was used as input for metabolic guild identification both using classical methods and our new Aspect Bernoulli (AB)-based method  Kruskal,  of the fFinally, we sought a method that could reduce our data to a lower number of dimensions with defined and clear separation into clusters of functions that represent metabolic guilds. Therefore, it was essential that our method could identify signals of metabolic guilds driven by relatively rare functions even in the presence of high abundance functions such as core carbon metabolism or housekeeping genes. This aspect was important because we expected many of these core metabolisms to strongly co-occur due to their essential nature and thus could potentially limit our ability to define more biogeochemically relevant metabolic functional guilds. We found that an augmented AB model was able to best accommodate all of these requirements. We present this model and the underlying statistical method that defines this approach in the following section.Y∈RG × F. The AB model was selected as it is designed for sparse matrices of binary data. AB is similar to Latent Dirichlet Allocation (LDA) that has been applied to similar problems [e.g., topic modeling, population structure  algorithm as described in Bingham et al.  as detailed in the study by Bingham et al. ; (2) genomes that were identified as being associated with the aspect k were likely to contain functions at the top of aspect k's list . The functions that combined to define a metabolic functional guild could then be identified based on high-ranking functions in the aspect lists.In order to define metabolic functional guilds  . This 2/K threshold ensured that we excluded genomes that had nearly uniform Γ vectors. For our composite dataset, this threshold did not exclude any genomes.Next, we identified the genomes that were most strongly associated with each aspect . We will hereafter refer to this set of genomes Ak, we calculated qfk:From Ak and the mean abundance within Ak. Finally, we multiplied the marginal probability rfk (Equation 1) by the adjustment factor qfk (Equation 4). This gave us the score metric sfk that we used to identify our guilds:which is the ratio of the abundance of each function within qfk upweights functions f that are more abundant among probabilistic representatives of aspect k than average  using a fixed number of functions, five functions in this case  asA key objective of the pipeline was to identify functions co-occurring within individual genomes that were meaningfully associated. Ideally, for a guild YgA and YgB are the presence or absence of A and B in genome g. High confidence values suggested that the presence of function B was highly conserved with that of function A. We computed the forward and reverse confidence values for every pair of functions in the guilds identified from our data. Because of the way we defined mapback genomes, these confidence values were all 1 within our mapback genomes and ranged between 0 and 1 for our ‘outgroup' genomes .where A and B are functions from our dataset and K, is a free parameter in the AB model that determines the maximum number of guilds that can be identified. The ideal choice of K is dataset specific and is a function of the underlying structure of the data matrix. To test the impact of this choice on the resulting guilds identified by our method, we constructed a large collection of synthetic datasets comprised of either one or three artificial guilds appended to our original composite dataset of 3,840 genomes and 212 functions. These guilds were defined to be “perfect” guilds when genomes either had all the artificial guild functions or none of them. For example, an artificial guild with 5 functions and 2% total abundance in the dataset would have all 5 functions perfectly co-occurring in 77 genomes, while the remaining 3,763 genomes would not possess any of these artificial functions . Guild parameters were drawn from three possible abundances  and three possible sizes  with all unique combinations tested  were excluded due to insufficient marker gene coverage. Another 39 genomes that were included in the tree were flagged during the quality assessment step for high redundancy estimates (an average of 16.7% redundancy) but were still highly complete (an average of 95.7% completeness). Of the 3,775 high-quality genomes, there were 3,529 bacterial genomes representing 51 unique bacterial phyla. Among these phyla were the key marine superphylum Proteobacteria  to our dataset and assessed their ability to extract the low-dimensional structure of co-occurring functions in the form of guilds. The results of the NMDS are shown in We applied two classic statistical methods  using the Jaccard distance metric with complete linkage and two different cut heights, 0.9 and 1  were in a single guild of highly abundant core functions. We also tested clustergram with a cut height of 1 that produced 17 distinct clusters of functions. The average number of functions in a cluster increased to an average value of 11.1 (ranging from 2 to 66 functions) but the number of mapback genomes dropped sharply to an average of just 3.2 mapback genomes (ranging from 0 to 17 genomes) per guild. Seven of these guilds had no mapback genomes, and the two largest guilds alone accounted for 46.7% of the total data used for this clustering procedure.Applying c guilds . These cclustergram to that of the AB model and demonstrate that both methods identify similar guilds but that clustergram both breaks the AB guilds up into smaller groups (fewer functions) and results in guilds with fewer mapback genomes. Thus, the AB method can better capture metabolic functional guilds that contain a meaningful number of functions (>3) with substantial numbers of mapback genomes.We identified several disadvantages of the classic statistical methods. First, large numbers of core metabolisms found in many genomes  formed huge guilds with few mapback genomes, which were therefore not informative as metabolic guilds see . Second,In the following sections, we present an assessment of the robustness of the AB model for detecting guilds, a summary of the AB model guilds from the composite dataset, and then a comparison between the AB model and the classic methods.K prior to running the algorithm. To test the impact of the choice of K on the ability to detect different-sized guilds  and guilds with different abundances in the dataset , we ran the artificial datasets through the method with a wide range of K values . This analysis  that are being targeted in the analysis. For this study, we manually assessed guilds derived from K values within the identified range in order to select our final value of K (K = 10). We recommend that a similar analysis be performed prior to applying this method to a new dataset.The AB model requires the user to define K values using two metrics: hit rate and extra hits. The hit rate describes the overall frequency with which we identified our artificial guilds. In the ideal case, we would observe all of an artificial guild's functions present at the top of the score-ordered function list (top 15) in exactly one aspect. Thus, for a simulation using three distinct artificial guilds, we would expect to see three hits per simulated dataset , which would give us a 100% hit rate, or a hit rate frequency of 1. Extra hits catalog instances where we observed an artificial guild occurring at the top of more than one aspect list, i.e., an artificial guild being divided across two aspects.We quantified the ability of our method to identify artificial guilds in our artificial datasets (see Section 2) over a range of K values . On the other hand, if K was too large, guilds showed up in multiple aspects (over-fitting system). We concluded that a good range for K was around the point where the hit rate was maximized while extra hits remained zero. A full analysis of the impact of guild size, guild abundance, and K value on guild identification, as well as the impact of randomly inserting guilds and the number of artificial guilds inserted, is presented in The size of the guild and abundance of the guild in the dataset impacted the ability of the method to identify artificial guilds at different K values . As guilK = 10, the likelihood appeared to plateau at its maximum value after ~500 iterations  algorithm implemented as detailed by Bingham et al.  to detererations . We alsoerations .The AB method successfully identified guilds within the composite dataset that were found in a substantial number of genomes in the dataset and contained functions that were specific to that guild (see Section 2). When defined using the top 5 scoring functions (approach 1), the resulting guilds averaged 116.2 mapback genomes (ranging from 11 to 468 genomes). When guilds were defined to include functions co-occurring within at least 100 genomes (approach 2), the average guild size was 5.7 functions per guild (ranging from 2 to 20 functions). K = 10) of the minimum confidence values was 0.09 (ranging from 0.029 to 0.132). In other words, functions A and B in guild k were found together only ~10% of the time in the non-mapback genomes and 100% of the time in the mapback genomes. Guilds defined using approach 2 (~100 mapback genomes) had a 0.338 average confidence value in the outgroup genomes and a 0.029 (ranging from 0 to 0.105) average minimum confidence value. Both approaches for defining guilds resulted in guilds comprised of functions that were specific to that guild. When looking at the co-occurrence of each pair of functions from the guild set of functions (guild function pairs), low confidence values were observed in the outgroup genomes for each guild function pair as compared to the value of 1 for the guild function pairs in the mapback genomes (by definition). Guilds identified using approach 1 (top 5 scoring functions) had a 0.455 average confidence value in the outgroup genomes. However, many pairs of functions were substantially less conserved in the outgroup genomes . For this, we looked at the minimum outgroup confidence value across all pairs of functions in each guild . For approach 1, the average across all 10 guilds  as the AB method (N = 10). Of these 30 clustergram guilds, the majority (60% of the guilds) possessed three or fewer functions with 33.3% of the guilds constituting just a pair of functions. When we used the conservative criteria of at least 100 mapback genomes per guild (approach 2), the AB method generated a comparable number of guilds with 3 or fewer functions . However, the two methods differ substantially in terms of number of mapback genomes identified for each guild. Clustergram yielded guilds with an average of 38.8 mapback genomes per guild, substantially less than the two AB methods which averaged 116.2 and 142.9 mapback genomes for approaches 1 and 2, respectively. When we reduced the threshold for AB approach 2 to the clustergram average of 39 mapback genomes per guild, we found just one guild with three or fewer functions . To make a more direct comparison to the clustergram guilds, we re-ran the AB pipeline with K = 30. Allowing for a higher number of guilds in the AB method resulted in a similar number of mapback genomes per guild as the runs with K = 10 with an average of 113 mapback genomes (ranging from 0 to 1436) for approach 1 and with only one guild having no mapbacks. However, when K = 30, the AB method resulted in a high frequency of duplicate guilds, either fully duplicated or partially duplicated (see We compared the guild sizes and mapback genome numbers of the clustergram cut height to 1 (clustergram compared to 10 for AB) between the different methods. A cut height of 1 reduced the number of small clustergram guilds (3 or fewer functions) to 41.2%. However, this even further decreased the number of mapback genomes for each guild (an average of 3.2 genomes per guild with some guilds having no mapbacks). For both cut heights, clustergram identified one guild with 42 functions (cut height = 0.9) and 66 functions (cut height = 1), which correspond to 19.8% and 31.1% of all functions in the dataset, respectively. This large guild was comprised entirely of highly abundant functions and was substantially larger than the largest guild produced by AB approach 2 (28 functions using the lower threshold of 39 or more mapback genomes). Furthermore, the large clustergram guild had just 4 and 0 mapback genomes for cut heights of 0.9 and 1, respectively, while the 28-function AB guild had 61 mapback genomes. Finally, we attempted using a dynamic cut height method for clustering functions which improved the guild sizes and number of mapback genomes over the static height but still resulted in guilds with fewer mapback genomes than the AB guilds  with only two of the ten AB guilds being contained within a single clustergram cluster. When we examined the clustergram guilds that contain the AB guild functions, we found that they average 52.8 mapback genomes compared to 116.2 for the corresponding AB guilds. This suggests that the AB method can identify groups of functions that are more commonly found together in the dataset.We next assessed the differences in guilds functions identified by the two methods using AB approach 1 where guilds were defined with a static number of functions. We observed several reoccurring patterns. When using a cut height of 0.9 for clustergram clusters and marginally reduced the fragmentation of AB guilds between clustergram guilds with AB guilds now being split across 1.7 clustergram guilds on average (ranging from 1 to 3 guilds). At this linkage, the clustergram guilds which contained the AB guild functions had on average 30 additional functions (ranging from 5.5 to 61) and only 0.33 mapback genomes (ranging from 0 to 1) compared to the corresponding AB guilds which had 116.2 mapback genomes (ranging from 11 to 468). There were several instances (4 of 10), where the AB guild functions clustered fully or partially into the large clustergram guild with 66 functions containing the highly abundant functions in the dataset with no mapback genomes.Increasing the cut height to 1 resulted in fewer K, which determines the number of guilds identified (see discussion above in Section 3.3.1). However, we demonstrate how a user can use our pipeline to make an informed decision as to how to choose the best value of K. Another key distinction between the two methods is that clustering methods precisely define the functions belonging to each guild. The AB method provides information both about which functions are strong indicators of the guild and which genomes have a high probability of membership in the guild. The user must then decide which set of functions to define as a guild. We provide two approaches for making this distinction and highlight how this additional information generated by the AB method can be used to generate hypotheses (see discussion below in Section 4.1). Additional advantages to the AB method are that the AB method does not require all functions to be members of a guild or a function to be a member of just one guild and that the AB method can distinguish between false and true absences/presences in the dataset. Finally, it is important to note for the AB method that if there are mapback genomes for a guild then the guild is by definition meaningful . However, the absence of a guild does not necessitate that that guild does not exist. The AB method might not have identified a guild for several other reasons, including other structures in the data matrix which can make rare guilds difficult to find, or the absence of a key annotation that is crucial for distinguishing it from the rest of the dataset.This analysis demonstrated that both the AB and clustering methods can identify functional guilds from our dataset and that there was an overlap in the functions that were grouped together into guilds using the two methods. We showed that the AB guilds both contained more functions and were more highly represented in the dataset  than the guilds defined using the clustering method. As with any method, there are both advantages and disadvantages to the AB method. One disadvantage of the AB method is the need to choose a value of the free parameter a priori knowledge. As such, the identification of known guilds  is a strong indication that the method can detect biologically meaningful phenomena even when these associations are in low abundance in the dataset. In this study, we highlight three emergent guilds and draw connections to previously identified co-occurring biochemical processes. The other seven guilds identified by the method are also of significance (11–235 mapback genomes) and are listed in Our approach identified several biogeochemically relevant metabolic functional guilds with numerous genome representatives in the composite dataset. It is important to note that these guilds emerged from this analysis without any curation or N = 95) and 2.7% (N = 105) of the genomes, respectively. However, our method was able to identify a photosynthesis guild with 10 total functions including photosystems I and II, NAD(P)H quinone oxidoreductase, cytochrome b6f complex, and RuBisCO (The photosynthetic functions served as a good test case of our method. Our composite dataset was curated in such a way that photosystems I and II were only present in 2.5% ( RuBisCO . This 10The approach identified a guild related to the consumption of the organic sulfur compound dimethylsulfoniopropionate (DMSP). This guild consisted of DMSP demethylation, DMSP lyase, and sulfite dehydrogenase (quinone), and had 139 mapback genomes. These three functions were the highest-ranked functions within a single aspect . For thiR. pomeroyi strain DSS-3 and two phytoplankton species, Landa et al.  resulted in 98 mapback genomes.The AB method suggests that there are several additional functions that might commonly co-occur with these three DMSP-related functions . For exaa et al.  demonstra et al. . The nita et al.  and has a et al. . TherefoThiosulfate oxidation also occurs in the top 15 ranked score list (rank 15). Previous experimental study has shown that this pathway is involved in DMSP degradation , CP-lyase operon (PhnFKLMNOP), CP-lyase cleavage (PhnJ), and a phosphonate transporter (PhnCED). The C-P lyase pathway has been shown to break down a variety of phosphonate bonds, including phosphonates associated with semi-labile high molecular weight dissolved organic matter (Metcalf and Wanner, K is a crucial free parameter that needs to be selected for each novel dataset to which this method is applied. We recommend constraining K using a similar heuristic approach to the one we described above or using other previously suggested methods such as the Akaike information criterion (deLeeuw, The guilds identified by our method were an emergent property of the dataset itself. This means that the absence of a known or potential guild in the model output does not necessarily mean that guild was not present in the dataset. Using a different collection of annotated genomes could potentially change the abundances of the functions within the dataset, which could greatly impact whether the method identified a specific group of functions as a guild or not. For example, we demonstrated that guilds with abundances of 2% or lower were difficult to consistently observe. Furthermore, as discussed above, deLeeuw, .The co-occurrence of metabolic functions has long been studied in the field of biochemistry where metabolic pathways are elucidated. However, these studies are typically very labor-intensive and require cultured representatives. This can present an issue since only a small fraction of marine microbes have been cultured (Rappé and Giovannoni, In addition to generating testable hypotheses, this method presents several potential future applications. One possibility is in assisting with genome annotation through the incorporation of hypothetical gene products that have not yet been functionally characterized. One recent study (Faure et al., Understanding microbial metabolic functional guilds is an essential step in describing microbial communities based on their metabolic activity, particularly for key heterotrophic communities. Rather than focusing on the functional composition of the entire community, our method identifies collections of co-occurring functions that form the building blocks of a community's functional structure. Defining the community as such will allow us to develop improved numerical ecosystem models that capture these metabolic capabilities. In addition, it will help us to better build and validate models, such as the trait-based ecosystem model GENOME described in Coles et al.  study, thttps://github.com/LevineLab/AB-guilds_model; https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA391943 BioProject ID PRJNA391943, https://mmp.sfb.uit.no/databases/; https://www.ncbi.nlm.nih.gov/bioproject/572885 BioProject ID PRJEB33281.Publicly available datasets were analyzed in this study. This data can be found here: RR and NL designed the project. RR, SH, NL, and JB developed and tested the AB model. RR and BT compiled the datasets and conducted the taxonomic classification, quality assessment, and phylogenetic analysis of the genomes. RR conducted the model simulations and guild analyses. All authors contributed to the writing of the manuscript, article, and approved the submitted version.


Underage drinking and related risky sexual behavior (RSB) are major public health concerns on United States college campuses. Although technology-delivered personalized feedback interventions (PFIs) are considered a best practice for individual-level campus alcohol prevention, there is room for improving the effectiveness of this approach with regard to alcohol-related RSB.The aims of this study are to (1) evaluate the impact of a brief PFI that integrates content on alcohol use and RSB and is adapted to include a novel cross-tailored dynamic feedback (CDF) component for at-risk first-year college students and (2) identify implementation factors critical to the CDF’s success to facilitate future scale-up in campus settings.This study uses a hybrid type 1 effectiveness-implementation design and will be conducted in 3 phases. Phase 1 is a stakeholder-engaged PFI+CDF adaptation guided by focus groups and usability testing. In phase 2, 600 first-year college students who drink and are sexually active will be recruited from 2 sites (n=300 per site) to participate in a 4-group randomized controlled trial to examine the effectiveness of PFI+CDF in reducing alcohol-related RSB. Eligible participants will complete a baseline survey during the first week of the semester and follow-up surveys at 1, 2, 3, 6, and 13 months post baseline. Phase 3 is a qualitative evaluation with stakeholders to better understand relevant implementation factors.Recruitment and enrollment for phase 1 began in January 2022. Recruitment for phases 2 and 3 is planned for the summer of 2023 and 2024, respectively. Upon collection of data, the effectiveness of PFI+CDF will be examined, and factors critical to implementation will be evaluated.This hybrid type 1 trial is designed to impact the field by testing an innovative adaptation that extends evidence-based alcohol programs to reduce alcohol-related RSB and provides insights related to implementation to bridge the gap between research and practice at the university level.ClinicalTrials.gov NCT05011903; https://clinicaltrials.gov/ct2/show/NCT05011903DERR1-10.2196/43986 Despite declines over the past several decades, underage drinking and related harm continue to be major public health concerns on United States college campuses -3. NatioThe first weeks of college are referred to as the red zone with escalations in heavy drinking -18 and sNIAAA’s College Alcohol Intervention Matrix identifies technology-delivered personalized feedback interventions (PFIs) as a best practice for individual-level campus alcohol prevention . PFIs tyPast work from the investigative team ,43,44 hiThe prevailing approach to technology-delivered PFIs is to provide a single, point-in-time snapshot of one’s behaviors and beliefs relative to others, analogous to a “between-person” comparison, which can create initial discrepancy and enhance motivation to change . HoweverOver the past 3 decades, researchers have made strides to enhance brief alcohol intervention implementation through a number of key adaptations , a comprehensive set of constructs describing factors influencing implementation across all stages of the implementation process ,70. CFIRPhase 1 will focus on adapting the existing integrated PFI to the enhanced PFI+CDF with the following steps: (1) focus groups with multilevel stakeholders, including students and student affairs staff; (2) building the data capture and content platform; (3) usability testing; and (4) program refinements. These 4 key iterative steps of adaptation are crucial in the systematic modification of existing evidence-based interventions to improve their effects and potential for implementation . FurtherWe will conduct focus groups with stakeholders  to solicit their perspectives on a prototype of the proposed PFI+CDF intervention. Focus group participants will be students and student affairs professionals  at UK and UNT, as well as student affairs professionals at the national level, recruited at the annual meeting of NASPA-Student Affairs Administrators in Higher Education, the premier conference in this field . RecruitAt UK and UNT, we anticipate a single focus group for staff and 3 focus groups for students, with 5-8 participants per group . At NASPA, we will aim for 4 focus groups with 5-8 participants per group, for a total of 32 participants. Development of the focus group interview guide will be informed by the CFIR interview guide tool  and willWe will conduct a systematic thematic analysis to identify relevant themes from the qualitative data collections . Focus gFindings from the multilevel stakeholder focus groups will inform the development of an updated version of an existing evidence-based integrated alcohol and RSB feedback profile (PFI) and the addition of CDF content, as well as an assessment and delivery platform necessary to populate and house the PFI and CDF content.The PFI and CDF content will be housed in a website application platform that offers flexibility in reach and accessibility but is optimized for viewing across device types . For example, links to PFI and CDF content can be sent to participants via multiple channels  to access feedback. The assessment and content platform will be built as an integrated system, essential for scale-up for several reasons: (1) survey responses inform both the PFI and CDF content and are a necessary part of the system, (2) it allows for a built-in evaluation mechanism, and (3) it creates one overall system that is portable for hosting at other sites. The underlying programming will be conducted in HTML5 to ensure flexibility for future adaptations to other platforms. The web-based app will run on common web browsers on iOS and Android smartphones, tablets, and on iOS and Windows personal computers. A backend tracking program will record user progress.Consistent with prior work, PFI content will be generated from responses to a baseline assessment and offer participants integrated feedback on both alcohol use and RSB across the following content domains: a behavioral profile, normative comparisons, blood alcohol content, expectancies, perceived risk, likelihood of engaging in future risk behaviors, and protective behavioral strategies. In addition to integrated alcohol and RSB content, the PFI will include an option to view feedback on alcohol use behaviors only, as pilot data indicated separate alcohol-focused content was necessary to reduce alcohol use not specific to sexual behavior.CDF content will be generated from weekly diary-style surveys (Friday to Monday) in which students report their alcohol use and RSB, as well as their preferences when interacting with CDF content. Participants will receive dynamic feedback on their own behaviors for 12 weeks of the fall semester, allowing them to see their behaviors over time and receive feedback on their behavior changes . Although the overall system and specific design features will be created in consultation with students and technology developers, and subsequently refined following the usability testing, it is anticipated that the CDF content will include (1) a brief and updated behavior profile, (2) normative feedback on their current weekly drinking and related sexual behavior pattern, (3) positive reinforcement for improving behaviors within weekly and monthly time frames in figures, (4) brief updated intervention content topics  along with an option to see a full version, and (5) alerts for potential areas for improvement and referral information, if needed.Usability testing will focus on (1) soliciting feedback on users’ experience with the enhanced PFI+CDF and (2) troubleshooting the CDF diary surveys and dynamic weekly feedback methodology. The usability test will be conducted with the methodology, assessments, and timing of the RCT procedures for the PFI+CDF group (see phase 2 description below), with the exception that the testing does not need to start at the beginning of the academic year or include follow-up surveys. Participants  will provide standard ratings of the acceptability   or 3 or more drinking occasions within the past monthSexually active Not in a monogamous relationship  or in a monogamous relationship (dating only 1 person) for less than 3 months or in a monogamous relationship but open to cheatingNot pregnant or planning to become pregnantFollowing the baseline survey, participants randomized to 1 of the 3 PFI conditions will immediately be linked to their PFI, which will also be sent via email and SMS text message. A 2-week window is allotted at the beginning of the semester for participants to complete their baseline assessment and, if applicable, view their PFI content. Following that period, participants in the PFI+CDF and PFI+GHI will be sent prompts via email and SMS text message to complete diary surveys on Friday through Monday of each week. On Tuesdays, participants in the PFI+CDF group will receive a link to access their CDF corresponding to that week’s behavior, via text and email, and those in the PFI+GHI group will receive a link to access health topics relevant to college students guided by the American College Health Association website  . These pIn addition to self-report measures, participant behavior analytics will be captured to help ensure intervention fidelity and be included as covariates in analyses. Analytics include (1) whether or not participants accessed their PFI, CDF, or GHI; (2) the number of times each PFI, CDF, or GHI was accessed; (3) time spent viewing each PFI, CDF, or GHI; and (4) time spent viewing each topic area within the PFI, CDF, or GHI. These data will be captured through tracking routines programmed on the website. We will examine the depth of college students’ engagement with intervention content to assess the effects of program exposure  based on the level of access to content  and time spent on various intervention components. These data will provide intervention acceptability metrics and allow us to control each participant’s exposure to the PFI, CDF, or GHI content in outcome analyses.Power analyses to detect intervention main effects were conducted using Optimal Design Plus Version 3.01 software and the “power determination approach.” Based on past research, we assumed a standardized effect size of 0.3  from the repeated measures RCT with 5 follow-ups over 13 months. To examine the effect of the PFI+CDF and PFI interventions, compared to the control, a minimum of 452 participants are required for a power of 0.8 at an alpha level of .05. To examine the effect of the PFI+CDF intervention (vs PFI+GHI or PFI-only intervention), at least 350 participants are required. Based on this power analysis, there is sufficient power (≥0.89) to detect a small to medium effect size for the integrated interventions  versus control and sufficient power to detect the small effect of the PFI+CDF intervention compared to the PFI+GHI or PFI-only intervention over time. Although the overall effect size was conservatively assumed to fall between small and medium in size, due to the fine-grained assessment approach within the time period where most changes are likely to occur, followed by a long-term 13-month follow-up assessment, we anticipate that the proposed study is sufficiently powered.For study phase 2, we will use a multilevel model for the main outcome analysis where the participant is defined at the highest level of the model (level 2), and observations (level 1) are nested within the participant. We will assume that the observation-level (level 1) residual error term and participant-level (level 2) intercept coefficients  are each normally distributed with mean zero and a variance that is estimated from the data, while intercept and slope coefficients are multivariate normally distributed with mean zero for each, and a covariance matrix that is estimated from the data. Intervention groups will be tested as a fixed effect. Given that many outcomes of our interest are count outcomes with excessive zeros, we will utilize appropriate models to accommodate a zero-inflated outcome distribution with overdispersion ,84. The We will use a parallel process growth model to test the codevelopment of correlated processes  and a parallel process growth mediation model. This allows for the examination of 2 potentially dependent reduction trajectories, which are altered by intervention. We expect that those who reduce alcohol misuse more rapidly in response to intervention will also exhibit a trajectory in which one’s likelihood of engaging in RSB decreases, which can be tested by comparing a model where the 2 slope terms are correlated with a null model where its correlation is constrained to be zero. Intervention effects can be explicitly tested by adding intervention membership as an external covariate that changes these coupled processes. Intervention effect modifiers or moderators will be tested in a confirmatory  or exploratory investigations.A qualitative postintervention assessment of intervention implementation will be conducted with multilevel stakeholders, including students and both campus- and national-level student affairs staff. The goals are to better understand (1) participating students’ experiences with the intervention; (2) barriers and facilitators to implementation, guided by CFIR; (3) potential program sustainability; and (4) necessary modifications for other sites for scale-up.Focus group participants will be a subset of students from the PFI+CDF arm of the RCT, as well as campus- and national-level stakeholders who also participated in preimplementation focus groups. Participants in the PFI+CDF condition will be asked if they are interested in participating in a follow-up study after the 13-month follow-up. We will randomly select up to 48 students who indicate interest in participating (n=24 per site) at varying levels of engagement with the intervention  for a maximum of 3 focus groups per site (5-8 participants per group). Campus student affairs staff will be contacted via phone and email for staff focus groups (1 focus group per site for student affairs staff with 5-8 participants per group). At the national level, we will conduct 4 focus groups, with 5-8 participants per group, yielding a total of up to 32 participants. All focus groups will last 60-90 minutes with US $50 and US $100 gift card incentives for students and staff, respectively. The analytic plan for phase 3 qualitative data will mirror phase 1.The study underwent review and approval by the UK Institutional Review Board (IRB #62769), which serves as the single IRB of record for this study. All participants will sign an approved consent form in accordance with the ethical standards of Helsinki.Recruitment and enrollment for phase 1 began in January 2022. The findings of phase 1 will inform the development and build of integrated PFI+CDF. It is anticipated that phase 2 will begin in August 2023, and phase 3 will begin in October 2024. Data will be shared in the NIAAA Data Archive twice a year (April 1 and October 1), starting from October 1, 2023. Findings will be published in peer-reviewed journals and presented at international, national, or regional professional meetings and conferences.This project aims to fill a gap highlighted in the NIAAA Strategic Plan 2017-2021  by advanThere are several potential limitations that need to be acknowledged. First, the emphasis on first-year college students may be restrictive. We chose to focus on first-year students because they are at risk for alcohol use and RSB -26, but This innovative, multisite hybrid effectiveness-implementation study will (1) evaluate the impact of a PFI that integrates content on alcohol use and RSB and is adapted to include an innovative CDF component for at-risk first-year college students and (2) identify implementation factors critical to its success to facilitate future scale-up in campus settings. The findings of this study will improve existing evidence-based alcohol programs and bridge the gap between research and practice at the university level.


We also make available structural models for ~150 thousand non-redundant paired antibody sequences (10.5281/zenodo.7258553).Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of deep learning models trained to accurately predict the structure of antibodies (ABodyBuilder2), nanobodies (NanoBodyBuilder2) and T-Cell receptors (TCRBuilder2). We show that ImmuneBuilder generates structures with state of the art accuracy while being far faster than AlphaFold2. For example, on a benchmark of 34 recently solved antibodies, ABodyBuilder2 predicts CDR-H3 loops with an RMSD of 2.81Å, a 0.09Å improvement over AlphaFold-Multimer, while being over a hundred times faster. Similar results are also achieved for nanobodies,  and TCRs. By predicting an ensemble of structures, ImmuneBuilder also gives an error estimate for every residue in its final prediction. ImmuneBuilder is made freely available, both to download ( ImmuneBuilder is a set of deep learning models trained to predict the structure of antibodies, nanobodies, and T-Cell receptors with state-of-the-art accuracy while being much faster than AlphaFold2 and AlphaFoldMultimer. Nanobodies, single-domain antibodies naturally found in organisms such as camelids and sharks, have also received significant interest as therapeutics, with a recently accepted nanobody drug and a number undergoing clinical trials4.The adaptive immune system in humans is effective at identifying and neutralising a wide range of pathogens. To achieve this, immune cells have developed antigen-specific proteins such as T-cell receptors (TCRs) or, in the case of B-cells, antibodies. While antibodies are capable of binding with great affinity and specificity to the surface of almost any antigen, TCRs target digested pieces of intracellular proteins that are presented on the cell surface by the major histocompatibility complex. Due to their key role in identifying a wide range of antigens, antibodies and TCRs have become proteins of particular interest for therapeutic development, with several TCR drugs in clinical trialsα and Vβ), or being found at the tip of one Ig domain, in the case of nanobodies.All three of these immune proteins are built up from immunoglobulin (Ig) domains with the binding site either sitting across two Ig domains in the case of antibodies (VH and VL) and TCRs . In nanobodies, the binding site is concentrated in only three CDR loops on its single Ig domain. These CDR loops show variable length, composition and structure with the most variable being CDR-H3 in the case of antibodies and nanobodies8.Despite the similarities in the global structure of antibodies, TCRs and nanobodies, their binding sites are known to have distinct properties and their CDRs have different length distributions as well as occupying distinct areas of structural space13, but structural information allows for a more in-depth understanding than studies focused on sequence alone14. For example, knowledge of CDR loop conformations has been used to help identify antibodies that bind to similar targets15, while accurate knowledge of side chain atom placement can aid in identifying key interactions in antibody-antigen binding17.As with many proteins, the availability of sequence data far outstrips structural information18. Computationally predicted structural models can be used to circumvent this problem. This is particularly the case for immune proteins, as next-generation sequencing of immune receptor repertories is now routinely used in the study of the adaptive immune system20. These methods enable researchers to obtain millions of sequences per study, making structural analysis of this data a challenge. For example, Observed Antibody Space (OAS) contains over two billion antibody heavy chain sequences and is growing rapidly10. If this huge amount of sequence data is to even partially be analysed in terms of structure, rapid accurate methods for the prediction of antibody structures are required.Experimental structure determination is time-consuming and expensive21. This was then extended to AlphaFold-Multimer to accurately predict protein complexes22. Many methods have followed from AlphaFold2 and AlphaFold-Multimer but these remain the de facto gold standard for single domains and complexes25.AlphaFold2 is a deep learning method that has revolutionised the field of computational protein structure prediction, achieving near experimental accuracy for a large number of single-chain proteinsThe AlphaFold2 model can be divided into two main steps: In the first step, the Evoformer module is used to extract evolutionary couplings from alignments of many protein sequences into information-rich embeddings. It then uses these embeddings in the structure module to predict the 3D structure of a given protein sequence.27. By using knowledge specific to a type of protein, they can easily predict the conserved regions in that protein allowing greater focus on harder details. For example, DeepH3 was shown to outperform TrRosetta on antibodies29, while Nanonet obtains results of similar accuracy to AlphaFold2 on nanobodies with a far simpler architecture30. More recent examples of this are IgFold25 and EquiFold31, where the authors trained antibody-specific models that predict structures of comparable accuracy to AlphaFold-Multimer.Structure prediction methods specific to a certain class of protein tend to outperform more general methodsIn this paper, we present ImmuneBuilder, a set of deep learning models developed to predict the structure of proteins of the immune system. By training on specific protein types, we are able to create rapid accurate models, enabling ImmuneBuilder to be routinely used on large sequence data sets. We have built three models, ABodyBuilder2, an antibody-specific model, NanoBodyBuilder2, a nanobody-specific model and TCRBuilder2 a TCR-specific model. We show that these methods perform at least as well as state-of-the-art methods for their respective protein types while predicting structures in a fraction of the time. We also demonstrate that these methods both accurately predict details of the structure and create physically and biologically sensible structures.The three ImmuneBuilder models are made freely available for download and as web servers.Throughout the results section we will focus on the results for ABodyBuilder2 (AB2) on antibodies with the results for NanoBodyBuilder2 (nanobodies) and TCRBuilder2 (TCRs) discussed in Supplementary Notes 32 (ABB)), one general protein structure prediction method (AlphaFold-Multimer22 (AFM)), and three antibody-specific methods (ABlooper33 (ABL), IgFold25 (IgF) and EquiFold31 (EqF)). As a benchmark, we selected a non-redundant set of 34 antibody structures recently added to SAbDAb13 (see methods). This was done so none of the antibody structures in the benchmark would have been seen during training for any of the benchmarked methods. To give a complete picture of how these methods perform, we carryout a comprehensive benchmark using five different measures. Figure We compare ABodyBuilder2 to several other methods. These methods are a homology modelling method  and around 1Å for protein loopsABodyBuilder2 and AlphaFold-Multimer are the most accurate methods at predicting the structure of CDR-H3 . EquiFold, IgFold and ABlooper generate structures with CDR-H3 loops around 10% less accurate than ABodyBuilder2 and AlphaFold-Multimer. The worst method for predicting CDR-H3 loops is the original version of ABodyBuilder, showcasing how deep learning has improved our ability to model. Supplementary Note 36 and TCRBuilder26 for TCR modelling, and MOE37 and ABodyBuilder32 for nanobody modelling) and machine learning methods AlphaFold-Multimer22 for TCRs and AlphaFold221 for nanobodies. Supplementary Fig. Tables As described in the introduction, in antibodies the binding site sits across the heavy and light chain variable regions (VH and VL). With half of the CDRs on each chain, the relative VH-VL orientation can have an impact on the structure of the binding site.38. A brief description of how these values are defined is given in Supplementary Note 38. The results for TCR domains are given in Supplementary Table To quantify how accurate each method is at predicting the relative orientation between chains, in Table 38, the vector dc was chosen as an axis as it was found to be the most conserved amongst antibody structures. All of the benchmarked methods predict this distance with very high accuracy. All methods are also accurate at predicting the angles, with small errors with respect to what is observed in experiments. However, small deviations in these angles will still have an impact on the structure of the binding site. ABodyBuilder2 is on average the most accurate method at heavy and light chain packing by a small margin.As an upper bound for the accuracy of predicted structures, the average standard deviation of the VH-VL orientation measurements in 55 antibodies with structures resolved over five times is shown in Table 39, where a side-chain torsion angle is considered correct if it is within 40 degrees of the true conformation. The original implementation of ABodyBuilder will occasionally fail to predict a side chain, this is treated as an incorrect prediction. A residue is labelled as buried if its relative solvent accessibility  is below 7.5%. The results of this analysis are given in Table During binding, an antigen will mostly form interactions via side chain atoms on the surface. Therefore to be able to study antigen binding, predicted antibody structures must accurately model the position of side chain atoms and whether they are exposed on the surface or buried. To benchmark the accuracy of side-chain modelling we use a method similar to ref. 41 and Rosetta42, respectively), it is perhaps not surprising that they are the least accurate at modelling the chemical surface. EquiFold, AlphaFold-Multimer and ABodyBuilder2, all of which output all-atom structures, predict the χ1 and χ2 side chain atoms with high accuracy while struggling to model longer side chains. The original implementation of ABodyBuilder predicts side chains with comparable accuracy to AlphaFold-Multimer and ABodyBuilder2. All methods are highly accurate at predicting whether a residue is exposed or buried, EquiFold is the most accurate.As ABlooper and IgFold are deep learning methods that only predict the backbone .To further demonstrate the usefulness of ImmuneBuilder, we predicted the structure of around 148 thousand non-redundant paired antibody sequences from OASIn the methods section we describe in detail the data and models for creating ABodyBuilder2. An overview of the steps used to predict an antibody structure with ABodyBuilder2 is shown in Fig. 11, a database containing all antibody structures in the PDB45. The training data was extracted on the 31st of July 2021 resulting in a total of 7084 structures. Filters were used to ensure structures in the training data had both the VH and VL chains, were not missing residues other than at the start and end of the chain, and had a resolution of 3.5 Å or better. Structures with the same amino acid sequence were kept in the training data to expose the model to antibodies with multiple conformations. As a validation set, we used the set of 49 antibodies in the Rosetta Antibody Benchmark. Structures with the same sequence as antibodies in the validation set were removed from the training set.The data used to train, test, and validate ABodyBuilder2 was extracted from SAbDabhttps://github.com/oxpig/ImmuneBuilder.For the test data, we extracted all PDB files containing antibody Fv structures in SAbDab added between the 1st of August 2021 and the 1st of June 2022. Only crystal structures resolved by X-ray diffraction and with a resolution better than 2.3 Å were kept. A set of non-redundant Fvs were then selected from these and further filtered to remove antibodies with CDR-H3s longer than 22 amino acids and structures with missing residues. Finally, it was ensured that there were no structures with the same sequence in the test, training, and validation sets. This resulted in the set of 34 Fvs that was used to benchmark ABodyBuilder2 against other methods. A comparison of the maximum sequence identity to the training set against CDR-H3 RMSD for each Fv in the test set is shown in Supplementary Fig. The architecture of the deep learning model behind ABodyBuilder2 is an antibody-specific version of the structure module in AlphaFold-Multimer with several modifications. Residues are treated as rigid bodies, each one defined by a 3D point in space and a matrix representing its orientation. The input node features are a one-hot encoded representation of the sequence and the input edge features are relative positional encodings. At the start, all residues are set at the origin with the same orientation.21. Finally, torsion angles for each residue are predicted from node features and are then used to reconstruct an all-atom structure using hard-coded rules. Unlike AlphaFold-Multimer, all blocks have their own weights.The model is composed of eight update blocks that run sequentially. At every iteration, the node features are first updated in a structurally aware way using the Invariant Point Attention layer, and then residue coordinates and orientations are updated using the Backbone Update layer. For further details on how these layers work, see the original AlphaFold2 paper21. In AlphaFold2, the FAPE loss is clamped at 10 Å focusing on correctly placing residues relative to those closest to it. Similar to AlphaFold-Multimer, a modified version of FAPE loss is used for ABodyBuilder2 in which more focus is given to correctly placing CDR residues relative to the framework. This is achieved by clamping the FAPE loss at 30 Å when it is calculated between framework and CDR residues and at 10 Å otherwise. The final loss term is a sum of the average backbone FAPE loss after every backbone update and the full atom FAPE loss from the final structure.The main term in the loss function is the Frame Aligned Point Error (FAPE) loss, which quantifies how structurally similar the true and predicted structures are in the local reference frame of each residue. For details see ref. As is done in AlphaFold2, a structural violation term is added to the loss function. This penalises nonphysical conformations with a term for bond angles, bond distances, and clashing heavy atoms. In our models, this term was reduced by an order of magnitude with respect to AlphaFold2 as this was found to slightly improve prediction accuracy without significantly harming the physicality of the final prediction. Finally, the side-chain and backbone torsion angle loss from AlphaFold2 is also used.46 was used with a cosine annealing scheduler with warm restarts every 50 epochs, learning rates between 1e-3 and 1e-4, and a weight decay of 1e-3. For the second stage, the structural violations loss is added and dropout is set to zero. RAdam is also used for this stage with a fixed learning rate of 1e-4 and weight decay of 1e-3. To aid with stability, the norm of gradients is clipped to a value of 0.1 in the second stage of training. For both stages, a batch size of 64 is used and training is stopped if there was no improvement in the validation set after 100 epochs. On average, training took around four weeks for each model on a single GPU.Each model was trained in two stages. In the first stage, the structural violations term of the loss function was set to zero and a dropout of 10% was used. The RAdam optimiserABodyBuilder2 is composed of four deep-learning models trained independently to predict antibody structures. To select the best prediction, we align all predicted structures and choose as the final prediction the closest one to the average. This reduces the method’s sensitivity to small fluctuations in the training set. It also results in a small improvement in prediction accuracy.47 with an added harmonic force term to keep the heavy atoms of the backbone close to their original positions is used. In the rare case when two side chain atoms are predicted by the model to be within 0.2 Å of each other, the clashing side chains are deleted and remodelled using pdbfixer41. AMBER14 does not explicitly consider chirality, so when the predicted structure contains peptide bonds in the cis configuration, an additional force is added to flip their torsion angles into the trans configuration.Although the models are encouraged to predict physically plausible structures during training, they will occasionally produce structures with steric clashes, incorrect peptide bond lengths, or cis-peptide bonds. A restrained energy minimisation procedure with OpenMM is used to resolve these issues. The AMBER14 protein force field48 is used. First, the chirality at the carbon alpha centre of each D-stereoisomeric residue is fixed by flipping the hydrogen atom. The structure is then relaxed keeping the flipped hydrogen atoms in place before a final minimisation.By design, the ABodyBuilder2 deep learning model will always generate amino acids in their L-stereoisomeric form. However, it was found that during energy minimisation residues are occasionally flipped into their D-stereoisomer. To fix this, a method similar to that in ref. 22. It was run using the weights from version 2.2 and without the use of templates. The effect of templates on antibody structure prediction is shown in Supplementary Table 31 and IgFold25 models. As in their paper, Rosetta42 is used to minimise IgFold models. The original version of ABodyBuilder32 was run by using the SAbBox Singularity container (https://process.innovation.ox.ac.uk/software/p/20120-a/sabbox-singularity-platform—academic-use/1) from July 2022 excluding all templates with a sequence identity of 99% or higher. ABlooper33 (version 1.1.2) was run to remodel the CDR loops from the ABodyBuilder predictions. Structures generated by all methods were numbered using ANARCI49.We compared ABodyBuilder2 to five other methods: AlphaFold-Multimer, EquiFold, IgFold, the original version of ABodyBuilder and ABlooper. AlphaFold-Multimer was run using the freely available version of codeFurther information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


The gut microbiome is important for many host physiological processes and helminths and these interactions may lead to microbial changes. We carried out a longitudinal study of the impacts of S. haematobium infection on the gut microbiome of adolescents (11–15 years) in northern Nigeria pre and post praziquantel treatment. Using 16S sequencing a total of 267 DNA from faecal samples of infected versus uninfected adolescents were amplified and sequenced on an Illumina Miseq. We assessed the diversity of the taxa using alpha diversity metrices and observed that using Shannon index we obtained significant differences when we compared infected samples at 3, 9 and 12 months to baseline uninfected controls . Microbial community composition analysis revealed that there were only significant differences at 3, 9 and 12 months . We also demonstrated that the effects of the infection on the gut was more significant than praziquantel. Overall, our data suggests that S. haematobium, a non-gut resident parasite has indirect interactions with the gut. The bacterial taxa changes we have identified opens up the opportunity to investigate their role in human health, especially in urogenital schistosomiasis endemic communities. There are at least 240 million people infected annually worldwide, with 93% of those infected located in sub-Saharan Africa (SSA)3. Schistosomes have a complex life cycle involving humans, snails, and freshwater. There are three main species of Schistosoma that cause human schistosomiasis, S. mansoni, S. haematobium and S. japonicum4. In SSA, S. haematobium is the main causative agent of urogenital schistosomiasis6. Adult male and female schistosomes can be found in the veins of their human hosts, and schistosomula, a specific life cycle stage of the parasite can migrate through lymphatics and blood circulatory systems to the lungs and liver7. Ultimately, parasites reside within the perivascular or mesenteric venules. Fertilised eggs are shed in faeces or urine through the mating of adult male and female worms that arrive at the mesenteric veins of the lower plexus of the large intestine. Diagnosis of schistosomiasis is primarily through microscopic examinations of urine or faeces. Treatment is administered using praziquantel, and endemic countries engage in regular rounds of mass drug administration campaigns8.Schistosomiasis, also known as bilharzia, is prevalent in children due to lack of good hygiene practices, access to clean water, and poor health care facilities in affected countries such as Nigeria10. The microbiome of humans has been described to be associated with many communicable and non-communicable diseases. Helminths, have the ability to colonise the same environment as bacteria or have indirect systemic impacts on the gut and these interactions may lead to microbial changes in the gut12. Helminths have also been demonstrated to skew immune responses towards a T-helper 2 cell response and modulate the gut microbiome promoting the bloom in bacteria species such as Prevotella and Proteobacteia species15. Proteobacteria is seen as a marker of dysbiosis in the intestinal microbiome, as well as a critical determinant of host’s health, metabolism and inflammation16. Increases in Proteobacteria largely attributed to a bloom in Enterobacterales is associated with oxygen which contributes to microbiome and systemic inflammation which could affect infection prognosis21. In a recent study in Zimbabwe, Kay et al., reported differences in the microbiome structure of children infected with S. haematobium versus uninfected, with no reversal of the microbiome structure post praziquantel treatment. They also showed an abundance of Proteobacteria species in their infected cohorts14. Studies in Australia and Ecuador have also shown that helminth infections increased overall bacterial diversity in infected versus uninfected cohorts23.The gut microbiome is important for many host physiological processes, including the development of the immune system and other systemic changesS. haematobium versus uninfected controls. We demonstrated that the microbiomes of infected children differed dramatically from the uninfected group. Specifically, S. haematobium-positive subjects had more diverse microbiomes as measured by observed operational taxonomic units (OTUs). There was also a reduction in Firmicutes and an elevation in Proteobacteria which is a classic indication of microbiome dysbiosis. While this work is a strong indicator that S. haematobium is associated with microbiome differences it was lacking a longitudinal perspective or the impact of praziquantel therapy which may be required to establish the parasite as the cause of gut microbiome changes. There are limited studies from Africa that have examined the association between helminth infections and gut microbiome13. To understand the interaction of S. haematobium and the gut microbial community, we carried out a one-year longitudinal analysis of 267 stool samples of S. haematobium infection on the gut microbiome of adolescents (11–15 years) pre and post praziquantel treatment in Kebbi State, northwest Nigeria where urogenital schistosomiasis is endemic. We accounted for factors such as age, sex, diet, disease states, and other conditions that could influence the gut microbiome. Here, we describe the impacts of S. haematobium interactions with the gut microbiome on a longitudinal basis with samples collected at five time points.In our previous work, we profiled the faecal microbiome of adolescents (ages 11 to 15 years) positive for This study was approved by the Kebbi State Ministry of Health, Nigeria registration number KSHREC 106:47/2021. All research was undertaken in accordance with the relevant guidelines and regulations of the Kebbi State Ministry of Health. The study and its risks were explained to the participants, who then verbally assented to participation if they were interested. Parents and or legal guardians of adolescents were requested to provide informed consent for their child’s participation by signing the study consent form; in the case of illiteracy, thumbprinting was used, as approved by the State Ministry of Health Ethics Committee.To reduce the risk of co-infection with gastrointestinal helminths, any potential subjects who reported recent gastrointestinal distress were excluded. Prior to sample collection, the parents/guardians of adolescents who had consented to their children’s participation were informed of the study as well as any associated risks.S. haematobium eggs in the urine. A minimum of 10 mL of urine was collected per participant at baseline and every three months. Urine was spun down at 1,000 × g for 5 minutes, the supernatant was decanted, and the sediments were examined under the 40x objective of a brightfield microscope  to identify S. haematobium eggs13. Stool samples were also collected from each child who provided a urine sample and stored in sterile bottles. All samples were frozen at − 20 °C within 3 hours of collection until DNA extraction. The presence of eggs in the urine was used to identify cases of adolescents with urogenital schistosomiasis. After sample collection, all the children were treated with 40 mg/kg praziquantel by the State government nurses as part of the bi-annual national schistosomiasis control program.Stool and mid-stream urine samples were collected between 10 am and 2 pm in standard specimen collection bottles, labelled and transported to the laboratory for processing. The sedimentation technique was applied for examination of Microbial DNA was extracted from a total of 267 stool samples of adolescents: 46 urogenital schistosomiasis-infected adolescents supplied stool samples at baseline, they were treated with praziquantel and followed for one year and supplied stool samples every three months . Fifty u24 using a library of barcoded adaptor primers and a single reverse primer (806R) to amplify the V4 hypervariable region of the 16S rRNA gene with Phusion high-fidelity polymerase. The cycling conditions used for amplicon generation were as follows: (1) initial denaturation at 98°C for 30 s, (2) 34 cycles consisting of denaturation for 10 s at 98°C, annealing at 57°C for 30 s and extension at 72°C for 30 s, followed by a (3) final extension at 72°C for 5 min. Approximately 240 ng of each amplicon was pooled together for library generation.Extracted genomic DNA was quantified using Qubit2 (Invitrogen) to ensure sufficient quantity for amplification. Amplification was carried out in triplicates using the Earth Microbiome Project protocols16S amplicons were cleaned using the NucleoSpin PCR Cleanup Kit  before submission to the Rhode Island Genomics and Sequencing Center at the University of Rhode Island . Samples were pair-end sequenced (2 × 250 bp) on the Illumina MiSeq platform using the 600-cycle kit with standard protocols.25. Demultiplexed reads were imported into the software package Quantitative Insights Into Microbial Ecology 2 (QIIME2 version 2021.8)26. Within QIIME2, raw reads were quality-filtered, denoised and merged using the Divisive Amplicon Denoising Algorithm 2 (DADA2) pipeline25. Ribosomal sequence variants were aligned with mafft 27, and phylogenetic tree construction was done with fasttree2(via q2-phylogeny)28. Taxonomic assignment was conducted using the pre-trained Naive Bayes classifier and the q2-feature-classifier29 trained on the SILVA 132 99% database30. Taxonomy was assigned using the 99% identity SILVA (release 119) V4 classifier30. Amplicon sequence variants (ASVs) were generated through traditional clustering methods. The ASV table, rooted phylogenetic tree, representative sequences, and metadata from QIIME2 were then exported for further analysis in R (V3.3.1). Differential abundance testing was performed using the DESeq2 R package31. Microsoft Excel version 16.68 was used to rank the top 10 taxa from the ASV tables and graphs were plotted in Graphpad prism version 9.3.1.Raw 16S rRNA reads were subjected to quality filtering, trimming, de-noising with DADA2 (via q2-dada2)33 were calculated using the phyloseq package (version 1.30.0) in R (version 3.6.2)38. Two-tailed Mann Whitney U test (Prism v9) was used to determine the significance of differences in alpha diversity between infection groups. Beta diversity (Bray-Curtis dissimilarity) was analysed using the VEGAN . Principal coordinate analysis (PCoA) was then performed on the beta diversity distance matrices to visualize any relationships between microbiome composition and infection status.Alpha diversity  and beta diversity (Bray-Curtis dissimilarity)31. Significance was determined by the Benjamini, Krieger and Yekutieli test to correct for False discoveries with adjusted p-value < 0.0540.Differential abundance analysis between infection groups was performed using the DESeq2 (V1.14.1) package in R with agglomeration at various taxonomic levelsCohort characteristics: A total of 267 stool samples were collected over one year every three months at four-time points from 99 volunteers  aged between 11–15 years. There were 41 (89.1%) male and 5 female (10.9%) infected children analysed in this study . There w30 and combined the sequences with sequences we generated in the current study for analysis. We began by investigating changes in community composition at higher taxonomic levels at baseline. Comparison of sequences from samples that were analysed in 2022 and re-analysed sequences from samples collected in 2017 were combined for analysis. Using excel we ranked the top 10 ASVs and found that a baseline comparison of the three groups revealed a state of dysbiosis as a result of increased Proteobacteria, Euryarchaeota and decrease in Firmicutes and Cyanobacteria  and decreased   -C.Prevotellaceae, Ruminococcaceae, Streptococcaceae, Methanobacteriaceae and Enterobacterales at the family level. Family level changes at 3 months were similar to baseline, with the exception of marked increases in Prevotellaceae and Ruminococcaceae relative abundances. Family level changes at 6 months were similar to those of baseline, with the exception that Anaerovoracaceae was significantly reduced and Prevotellaceae was completely absent in both infected and uninfected subjects. At 9 and 12 months, the microbiomes were very similar, with Anaerovoracaceae being restored to uninfected levels at baseline  microbiomes remained similar between infected and uninfected groups, however these microbiomes were distinct from previous timepoints . There was an enrichment of Oscillospirales, Lachnospirales and reduced levels of Clostridiales. At the class taxonomic level, the main difference was observed at 3 months with a marked increase in Bacteroidia compared to all other time points. Additionally, Methanobacteria was completely absent at 3, 9 and 12 months from the microbiome of the kids  and 6 months  were present in addition to the changes observed at baseline. The differential abundances of the bacteria present at 9 and 12 months were similar . Firmicutes have been positively associated with soil-transmitted helminth infections46. At the genus level, we observed baseline differences in Clostridium_sensu_stricto_1, Romboutsia, Sarcina, Prevotella and other genera that have been demonstrated to be important members of the gut microbiome47. Similar to previous studies, we also demonstrate that in addition to the reduction in Firmicutes abundance, a major difference at the Phyla level between S. haematobium infected and uninfected kids was an increase in Proteobacteria in the infected cohorts. Some of the bacterial taxa changes we observed at baseline were similar to published reports from Zimbabwe where 16S and metagenomic analysis were done on stool samples S. haematobium infected kids15.Studies on the microbiome have gained significant advances in the last decade and improved our understanding of various biological processes in the human body, identified new drug targets and treatment strategies. Here, we used 16S sequencing to investigate the longitudinal impacts of S. haematobium infection over a period of one year at five-time points. Most studies on the impacts of helminths on the gut microbiome have been single time points or collected data pre and post-anthelminthic treatments. This presented a unique opportunity to investigate the temporal changes that may occur in the gut microbiome pre and post-praziquantel administration in the study population. In addition, it provided an opportunity to investigate the role of a non-gut resident parasite as the cause of the microbiome changes in these kids. The temporal analysis of the significant genera changes that increased across all the time points  identified genera such as Acinetobacter, Treponema, Bacteroides, Alloprevotella and Prevotella as some of the key taxa that changed. While the key genera that significantly decreased across all time points were Pediococcus, Chloroplast, Lactobacillus and Weissella. Pediococcus species e.g. P. acidilactici has been associated with improving recovery from gastrointestinal disorders and a decrease in clinical severity of atopic dermatitis49. Weissella species have been isolated from raw milk, fermented cereals and vegetables50. Some species, such as W. cibaria, have been identified to possess anticancer, anti-inflammatory, anti-fungal, anti-bacterial and immune-boosting properties52. W. cibaria is a lactic acid bacterium and has been associated with regulating the integrity of the intestinal epithelial barrier, through dampening inflammatory responses that reduce the synthesis of TNFa, IL-6 and Il-852. The significant reduction in the presence of these bacteria in the S. haematobium infected group might point to increased inflammation in the kids. Some studies have described the association of certain bacteria taxa, such as Dialister, Clostridium XIVa, Weissella and Bacteroides with incomplete parasite clearance53.We also describe the longitudinal impacts of Prevotella and Candida are associated with carbohydrate rich diets54. The kids in this study feed mostly on carbohydrate-rich foods such as fura da nono, rice and kunun zaki typical of northern Nigeria residents55. Indeed, we observed high Prevotella levels at baseline which increased significantly at later time points 9 and 12 months. The age group of the participants in this study was restricted to 11–15 years, this is important to note considering the variability associated with the microbiome structure of kids as they grow57. Our selection of this age group is based on our previous work, where we have demonstrated that there were no age-specific effects on the gut microbiome13. Owing to the peculiarity of our study population as previously reported, we could not control for possible sex-related bias, hence we had more males than females enrolled in this study. This is not unusual for a conservative Islamic population, where girls tend to be less willing to participate in studies of this nature. Previous studies have, however, reported that there were no sex-related bias on the impacts on helminth infections on the gut microbiome53. Within urogenital schistosomiasis endemic communities, several studies have reported that differences in infection patterns seen in gender is linked to increased water contact practices amongst males as opposed to females59. One of the limitations of this study was a loss to follow-up of some of the study subjects and the inability to collect samples from all uninfected kids at the time points that were analysed. This would have allowed more precise comparisons of the microbiomes as they change pre and post-praziquantel treatment over time. Another challenge with collecting uninfected samples from schistosome endemic communities is the likelihood of the kids having been infected in the past and being cured of the infection. However, we anticipated that the likelihood of the uninfected groups having obtained an infection within two years prior to the study was low. We make this assumption because there had been no mass drug administration campaign in the community for at least 2 years before our study, and all participants reported not to have taken any anthelminthic treatment within the same period.The African diet is carbohydrate-rich, and some studies have described that S. haematobium worms mostly live in the pelvis venous plexus60, yet they exert indirect effects on the gut microbiome just like other intestinal helminths66. The changes in the microbial composition of the gut through the activity of helminths have led to the hypothesis that there is a potential link between these microbial community changes and the modulation of inflammatory responses in autoimmune diseases.One of the challenging aspects of microbiome research is inferring causation, especially in a community that is schistosome endemic, which we could not establish in this study. We observed significant differences in the microbial communities at 3, 9 and 12 months compared to baseline and 6 months. Further analysis revealed that infection seemed to have greater effect on the changes on the gut than praziquantel. The changes in the microbiome of adolescents could also due to declining efficacy of the drug or normal changes associated with adolescents as they grow. It is interesting to observe that S. haematobium on the gut microbiome, these findings open up opportunities for future studies to examine the impacts of the bacteria taxa changes we have described in this study and its implication for human health.In conclusion, the strength of our study is in its design and the opportunity provided to examine the dynamic relationship of the parasite with the host pre and post-anthelminthic treatment over a period of one year. It also allowed us to study the microbial community differences and any dietary or environmentally related changes that occurred in the subjects over time, specifically at later time points. To the best of our knowledge, this is the first study to investigate the longitudinal impacts of Supplement 1