Goal-directed navigation requires learning to accurately estimate location and select optimal actions in each location. Midbrain dopamine neurons are involved in reward value learning and have been linked to reward location learning. They are therefore ideally placed to provide teaching signals for goal-directed navigation. By imaging dopamine neural activity as mice learned to actively navigate a closed-loop virtual reality corridor to obtain reward, we observe phasic and pre-reward ramping dopamine activity, which are modulated by learning stage and task engagement. A Q-learning model incorporating position inference recapitulates our results, displaying prediction errors resembling phasic and ramping dopamine neural activity. The model predicts that ramping is followed by improved task performance, which we confirm in our experimental data, indicating that the dopamine ramp may have a teaching effect. Our results suggest that midbrain dopamine neurons encode phasic and ramping reward prediction error signals to improve goal-directed navigation.  •VTA dopamine neurons have phasic and ramping activity during goal-directed navigation•Pre-reward ramping is modulated by learning and task engagement•A Q-learning model emulates phasic and ramping dopamine neuron activity in TD error•Pre-reward ramps in dopamine neuron activity and TD error improve task performance Dopamine neurons signal reward prediction error in their phasic activity, but reports of ramping activity and its potential function remain controversial. Using experimental and computational methods, Farrell et al. show that dopamine neurons display phasic and ramping activity and both act as reward prediction error signals for improving goal-directed navigation. In naturalistic environments, animals navigate in order to harvest rewards. Successful goal-directed navigation requires learning to accurately estimate location and select optimal actions in each location. Midbrain dopamine neurons are known to play a key role in reward value learning by encoding temporal difference (TD) error in their phasic activity . While tRecently, studies of freely moving animals have observed dopamine ramps as the animal progresses toward the reward location . In thesA similar VR experiment requiring animal locomotion toward reward showed that a subset of dopamine neurons displayed pre-reward ramping , althougTo address this, we imaged dopamine neural activity longitudinally as head-fixed mice learned to perform a goal-directed navigation task in closed-loop VR. This required animals to locomote to proceed through the corridor and estimate the location of a hidden reward zone. The task also differentiated between when the animal was actively engaged in finding the reward location or not, allowing a comparison between active navigation and passive viewing of VR.Across learning, phasic dopamine responses developed that resembled reward prediction errors and indicated the animal’s estimate of the reward location. We also observed the development of pre-reward ramping activity, the slope of which was modulated by both learning stage and task engagement. The slope of the ramp was correlated with the accuracy of reward estimation in the next trial, suggesting that the ramp played a teaching role in the selection of accurate location-specific action during navigation. We further devised a Q-learning model incorporating belief state inference, which could simultaneously produce phasic and ramping TD error, matching the dopamine neural activity recorded in the task. Our results indicate that midbrain dopamine neurons, through both their phasic and ramping activity, encode reward prediction error, which may provide teaching signals for goal-directed navigation.To examine the activity of midbrain dopamine neurons during goal-directed navigation, we designed a task in a VR corridor. Head-restrained mice of both sexes were free to self-pace their locomotion on a treadmill, which accordingly updated visual scenes in a closed-loop system  Figure . A speciVideo showing experimental setup and the behaviour of a mouse over two trials of the goal-directed navigation task in virtual reality. The mouse was head-fixed over a cylindrical treadmill with a Miniscope attached to its head. The mouse ran at a self-defined pace through a virtual corridor displayed on three screens, and licks in the lick port to receive reward. Reward delivery is heard as a click as the reward valve opens. The video is annotated to visually show when the reward is delivered (blue dot). After the corridor has been traversed, the screens display iso-luminant grey for an intertrial interval (4-6s), after which the animal is transported back to the start of the corridor to initiate the next trial.As mice learned to perform the task, we measured the global activity of midbrain dopamine neurons. We expressed a genetically encoded calcium indicator GCaMP6m;  using viDopamine activity showed sharp, transient increases and decreases following rewarded and unrewarded licks, respectively D. Average trials C but difWe also examined how these phasic dopamine responses changed over learning D–2I. In In summary, the learning-related changes in peri-lick phasic neural activity, particularly when examined in the spatial dimension, are broadly consistent with the reward prediction error term of TD reinforcement learning (RL) models . The actPrior to reward delivery, we observed the development of phasic dopamine activity across training in response to reward-predictive cues, as well as a slow ramp in the pre-reward activity leading up to the reward zone location . Phasic location .Figure 3As it has been suggested that increased dopamine activity could reflect increased motor vigor , we alsoTo examine the effect of trial type, we compared cue responses and ramping activity between active and passive trials across the different training stages D. We fouTo confirm that both learning and task engagement impacted ramp gradient, we implemented LMM see  that eitWhile ramping dopamine signals have been observed under certain conditions , their fAs phasic cue and reward responses readily fit into a standard TD learning framework but ramping does not, we devised a model to investigate whether ramping could be explained as RPE. As Q-learning is a model-free algorithm that learns action values per state rather than state values , we consr) is received. The outcome is then used to calculate the prediction error (δ), which updates the value of the chosen action. The algorithm then iterates to the next state and repeats the process. For simplicity, we designed the algorithm such that if the agent chooses to lick in the reward state, it receives a reward value r of 1 but no reward if it does not lick. Similar to the behavioral task, we imposed a threshold of 2 licks prior to the reward state to prevent continuous licking, such that if the agent exceeded this threshold, it received a reward value of −0.1, and the trial terminated.We devised a Q-learning algorithm that incorporated a position inference and an eligibility trace to simulate the animals’ learning in the task  and the activity of their dopamine neurons . In desi sB; see , which iWe ran 100 agents for 4,000 trials each and found that the agents learned to perform the task in a similar manner to our mice, slowly improving the ratio of rewarded to unrewarded trials over learning F and 4G.h others A. Both trd state D and S5Erd state B and S6Brd state E and S6ATD error allows learning prior to reward delivery because it is calculated by comparing the values of consecutive states due to its method of bootstrapping from predictions of value of the current and subsequent states. In TD models, this TD error signal moves backwards to assign credit to reward-predictive stimuli, producing cue responses . Why, thd states . To explilitated B).The phasic cue responses in TD error can then be explained as a consequence of the reduction in uncertainty in position inference when a cue is passed. In the model, there is uncertainty of which state is being occupied, but this uncertainty is reduced when in the presence of a reward-predictive cue: these locations provide more certainty that this is not the state to lick in. Therefore, the estimated current value of not licking , we found a distribution of slopes A, left. The model predicted that a positive slope in pre-reward TD error  should be followed by increased licking in the reward state on the subsequent trial  compared with the negative slope trials , spatial encoding could not be differentiated from an equivalent passive approach temporal task . In contWe observed two patterns of activity: phasic and ramping. The observed patterns of phasic responses to reward-predictive cues and reward delivery were to be expected, as shown in many previous experiments  and predPre-reward ramping dopamine has been observed in many studies  but withThe finding that the ramp slope increases across learning has been observed previously in a fixed-distance locomotor task , and thiOur model can further explain why ramping occurs in different tasks with different time courses and gradients: goal-directed navigation necessitates state estimation under conditions of uncertainty and therefore produces ramping TD error. In some tasks, this uncertainty may be reduced so that ramps decay with extended learning  FiguresD and S7EFinally, while dopamine has been suggested to play a role in vigor and motivation , we findGiven the similarities in the characterization of phasic and ramping signals and that the ramp did not reflect increased locomotor vigor, we asked whether the ramp could represent RPE using a Q-learning model. The model was given the basic information required to navigate to reward: a representation of self-location and a representation of visual cues observed. While simplistic, our model nevertheless recapitulated our experimental data, capturing how mice learned to lick in the reward location but not lick prior to that. The model reproduced both pre-reward ramping and also phasic responses to reward-predictive cues and reward delivery, as we saw in our calcium imaging data. Moreover, the model predicted that a positive pre-reward slope would be followed by increased reward location licking on the subsequent trial, which we also found in our experimental data. Together, this supports the idea that pre-reward ramping is a form of RPE that may provide a teaching effect to improve goal-directed navigation. To caveat this, the model was not designed to explain the difference between active and passive trials but rather to explain how a pre-reward ramp in TD error could arise.Firstly, our neurophysiological results are based on observation of global dopamine neuron activity, averaged across the entire field of view of neurons. Single-cell resolution could provide further information about whether ramping activity is a feature of all or subsets of neurons, as suggested by previous studies .Secondly, the Q-learning model presented is optimized for the behavioral task that our animals performed and would likely have to be altered to capture particular features of different tasks. Specifically, we used selective weighting of the two Q-values by the belief of being in the reward state and the eligibility trace, respectively. A more generalizable model would allow the weighting to come about over training rather than being hard coded. Another aspect is that we impose a negative reward value for the model if the pre-reward licking threshold is exceeded, whereas the mice just have the reward opportunity loss when the trial terminates. The inclusion of a small negative reward value was simplistic and allowed the model to learn that trial termination was a negative outcome to speed learning , althougFinally, while the model mostly recapitulates our experimental data, there is a difference in VTA dopamine neuron activity and TD error in the early learning stage, where the model predicts ramping TD error, but our data instead show cue responses with minimal or no pre-reward ramping. This may be due to dopamine neurons responding to salient visual features, a feature that is reported in animals  but thatFrom our results, we find substantial evidence that ramping dopamine encodes RPE. As many studies that have observed dopamine ramping have done so in tasks that require animals to navigate to goals, we posit that ramping might result from a need to integrate spatial and temporal information to perform the correct actions in the correct locations to obtain reward. This ramping further matches the characterizations and functions of RPE given the development over learning, the dependence on task engagement, and improved performance on the subsequent trial.Overall, our data show a ramping of VTA dopamine neuron activity that is most consistent with encoding of RPE. The model unifies both phasic and ramping activity as a single RPE signal, and our analysis indicates the potential teaching effect of dopamine ramping signals in improving reward localization. Together, we conclude that VTA dopamine neurons, through both their phasic and slower ramping activity, signal RPE during the learning of goal-directed navigation.karolinajhfarrell@gmail.com).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Dr Karolina Farrell (This study did not generate new unique reagents.The Jackson Laboratory) with a female C57BL6 mouse . Following genotypic identification (Transnetyx) of DAT-cre offspring, heterozygous DAT-cre breeders were selected and subsequently paired with C57BL6 breeders in order to maintain the colony. Following pregnancy confirmation, males were separated out. Pups were weaned three weeks after birth, earmarked for genotyping, and group-housed in single-sex cages. Mice were 8–16 weeks old at the start of behavioural training.The DAT-cre transgenic mouse line was started by breeding one male DAT-IRES-cre  in their home cage following behavioural training to ensure sufficient hydration  diluted 1:3 in aCSF was injected at a rate of 50 nL/min into the VTA  and the pipette was left in place for 10 minutes. Following this, for six of the mice, a blunt needle was inserted and lowered between 1.5 and 2 mm from dura before being removed. The GRIN lens (Inscopix 1050-002179) was then inserted, at an approximate rate of 400–500 μm/min to a depth around −4.3 mm and secured in place using dental cement . A custom metal headplate was cemented behind the lens, and a plastic cap (cut-off end of Eppendorf® tube) was cemented over the lens for protection. 0.2 mL of warmed saline was administered subcutaneously per hour of surgery to maintain hydration. Following recovery, mice were closely monitored and given 20 μL meloxicam in condensed milk and high-protein wet food for 3 days post-surgery.Experimental mice underwent two surgeries. In the first surgery, mice were induced with 3S. Stiteler, miniscope.org) was secured to the baseplate using a set screw.The second surgery was performed 2–3 weeks after the first, to allow for viral expression and inflammation reduction. The mouse was similarly induced, maintained and monitored. Following head-fixation, the protective cap was drilled out and the lens was cleaned. A modified UCLA Miniscope  with an Kraft Foods), pseudo-randomly delivered, through a lick spout to encourage running and identify putative dopamine reward responses, while monitoring licks using a custom infrared sensor. Mice were free to run in the task as much as they desired for about 30 minutes during the dark cycle each day  interfaced with the VR code through an Arduino Leonardo board. The task used a 150-cm long corridor, with a low-contrast white noise pattern along the ceiling, walls and floor (8-cm width and height). The visibility of the corridor was limited to 70 cm ahead. A full traversal through the corridor is considered a completed trial. Reaching the end (or timing out) initiated an ITI where the corridor was replaced with isoluminant grey. The ITI was chosen randomly between 4 and 6s, to ensure that timing between spatial features could not carry past each trial.All behavioural training was performed during the dark cycle, and in a dark room. Mice were handled, water-restricted, and acclimatised to head-fixation on a custom Styrofoam wheel  and Mini screens . The thr screens , 2018  on the walls, centred at 20 cm and 45 cm along the corridor respectively. An unmarked reward zone spanned 60.5 cm to 67 cm in the corridor. On each trial, a reward was delivered to a spout in front of them. The spout incorporated an infrared sensor to detect licking. The exact location of the reward zone was not indicated by any cue and instead had to be estimated by the mouse based on prior cues and actions. If the mouse did not lick in the reward zone, then it would passively receive the reward at the end of the zone . However, if it licked within the zone, then reward delivery was actively triggered , and therefore delivered earlier than in the passive trials D. The deFor comparison of data across training, training sessions were split into three stages: early, mid and late training. Early and late stages were defined as the first and last quartile of sessions respectively for each animal, with the rest being classified as mid-training.Calcium fluorescence was detected using a Miniscope through an implanted GRIN lens A which aMathWorks, Natick, MA, USA) for pre-processing. Rarely, unstable signal was produced by Miniscope movement or power surges or lapses. Two types of signals were considered unstable. The first type was fluorescence that exceeded or fell below a threshold of 1.5 standard deviations away from the mean fluorescence across the whole training session. The second type was when fluorescence surrounding the first type (±100 ms) exceeded half of the difference between the maximal fluorescence and mean fluorescence, therefore constituting an ‘upswing’ or ‘downswing’ of a large transient. Following removal of these two types of unstable signal, a photobleaching curve was fitted across the entire session using Matlab polyfit (2nd order) and subtracted from the fluorescence trace. The trace was then corrected for baseline variation by subtracting the lower 10Imaging data collected using Bonsai was imported into Matlab R2018a , and trials with unstable signal were excluded from analysis. Sessions were included if they had st order) to the fluorescence in the window 0–60 cm.Behaviour during training was assessed through binned mean licks/cm across the virtual corridor, as well as through the speed of the wheel rotation. Phasic responses to the cues were calculated as the maximum minus the minimum values within each 12-cm cue window . Phasic responses to the reward were calculated as the maximum value in the reward window (60–90 cm) minus the mean of the activity within the pre-reward window (50–60 cm). Ramp gradient was calculated as the gradient of a fitted linear line  between the fluorescence at zero and the minimum fluorescence in the second half of the window of fluorescence being examined  for each trial. Paired data was tested for differences using the Mann-Whitney U test (Matlab ranksum), as were tests of difference from zero, while the Wilcoxon signed rank test (Matlab signrank) was used to test for differences between different training stages. These results were then confirmed using linear mixed modelling (Matlab fitlme), which accounts for repeated measures in our longitudinal experimental dataset.Rewarded lick traces  includedn on lick distribution on trial Ramp slopes were classified as positive or negative by considering all ramp slopes across all trials, sessions and animals and taking the most positive third as ‘positive’ and the most negative third (below zero) as ‘negative’. For analysis of the effect of ramp slope on trial Boehringer Ingelheim) intraperitoneally, and transcardially perfused with 1X PBS followed by 10Sakura FineTek) and 40 μm slices were made using a cryostat (Leica CM1850 UV). Slices were washed five times in 1X PBS before overnight incubation on a rotating platform at room temperature in primary solution: 1:5000 mouse anti-TH  in PBS-T  in PBS-T. Slices were then washed five times in 1X PBS before being mounted and allowed to dry. Mounting medium with DAPI  was added to stain cell bodies, before adding the coverslip and sealing with nail polish. Slices were then imaged at 10x magnification using a confocal microscope (Leica DMi8) and LAS X software (Leica).Mice were deeply anaesthetised using 3.5Mathworks). The model considered a corridor of 30 discrete states where an agent would move sequentially through the states in each trial (t), and could choose to ‘lick’ or ‘not lick’ in each state (s). Cue states were defined as r) of −0.1 and the trial was terminated such that it progressed to the first state of the next trial.Our Q-learning model was implemented using Matlab 2018a  selection was performed in an ε-greedy manner where ε = 0.1, such that there was a Q-values of licking and not licking and selecting whichever had a higher value: either Q-value for the chosen action Action . All statistical analyses are detailed in Mean percentage trial type E is an aData shown in Model performance as shown by percentages of rewarded and unrewarded trials F is averIn Data in Comparisons of lick distributions for late-stage trials following large positive, small positive, or negative reward response trials   were pery is the variable of interest:Linear mixed modelling (LMM) with random intercepts and slopes was additionally implemented in Matlab (fitlme) to examine the dependence of the data on task variables such as trial type and session and account for repeated measures such that there were independent random effects terms for intercept and slope with animal identity as the grouping. Models were compared using a likelihood ratio test (Matlab compare) to ascertain which model best described the data.


In recent decades, tremendous progress has been made in the medicinal field in understanding the molecular mechanisms underlying human pathologies, due to the significant development of advanced laboratory techniques and technologies. As a result, better knowledge of pathologies has also led to greater diagnostic, prognostic, and therapeutic opportunities. There have also been some important innovations in the field of gastrointestinal diseases, which are the focus of this Special Issue. In neoplastic diseases, for example, the development of immunotherapy and the genetic and molecular classification of various malignancies have led to significant advancements. Some are still being explored: the use of chimeric antigen receptor T-cell (CAR-T) and natural killers (CAR-NK), which have been very innovative in oncohematology, with six drugs being approved for the treatment of leukemia and myeloma , is stilTranslational medicine, personalized medicine, and precision medicine are the future. We are taking the first steps in this direction by reaping the rewards of basic molecular research that will eventually lead to new therapies. However, the optimism of recent years should not obscure the fact that we are only at the beginning of this journey and that there is still much to be done and understood to best diagnose and treat gastrointestinal diseases. The future is now ours.


Social media has become an important resource for discussing, sharing, and seeking information pertinent to rare diseases by patients and their families, given the low prevalence in the extraordinarily sparse populations. In our previous study, we identified prevalent topics from Reddit via topic modeling for cystic fibrosis (CF). While we were able to derive/access concerns/needs/questions of patients with CF, we observed challenges and issues with the traditional techniques of topic modeling, e.g., Latent Dirichlet Allocation (LDA), for fulfilling the task of topic extraction. Thus, here we present our experiments to extend the previous study with an aim of improving the performance of topic modeling, by experimenting with LDA model optimization and examination of the Top2Vec model with different embedding models. With the demonstrated results with higher coherence and qualitatively higher human readability of derived topics, we implemented the Top2Vec model with doc2vec as the embedding model as our final model to extract topics from a subreddit of CF (“r/CysticFibrosis”) and proposed to expand its use with other types of social media data for other rare diseases for better assessing patients' needs with social media data. The call for “recognizing the need to promote and protect the human rights of all persons that included the estimated 300 million persons living with a rare disease worldwide” was made with UN Resolution in 2021Social media has been increasingly applied in biomedical research, as Lim et al. outlined and discussed the opportunities of using social media in medical and health care Lim, . Many stLatent Dirichlet Allocation is an unsupervised, probabilistic modeling method, which extracts topics from a collection of documents. Each topic is made up of the probabilistic distribution of words contained in that topic. The words that have the highest probability for that topic are used to describe the contents of that topic. These probability distributions of words are used to assign probabilities of topics to each document. Limitation of LDA on topic modeling has been reported that included the need for data cleaning and pre-processing, selection of model parameters, such as the number of topics, and interpretability and validation of the generated topics.  is a genetic (inherited) disease that affects multiple organs in the body We removed web links, email addresses, and text within brackets from the original text of each document. We also replaced contractions with their expanded versions, e.g., “can't” to “cannot,” “haven't,” to “have not.” An example of the original text and the cleaned version from this step for a single document can be seen in We tokenized each cleaned document into tokens using the NLTK word tokenizer and converted the tokens to all lower case creating a list of tokens as shown in The list of tokens for each document was tagged with a part of speech (POS) using the POS-tagger module from NLTK. An example output of the tagging can be seen in We only included tokens tagged as adjective, adverb, noun, or adverb. The parts of speech were converted to wordnet parts of speech, e.g., all tags that start with N were converted to n for nouns. The tokens that were not tagged with the acceptable POS tag were removed as can be seen in Stop words were removed from the remaining list of tokens if they were found in the stop words in Gensim. Tokens were then lemmatized using the WordNetLemmatizer from NLTK. A lemma is the least inflected version of a word based on its meaning, e.g., as shown in Then, we created bigrams and trigrams from the tokens. A look-up table of integer ids and tokens was then created from the set of tokens such that each unique token is given an integer id. For example, from Finally, we generated corpora composed of pairs of token ids and token frequencies for each document. As an example, the list of tokens from the document in We created topic models using LDA and implemented through Gensim. Here, we employed hyperparameter tuning and LDA model optimization, with an aim of finding the most representative number of topics with the highest coherence automatically without further manual interpretation.a priori belief on the document to topic distribution)     Bouma, , shows tWe evaluated the performance of the Top2Vec model with six different embedding methods that included doc2vec and five transformer-based pre-trained models. Doc2vec jointly learned embedded document and word vectors in the same semantic space using a distributed bag of words (DBOW) model. The DBOW model used document vectors to predict surrounding words in a context window in the document, which is similar to the word2vec skip-gram model. . Each trial generates one model with different hyperparameters. The LDA model with the highest coherence, the trial of #134 via ATPE, was then evaluated qualitatively based on the generated topics. Word clouds were created where the font size of each word corresponded to the relative probability of that word belonged to that topic as generated by the LDA model. We then grouped the topics into six topic categories, based on our manual interpretation, as “Daily Life,” “COVID,” “Testing,” “Support,” “Medication and Medical Equipment,” and “Symptom and Side Effect,” shown in We examined multiple embedded models within Top2Vec, as shown in Top2vec with the doc2vec embedding model automatically generated a total of 68 topics. We grouped these 68 topics into eight categories as listed in Word clouds were generated with the font size of each word corresponding to the cosine similarity score, which measures how close the word vector was to the topic vector in semantic space. Thus, words with a high score were located at a small distance away from the topic vector in semantic space and were semantically closely related. Word clouds for the topics related to the topic categories of “Medication and Medical Equipment” are shown in In this study, we explored LDA and Top2Vec, two widely applied topic modeling algorithms on Reddit with an overarching aim of uncovering the needs/concerns patients with CF might have. From this study, we concluded that Top2Vec with the embedded algorithm of doc2vec outperforms LDA, not only because Top2Vec automates the process of topic modeling without pre-determining several hyperparameters, e.g., the number of topics, but also because it generates more concrete topics, as shown in the Results section.a priori knowledge of the number of topics is not required. This is a major advantage over the LDA. Additionally, since it uses semantic space to represent words, there is no need to do pre-processing to remove stop words or perform lemmatization or stemming. Because only semantically similar words are found near the documents, the uninformative stop words are not found near the document vectors. As a preliminary study, we found that Top2Vec discovered topics that were more informative and representative of the subreddit.As outlined in Angelov , Top2VecDoc2vec embedding algorithm implements the DBOW model to predict surrounding words in a context window in the document, but with the limitation of using the same vector for words appearing in different contexts. (Le and Mikolov, In this study, Top2Vec programmatically generated 68 topics corresponding to eight different categories, which overlap with the main topic categories from LDA. However, by looking at those topic categories in the word cloud, which are shown in As a proof-of-concept, we will further extend this work by analyzing more rare disease-related subreddits with an aim of accessing urgent needs for rare disease patients from social media and consequently identifying research gaps and initiating new research activities.The original contributions presented in the study are included in the article/BK conducted data analysis and drafted the manuscript. SQ managed the project, participated in the discussion, and helped on the manuscript. YX participated in the discussion. QZ conceived and supervised the project and drafted the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


MCM1-AGAMOUS-DEFICIENS-SERUM RESPONSE FACTOR). In plants, this family has expanded greatly, with more than one-hundred members generally found in dicots, and the proteins encoded by these genes have often been associated with developmental identity. AGL15 transcript and protein accumulate primarily in embryos and has been found to promote an important process called plant regeneration via somatic embryogenesis (SE). To understand how this TF performs this function, we have previously used microarray technologies to assess direct and indirect responsive targets of this TF. We have now revisited this question using next generation sequencing (NGS) to both characterize in vivo binding sites for AGL15 as well as response to the accumulation of AGL15. We compared these data to the prior microarray results to evaluate the different platforms. The new NGS data brought to light an interaction with brassinosteroid (BR) hormone signaling that was “missed” in prior Gene Ontology analysis from the microarray studies.AGAMOUS-like 15 (AGL15) is a member of the MADS-domain transcription factor (TF) family. MADS proteins are named for a conserved domain that was originally from an acronym derived from genes expressed in a variety of eukaryotes ( Somatic embryogenesis (SE) is a biotechnological tool that is utilized for basic research as a means to regenerate transgenic plants to test gene function with the aim to understand plant development and responses to the environment. SE also provides a valuable resource for plant improvement and the propagation of commercial crops . SE is aAGAMOUS-LIKE15 (AGL15) encodes a member of the MIKC  subfamily of MADS domain transcription factors (TFs) that promotes somatic embryo development when constitutively expressed in Arabidopsis , soybean (Glycine max), and cotton (Gossypium hirsuitum), while mutants (loss-of-function) of both AGL15 and AGL18 (AGL18 encodes a protein closely related to AGL15) significantly reduces somatic embryo development in Arabidopsis [AGL15 expression [35S:AGL15 transgene also promotes somatic embryo development from the shoot apical meristem (SAM), in a culture system called SAM somatic embryogenesis (SAM SE) from mature seeds that complete germination in liquid media containing 2,4-dichlorophenoxyacetic acid  [bidopsis ,3,4. Onepression  (currentPrior work in the lab performed chromatin immunoprecipitation (ChIP) followed by genomic tiling microarray hybridization (ChIP-Chip) for AGL15 embryo culture tissue (ECT) to identify potential binding sites genome-wide. In addition, microarrays to assess transcript accumulation were used to assess changes in response to AGL15 accumulation [In this study, we employed the next-generation sequencing (NGS) to obtain a better resolution of genome-wide binding by AGL15 using ChIP followed by sequencing (ChIP-Seq), and transcript accumulation in response to AGL15 using RNA-sequencing (RNA-Seq) techniques. High throughput sequencing is becoming more ubiquitous as the cost of high-throughput sequencing technology continues to drop. NGS also does not have the same limitations as the arrays. In fact, the arrays used in Zheng et al. (2009) [AGL15 transgene supports the stable establishment of ECT [Embryo culture tissue (ECT) was used to map in the vivo AGL15 binding sites using the ChIP-Chip approach [t of ECT , and thet of ECT , ABSCISIt of ECT . Here, wt of ECT , which wt of ECT , and comt of ECT . In CisG−3. Other selected categories within “biological processes” are shown in AGL15 binds to thousands of regions in the Arabidopsis genome. In our study, we found 6596 potential peaks that were common among the three replications with a false discovery rate (FDR) <0.01. A comprehensive list is provided in −2; and “response to fatty acid” (GO:0070542), FE 3.73, FDR—1.27 × 10−9. Other overrepresented categories within “biological responses” are shown in CisGenome was previously used with ChIP-Chip to identify 3708 peaks (FDR < 0.01) assigned to genes associated with AGL15 binding sites that were assigned to 3360 genes [, cis motif enrichment for common binding regions from ChIP-Chip and ChIP-Seq show an overrepresentation of CArG motifs   demonstrFigure 2AGL15 (35S:AGL15) results in a significant increase in somatic embryogenesis in both the ECT and SAM SE systems, while loss-of-function as a double mutant with the closely related AGL18 gene (agl15/agl18) results in a significant decrease in SE in both systems [35S:AGL15 transgene [35S:AGL15 line, and the agl15/agl18 double mutant. The double mutant agl15/agl18 SAM SE expression data have been described in Paul et al. (2021) [35S:AGL15 line compared to Col (wt). Two biological replicates were performed for 10-day old SAM SE tissues for each genotype and the percentage of total mapped with genes for each replicate was determined. The results showed that 1294 genes showed increased transcript levels, and 1664 genes showed decreased transcript levels in response to the transgene . The data are presented in Only knowing the binding regions of AGL15 does not demonstrate a regulatory consequence of AGL15 association, so it is important to determine the genes that respond to changes in AGL15 accumulation in some tissues . Overexpression of AGL15 5S:AGL15 ransgene . In addiransgene ,3. As su. (2021) . Here, w35S:AGL15 or agl15/agl18) from the microarray [Furthermore, we compared the RNA-Seq and microarray gene expression studies , which u35S:AGL15 compared to Col, wt) data to obtain better insights on the direct induced/repressed targets as well as indirect targets that are responsive but not bound. As shown in Prior studies combined ChIP-Chip and microarray data to provide the genes that are bound as well as responsive (either induced or repressed in response to AGL15 accumulation). Here, we combined the ChIP-Seq and RNA-Seq (AGL15 expression [BRI1-EMS-SUPPRESSOR 1 (BES1), and BRASSINAZOLE-RESISTANT 1 (BZR1) were found to bind directly to AGL15′s promoter region, and the regulation of AGL15 by these BR TFs in controlling SE is implicated by the fact that loss-of-function of agl15 restores normal seedling development to mutants in the BR pathway as opposed to SE development in the AGL15 background.GO analysis of the directly repressed targets found by NGS highlighted genes involved in response to brassinosteroid (BR) signaling was overrepresented B. This wpression . Genes eBZR1 (At1g75080) and BES1 (At1g19350) had 5′ upstream regions directly associated with AGL15, both in the ChIP-Chip and ChIP-Seq studies . Because of the impact of these genes on SE, we followed up using qRT-PCR on the 10 d SAM SE tissue. As shown in BZR1 transcript accumulation did not respond to the 35S:AGL15 transgene, BES1 transcript was significantly reduced in this background, indicating that AGL15 can repress BES1 expression. It should be noted that these two genes are highly similar (84% identity) and required use of the 3′ UTR to design specific primers.While the RNA-Seq data did not show a significant response of these genes to AGL15 accumulation, the microarray data [We determined and compared the genome-wide binding sites of the MADS-domain transcription factor AGL15 using ChIP-Seq and ChIP-Chip. Although the sequencing technology is a different approach and new biological replicates than those used in the ChIP-Chip study were generated, we found a high correlation between the datasets. About 75% of the ChIP-Chip genes associated with the AGL15 binding sites were also present in the ChIP-Seq data using CisGenome to analyze the data and considering all three replicates together . The ChISimilar studies comparing ChIP-Chip and ChIP-Seq were performed for another MADS-domain transcription factor SEP3 ,15. KaufWhile the shared binding sites between ChIP-Seq and ChIP-Chip and those unique in ChIP-Seq showed overrepresented motifs that corresponded to potential cis-regulatory elements recognized by AGL15, the ChIP-Chip unique binding sites did not identify this element . This coAs described above, the different approaches to characterize genome-wide binding sites have different limitations. Both the unique ChIP-Seq targets and the ChIP-Seq/ChIP-Chip shared targets found an enrichment of genes involved in stress response, in particular, the response to osmotic stress (GO:0047484) and the regulation of response to salt stress (GO:1901000) (ChIP-Seq) or just response to salt stress (GO:0009651) (ChIP-Chip). The ChIP-Chip unique sites do not show enrichment in these categories, but do highlight involvement in response to other stresses . ChIP-SeWe then determined the targets that respond to AGL15 accumulation using NGS compared to the microarray to assess transcriptomes. RNA-Seq has advantages over expression arrays because expression arrays are limited by the genes represented and do not (often) include genes such as small RNA encoding genes. In addition, only identified genes are represented. By combining ChIP-Seq and RNA-Seq, we determined direct responsive targets. We found a minority of bound sites responded to AGL15 as direct induced (4.4%) and repressed (7.1%) genes, however, a larger number of responsive genes were identified compared to the prior study . HoweverOur results show that the minority of genes with which AGL15 is directly associated show response, as measured by transcript accumulation. Several studies had similar comparable fractions as direct targets that are responsive to the accumulation of the transcription factor. For instance, for HY5, only 5.6% of the bound sites showed a response at the cutoffs used . AssociaAT1G22220 (AUXIN UP-REGULATED F-BOX PROTEIN 2-AUF2) [(SENESCENCE-ASSOCIATED GENE 21-LEA5/SAG21 [ARGONAUTE10 (AGO10), which was shown to be involved in regeneration processes and the maintenance of stem cell populations in Arabidopsis by controlling the miR165/166 pathway [When we performed the analysis of the GO term “biological responses” using the unique ChIP-Seq bound genes (2944) and direct induced targets (283), we found a gene list involved in the cellular response to stress (GO:0033554), which is one of the key inducing factors for SE. Some of the genes found were  2-AUF2)  and AT4GA5/SAG21   , and their interaction with AGL15 in SE have been reviewed in Joshi et al. (2022) . The rol0010105) , and theBZR1 and BES1, discussed above, we found two additional BR-related genes AT2G01950, SERINE/THREONINE-PROTEIN KINASE BRI1-LIKE 2 (BRL2—direct AGL15 repressed target), and AT2G13790, SOMATIC EMBRYOGENESIS RECEPTOR KINASE 4  from this study, which might have potential roles in BR signaling and SE. BRL2 encodes a leucine-rich repeat receptor-like kinase (LRR-kinase) that is similar to BRI, a brassinosteroid receptor [SERK4 is associated with embryo lethality when combined with mutants in serk1/serk2/serk3(bak1) [One new GO annotation that was discovered in the NGS work, but not the array approach, was “cellular response to brassinosteroid stimulus” GO:0071367), which was significantly overrepresented in the AGL15 direct, repressed list . Recent 67, whichreceptor . SERK4 ireceptor . A loss k3(bak1) . Both gek3(bak1) ,33, lookk3(bak1) , “Develok3(bak1) , with dak3(bak1) ,39,40.Arabidopsis thaliana wild-type (wt), and 35S:AGL15 plants  were used for SAM SE. The seeds were allowed to develop to dry seed and SAM SE performed as described in [ribed in . For RNANuclei were isolated using extraction buffer  and soniChIP was performed as previously described . To analw/v) high MW  PEG . Two biological replicates were prepared for wild type and 35S:AGL15 and sent for library preparation and RNA-Seq . Data were analyzed by using CLC Genomics Workbench 12. To cast the broadest net, we used p > 0.05 and fold change of at least 1.5-fold cutoffs. The double mutant agl15/18 SAM SE expression data have been described in Paul et al. (2021) [For 10 d old SAM SE seedlings, RNA isolation and RNA-Seq were performed as follows. RNA was isolated using the QIAGEN RNeasy Plant Mini Kit following the manufacturer’s instructions, but supplementing the RLC buffer with 1% final (. (2021) . GO term. (2021) . The con. (2021) . The oliagl15 agl18) can be found in the GenBank/EMBL data libraries under the Bio project accession number PRJNA777254. The data for microarray and ChIP-Chip is available through GEO series accession number GSE17742 [35S:AGL15 SAM SE is available through accession number PRJNA903892.Sequence data for ChIP-Seq, and RNA-Seq (Col, wt and


Haemophilus influenzae, and by constructing a high-quality genome-scale metabolic model, we rationally identified new metabolic drug targets in this organism. Contextualization of available gene essentiality data within in silico predictions identified most genes involved in lipid metabolism as promising targets. We focused on the β-ketoacyl-acyl carrier protein synthase III FabH, responsible for catalyzing the first step in the FASII fatty acid synthesis pathway and feedback inhibition. Docking studies provided a plausible three-dimensional model of FabH in complex with the synthetic inhibitor 1-(5-(2-fluoro-5-(hydroxymethyl)phenyl)pyridin-2-yl)piperidine-4-acetic acid (FabHi). Validating our in silico predictions, FabHi reduced H. influenzae viability in a dose- and strain-dependent manner, and this inhibitory effect was independent of fabH gene expression levels. fabH allelic variation was observed among H. influenzae clinical isolates. Many of these polymorphisms, relevant for stabilization of the dimeric active form of FabH and/or activity, may modulate the inhibitory effect as part of a complex multifactorial process with the overall metabolic context emerging as a key factor tuning FabHi activity. Synergies with antibiotics were not observed and bacteria were not prone to develop resistance. Inhibitor administration during H. influenzae infection on a zebrafish septicemia infection model cleared bacteria without signs of host toxicity. Overall, we highlight the potential of H. influenzae metabolism as a source of drug targets, metabolic models as target-screening tools, and FASII targeting suitability to counteract this bacterial infection.Expediting drug discovery to fight antibacterial resistance requires holistic approaches at system levels. In this study, we focused on the human-adapted pathogen IMPORTANCE Antimicrobial resistance drives the need of synergistically combined powerful computational tools and experimental work to accelerate target identification and drug development. Here, we present a high-quality metabolic model of H. influenzae and show its usefulness both as a computational framework for large experimental data set contextualization and as a tool to discover condition-independent drug targets. We focus on β-ketoacyl-acyl carrier protein synthase III FabH chemical inhibition by using a synthetic molecule with good synthetic and antimicrobial profiles that specifically binds to the active site. The mechanistic complexity of FabH inhibition may go beyond allelic variation, and the strain-dependent effect of the inhibitor tested supports the impact of metabolic context as a key factor driving bacterial cell behavior. Therefore, this study highlights the systematic metabolic evaluation of individual strains through computational frameworks to identify secondary metabolic hubs modulating drug response, which will facilitate establishing synergistic and/or more precise and robust antibacterial treatments. Haemophilus influenzae is a human-adapted Gram-negative bacterial pathogen. Asymptomatic colonization begins in the upper airways, but it can spread through the respiratory tract and lead to invasive infections. The polysaccharide capsule vaccine has driven the almost complete disappearance of capsulated H. influenzae type b (Hib) in countries with established child immunization programs (H. influenzae serotypes and nontypeable strains (NTHi), untargeted by the Hib vaccine, emerge as important causes of infections. NTHi causes otitis media, conjunctivitis, sinusitis, and lower respiratory infections in children; exacerbations of chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF) in adults; and invasive disease in neonates, immunocompromised adults, and the elderly .protocol  and the protocol . A detai cytosol . Compareabolites . Reactioabolites . iNL638 ote tool  to definiNL638, we evaluated its capability of predicting well-known metabolic traits of RdKW20, such as the production of acetate and formate  only played a limited role  as a main source was taken by the PDH, which completely replaced PFL at oxygen uptakes higher than 7 mmol/gDW·h−1. Finally, under optimal oxygen availability, we observed a stable growth rate with acetate as the sole end product. These predictions agree with the switch in pyruvate metabolism from PFL to PDH, observed in H. influenzae as oxygen becomes available  and in the complete formulation (cCDM). We found 260 and 169 essential genes using minimal and complete CDM, respectively  Quantitative comparison of available experimental datasets of essential genes in H. influenzae. Download FIG S1, TIF file, 2.7 MB.Gene essentiality analysis. (A) Copyright © 2022 López-López et al.2022López-López et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the in silico and experimental data sets to identify subsystems overrepresented in all cases  and β-hydroxyacyl-ACP products, which are key components of the bacterial membrane. Acyl-ACP is used by the 1-acyl-sn-glycerol-3-phosphate (PlsB) and 1-acyl-sn-glycerol-3-phosphate (PlsC) acyltransferases to generate phosphatidic acid, the precursor of H. influenzae phospholipids phosphatidyl ethanolamine (PE) and phosphatidyl glycerol (PG) , 6 genes encoding enzymes required for phospholipid biosynthesis, and 12 genes encoding enzymes involved in lipid A biosynthesis . The H. rol (PG) ; β-hydrorol (PG) . Predict10.1128/msystems.01459-21.6FIG S2H. influenzae lipid metabolism. Genes commonly predicted to be essential by the iNL638 model in both complete or minimal CDM  determined for some of them (H. influenzae FabH activity (IC50 of 0.82 μM), high solubility, acceptable human plasma protein binding, easy chemical accessibility . Fa. Fa42). th Arg36  to D. Tholi FabH , as doesoli FabH . Single of FabHi , support10.1128/msystems.01459-21.7FIG S3RdKW20 structure are highlighted by colors. N-terminal domain core, alpha-helices (Nα1-Nα3) in light cyan and βeta-strands (Nβ1-Nβ5) in orange. C-terminal domain core, alpha-helices (Cα1-Cα3) in dark cyan and beta strands (Cβ1-Nβ54) in magenta. Elements of N-terminal insertions are in yellow (Lβ1-Lβ2) and light blue (Lα1-Lα2), and C-terminal ones in light pink (Lβ3-Lβ4) and green (Lα3). Download FIG S3, TIF file, 2.8 MB.Multiple alignment of FabH variants. Variants were identified in a previously whole-genome-sequenced NTHi clinical isolate collection . PolymorCopyright © 2022 López-López et al.2022López-López et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.01459-21.8FIG S4RdKW20 interaction mode. FabHi-FabHRdKW20 interaction mode compared with crystal structures of the E. coli enzyme in complex with close structural analogues of the inhibitor. (A) FabHi bound to FabHRdKW20 (green) superimposed to E. coli FabH (cyan) in complex with compounds 23 (violet) and 36 (cyan)  Comparison of ligand disposition in the FabHRdKW20 docking complex and the FabHE. coli complexes shown in panel A. Protein residues at hydrogen-bonding distance are also depicted (same color code). (C and D) Ligplot representation of hydrogen bonds (dashed lines) and hydrophobic contacts (brown arcs with radiating spokes) in the FabHi:FabHRdKW20 complex and the 5BNSE. coli complex, respectively. Polymorphisms of FabH may affect FabH stability and activity. (E) Structure of the FabH dimer with FabHi (sticks) bound to the active site of each monomer. Loops comprising residues 84 to 89 or 185 to 207 are colored in blue and orange, respectively. (F to H) Details of contacts at the dimer interface potentially sensitive to polymorphisms at positions 85 (F), 197 to 198 (G), 105, 118, and 186 (H). (I) Details of contacts relevant for the monomer stability potentially affected by polymorphisms of residues 169, 286, 287, and 296. Ser169 of RdKW20, together with E167, keep the orientation of strands Nβ1 and Nβ5  by hydrogen bonding to Asn2 and Arg4 residues. Network of hydrogen bonds participated by Ala286, Ile287 and Glu296 helping orientation of Cβ2, Cα3, Cβ3, Cβ4 and configuration of loop connecting Cα3 to Cβ3  is shown. Residues affected by polymorphisms are underlined and catalytic residues  are shadowed. Elements of secondary structure are indicated in brown lettering . Download FIG S4, TIF file, 2.8 MB.FabHi-FabH6 (cyan) , E. coliCopyright © 2022 López-López et al.2022López-López et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the H. influenzae viability.Before antimicrobial effect testing, we tried to improve FabHi solubility by preparing different salts . Assays RdKW20:FabHi complex structure relied on the H. influenzae RdKW20 genome content and FabHRdKW20 crystal structure  , with different frequency and CT distribution but intra-CT conservation  , 46. Beianalysis –50. FourH. influenzae strains to FabHi upon growth in CDM, being the medium used to interrogate the iNL638 model ; (ii) sharp slope without complete viability loss ; (iii) not-as-sharp slope but reaching complete viability loss ; and (iv) not-as-sharp slope, without complete viability loss .We first tested the susceptibility of the 14 selected cubation  to F, we10.1128/msystems.01459-21.9FIG S5H. influenzae selected strains carrying FabH variants A1 to A14 in two different media, CDM (A) or sBHI (B). NTHi strains grown on PVX agar for 16 h were inoculated (2 to 3 colonies) in 10 mL sBHI or CDM using 100-mL flasks and incubated for 11 h with shaking (100 rpm). Cultures were then diluted to OD600 of 0.07 in a final volume of 25 mL sBHI or CDM using 250-mL flasks, grown with shaking (180 rpm), and OD600 was recorded every hour for up to 8 h. (C to F) Antimicrobial effect of FabHi on H. influenzae. Determination of FabHi inhibitory effect on a panel of representative NTHi strains carrying different FabH allelic variant (A1 to A14), grown in CDM. Strains were shown to be susceptible to FabHi in a dose-dependent manner and were classified into four groups  based on increasing resistance. FabHi concentrations tested range from 10 to 600 μg/mL. Results are shown as log10 CFU/mL (means ± SD). For each strain, statistical comparisons of means were performed by one-way ANOVA and Dunnett’s multiple-comparisons test. A significant reduction on bacterial viability was observed: A1-NTHi375, at [FabHi] 200 μg/mL and higher, P < 0.0001; A2-P621, at [FabHi] 250 μg/mL and higher, P < 0.0005; A3-RdKW20, at [FabHi] 250 μg/mL and higher, P < 0.0001; A4-P652, at [FabHi] 75 μg/mL and higher, P < 0.0001; A5-P673, at [FabHi] 125 μg/mL and higher, P < 0.0001; A6-P642, at [FabHi] 100 μg/mL and higher, P < 0.0001; A7-P665, at [FabHi] 150 μg/mL and higher, P < 0.05; A8-P657, at [FabHi] 200 μg/mL and higher, P < 0.005; A9-P645, at [FabHi] 200 μg/mL and higher, P < 0.0001; A10-P851, at [FabHi] 150 μg/mL and higher, P < 0.0001; A11-P593, at [FabHi] 125 μg/mL and higher, P < 0.05; A12-P607, at [FabHi] 125 μg/mL and higher, P ≤ 0.0005; A13-P590, at [FabHi] 125 μg/mL and higher, P < 0.05; A14-P626, at [FabHi] 125 μg/mL and higher, P < 0.0001. (G) Expression of the fabH gene in a panel of representative NTHi strains carrying different fabH allelic variant, grown in CDM to half of each strain’s maximal OD600. This gene expression was shown to be variable among strains and not linked to the differences observed in terms of susceptibility. Statistical comparisons of means were performed for each previously established group by one-way ANOVA and Tukey’s multiple-comparison test . Antimicrobial effect of FabHi on strains A2-P621 (H), A7-P665 (I), A9-P645 (J), and A13-P590 (K), grown in sBHI (gray circles) or CDM (white circles). For each strain, statistical comparisons of means were performed by two-way ANOVA and Sidak’s multiple-comparisons test. A significant difference on bacterial viability was observed: A2-P621, at [FabHi] 275, 300 and 400 μg/mL, P < 0.005; A7-P665, at [FabHi] 600 μg/mL, P < 0.0001; A9-P645, at [FabHi] 125, 200, 275, 400 μg/mL and higher, P < 0.05; and A13-P590, at [FabHi] 400 μg/mL and higher, P < 0.005. Download FIG S5, TIF file, 2.8 MB.Effects of growth medium in bacterial phenotypes. Growth of Copyright © 2022 López-López et al.2022López-López et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the H. influenzae growth and antimicrobial susceptibility testing  to those forming poorly detectable biofilms (A4-P652 and A5-P673). FabHi 50 μg/mL (+), which allowed growth of all tested strains , loweredH. influenzae clinical strains representative of the observed FabH variation decreased when exposed to FabHi in a dose-dependent manner and with variable efficacy among strains. The susceptibility of H. influenzae to FabHi irrespective of the culture conditions argues in favor of FabH as a condition-independent drug target and highlights our gene essentiality analysis as a robust computational approach to identify drug targets.In summary, the viability of RdKW20 structure showed that amino acid substitutions were out of the substrate-binding cavity phenyl)pyridin-2-yl)piperidine-4-carboxylic acid showed increased susceptibility upon inactivation of the AcrAB-TolC efflux pump system . AcrAB-Tibiotics , this ism RdKW20  and the fabH gene allelic variation, we tested strains carrying the same variant. Variants A1 (NTHi375 as reference), A3 (RdKW20 as reference), A5 (P673 as reference), A6 (P642 as reference), and A8 (P657 as reference) were used as representative examples (To further assess if the observed heterogeneity would match only to examples . No signin vivo assays (see below), to become resistant to FabHi through serial independent passage in sBHI broth containing three different inhibitory concentrations of FabHi  medium aiming to mimic cultured epithelial cell infection conditions, showing a significant dose-dependent decrease . Based o10.1128/msystems.01459-21.10FIG S6P < 0.0001); 0 to 40%, high toxicity; 41 to 60%, intermediate toxicity; 81 to 100%, no toxicity. Download FIG S6, TIF file, 0.5 MB.FabHi toxicity analyses. (A) Cytotoxicity measured by the release of lactate dehydrogenase by A549 cells incubated for 16 h with increasing doses of FabHi in serum-starved medium. FabHi did not cause a cytotoxic effect on cells under the conditions tested (means ± SD). Statistical comparisons of the means were performed with one-way ANOVA and Dunnett’s multiple-comparison test. (B) Effect of FabHi on the mortality of zebrafish embryos exposed for 96 h postfertilization. Zebrafish embryos (20 per concentration) were distributed in 96-well microplates (one embryo/well), and exposed to FabHi at concentrations ranging from 0.5 to 12.5 μg/embryo . Mortality was assessed at the indicated time. C-, untreated negative control; C+, paracetamol-treated positive control; DMSO, vehicle solution by adding a volume equivalent to the one used with the highest drug concentration. Figure represents means ± SD from three biological triplicates  with 10 μL of bacterial suspensions containing 7 × 1010 CFU/mL or 0.9% saline solution as a control. NTHi P642 severely reduced zebrafish survival, and an infection dose consisting of 7 × 108 CFU/zebrafish caused progressive death of the animals after injection; however, strain P673 did not modify zebrafish survival (data not shown), precluding us from using this strain in this model system.Our final goal was to evaluate drug efficacy at the preclinical level, on a FabHi protective assay in adult zebrafish infected with NTHi, by following a previously established sepsis model , 58. We P < 0.0001)  test. Results showed that FabHi doses lower than 5.7  μg/embryo were nontoxic . Based o 0.0001) . This moH. influenzae as a basic computational framework to identify essential genes (among other possibilities) suitable for further exploitation as drug targets. The exponential increase of pathogen genomes and the emergence of high-throughput technologies are providing a myriad of large data sets. Among the numerous computational methods developed to generate meaningful outcomes of these data, metabolic modeling is emerging as a promising approach for the coherent organization of this information while providing mechanistic insights on biological systems  and second  substrates and the feedback inhibitor , all of which will compete with FabHi binding. Hence, the antimicrobial effect of FabHi depends on the available concentrations of all potential ligands and their respective affinities, which may vary among strains and growth conditions. Indeed, as shown above, several polymorphisms in FabH variants locate at regions relevant for activity and structural stability, and sequence changes at such positions may alter substrates and inhibitor binding as well as FabH catalytic efficiency. Additionally, strain variability associated with different CTs might modify, among others, the efflux pump system, with a subsequent impact on the amount of FabHi available to interact with FabH. Likewise, variations in expression level, conformation, or potency of other components of the FASII biosynthetic pathway, including peripheral enzymes involved in production of FabH substrates , may modNonetheless, variations observed among strains carrying the same FabH variant indicate that heterogeneity is multifactorial. Those strains are diverse in terms of genomic content, i.e., they do not cluster by CT , and sucRdKW20 complex structure indicates that FabHi affinity could be increased by introducing a hydroxyl group ortho to the N-atom of the piperidine ring, which could lead to an extra hydrogen bond with the side chain of Asn246 without introducing steric clashes. While of interest for further analysis, exploring the effect of FabHi modifications exceeds the scope of this study.On the other hand, inspection of FabHi:FabHH. influenzae and show how iNL638 can be used (i) as a powerful tool to better understand the metabolism of this pathogen, (ii) as a computational framework for large experimental data set contextualization, and (iii) for the discovery of condition-independent drug targets. We shed light on the mechanism of inhibition of FabH mediated by FabHi, and, beyond allelic variation, we suggest the strain-specific metabolic context is a key factor modulating drug performance. Therefore, the systematic metabolic evaluation of individual strains through computational frameworks will be of critical importance in the near future to design more precise and accurate treatments.Overall, we present here a high-quality metabolic model of 600) of 0.07 in a final volume of 25 mL sBHI or CDM using 250-mL flasks, grown with shaking (180 rpm), and OD600 was recorded every hour for up to 8 h. When necessary, bacterial viability upon host cell infection conditions was tested. To do so, phosphate-buffered saline (PBS)-normalized bacterial suspensions (OD600 of 1) were prepared by using NTHi grown on PVX agar; 100-μL aliquots were incubated in 1 mL Earle’s balanced salts  in the absence/presence of FabH inhibitor (FabHi) for 2 h at 37°C, serially diluted, and plated on supplemented Haemophilus Test Medium (sHTM) agar for CFU/mL determination. When needed, media supplements refer to with 10 μg/mL hemin and 10 μg/mL nicotinamide adenine dinucleotide (NAD).RdKW20 is a genome-sequenced capsule-deficient laboratory strain . NTHi37547–2O (dH2O), filtered, and used at the indicated working concentrations.FabHi was used by freshly preparing a panel of stock solutions, customized to each type of assay (7 to 35 mg/mL) in dimethyl sulfoxide (DMSO), and diluted to the working concentrations specified in each assay in CDM, sBHI, or RPMI 1640 . Antibiotics and FabHi working concentrations are specified for each assay (described below). Ampicillin and azithromycin dihydrate  were used to assess FabHi synergic effects (described below). For this purpose, 10-mg/mL stock solutions of each antibiotic were prepared in distilled H600 of 0.07 in a final volume of 55, 40, or 25 mL CDM using 250-mL flasks, grown with shaking (180 rpm) for ∼3, 4, or 6 h, respectively. Next, 45, 30, and 15 mL was collected at OD600 of 0.3, 0.4 and 0.8, respectively. Bacterial suspensions were centrifuged at , and pellets were instantly frozen in liquid N2, directly transferred to a lyophilizer , lyophilized for 24 h at 0.020 × 105 Pa and −80°C, and weighed. Using this procedure, we established a lineal relation (R2 = 0.99) between OD and DW: DW (g/liter) = (0.8627 × OD600) + 0.0279 (see Two to 3 colonies of the RdKW20 strain grown on PVX agar for 16 h were inoculated in 10 mL CDM using 100-mL flasks and incubated for 11 h with shaking (100 rpm). Cultures were diluted to OD10.1128/msystems.01459-21.3TABLE S1Table S1, DOCX file, 0.03 MB.Bacterial cultures dry weight. Download Copyright © 2022 López-López et al.2022López-López et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the H. influenzae lacks the GPR relationship, it could not be used as the template for draft construction. Instead, we used the high-quality and well-curated GEM of E. coli K-12  and peptidoglycan biosynthesis, were manually mass and charge balanced and added to the reconstruction. Furthermore, the reactions involving strain-specific cofactors were updated, such as those reactions using menaquinones or ubiquinones from the E. coli model, which were replaced by reactions accounting the cofactor usage of H. influenzae, i.e., demetylmenaquinone and demethylmenaquinol, respectively. Finally, the network gaps across the metabolic pathways were filled out by manual gap filling by adding new reactions based on the information stored in biological databases, scientific literature, and the functional annotation of strain RdKW20. For instance, metabolic gaps in fatty acid and molybdopterin biosynthesis pathways were manually completed during this step. Additionally, transport reactions were manually curated considering the connectivity of the cognate metabolites and experimental evidence. For instance, transport reactions for certain amino acids, fatty acids, uracil, pantothenate, biotin, dihydrofolate, protoporphyrin, pyridoxine, riboflavin, nucleotides, or NAD transporters were added manually based on available biochemical and physiological evidence. On the contrary, transport reactions for several compounds initially included in the automatic draft were removed due the lack of physiological evidence. The evaluation of iNL638 was done with MEMOTE v0.13.0 (https://memote.io/) (Since the available GEM of (iJO1366 ) for thiote.io/) .H. influenzae RdKW20 was used as a base for the construction of a highly complete biomass reaction  were used for solving the linear programing problems. The constraint-based model consists of a 1,161 by 1,385 matrix containing all the stoichiometric coefficients in the model of 1,161 metabolites and 1,385 reactions (S). Flux balance analysis (FBA) was used to predict growth and flux distributions , whereas mCDM formulation only allowed uptake of inorganic salts and essential vitamins for growth . Source code to simulate the iNL638 with cCDM and mCDM media and the MEMOTE full report are available at https://github.com/SBGlab/Haemophilus_influenzae_GEM.Formulation of ll cases , 27, 89.singleReactionDeletion function implemented in the COBRA Toolbox. FBA  was performed on silica gel 60 . Compounds were purified by flash column chromatography with silica gel 60 (230 to 400 mesh) (Merck). The purity of the compounds was analyzed using a high-performance liquid chromatography mass spectrometer (HPLC-MS) and was performed on an HPLC Waters 2695 instrument connected to a Waters Micromass ZQ 2000 spectrometer and a photodiode array detector. The column used was a Sunfire C18 , and the flow rate was 1 mL min−1. All retention times are quoted in minutes. Nuclear magnetic resonance (NMR) spectra were recorded with Varian XL-400 spectrometer operating at 400 MHz for 1H-NMR and at 100 MHz for 13C with Me4Si as an internal standard. Mass spectra were measured on a quadrupole mass spectrometer equipped with an electrospray source . Microanalyses were obtained on a Heraeus CHN-O-RAPID instrument. The purity of novel compounds was also determined to be >95% by elemental analysis.CH4, filtered, and evaporated to dryness. The final residue was purified by flash column chromatography  to give 1  as a colorless oily solid. 1H-NMR  δ 8.19 , 7.76 , 6.95 , 4.00 , 2.92 , 2.35 , 1.84 , 1.76 , 1.52 − 1.41 , 1.23 . HPLC 9.02 min (98%) (H2O/CH3CN from 15/85 to 95/5 in 10-min flowrate of 1 mL/min). MS (ES+) m/z 327.0 (M)+, 329.0 (M + 2)+. Anal. for C14H19BrN2O2 : C, 51.39; H, 5.85; N, 8.56. Found: C, 51.22; H, 5.91; N, 8.63.A solution of ethyl piperidine-4-acetate , 5-bromo-2-chloropyridine , and triethyl amine  in dry acetonitrile was heated under microwave irradiation to 125°C for 2 h. The crude was diluted with ethyl acetate (EtAcO) (10 mL) and washed with 1N HCl. The combined organics were dried over MgSO2CO3  in anhydrous DMF (12 mL) was stirred at 75°C for 16 h. The reaction was cooled to room temperature, and the residue was filtered through Celite and evaporated to dryness. The final residue was purified by flash column chromatography  to give 2  as a white solid. 1H-NMR  δ 8.40 , 7.71 , 7.39 , 7.25 , 7.13 , 6.73 , 4.75 , 4.28 , 4.17 , 3.12 , 2.54 , 2.00 , 1.83 , 1.27 . HPLC 5.03 min (99%) (H2O/CH3CN from 15/85 to 95/5 in 10-min flowrate of 1 mL/min). MS (ES+) m/z 373.2 (M + 1)+. Anal. for C21H25FN2O3 : C, 67.72; H, 6.77; N, 7.52. Found: C, 67.51; H, 6.80; N, 7.65.A suspension of 1 , 2-fluoro-5-(hydroxymethyl)phenylboronic acid , palladium(II) dichloride , K2O . The aqueous phase was acidified to pH 5, and the organic phase was separated. The aqueous phase extracted with EtAcO, and the organic phase combined was dried over MgSO4, filtered, and evaporated to dryness to obtain 3 . 1H-NMR  δ 8.24 , 7.65 , 7.38 , 7.22 , 6.87 , 4.47 , 4.30 , 2.80 , 2.14 , 1.90 , 1.70 , 1.12 . 13C-NMR  δ 174.1, 158.6 , 158.5, 147.7 , 139.7 , 138. 1 , 128.3 , 127.2 , 125.6 , 119.8, 116.2 , 107.1, 62.7, 60.3, 45.2, 41.2, 33.3, 31.5. HPLC 1.35 min (96%) (H2O/CH3CN from 15/85 to 95/5 in 10-min flowrate of 1 mL/min). MS (ES+) m/z 345.4 (M + 1)+. Anal. for C19H21FN2O3 : C, 66.27; H, 6.15; N, 8.13. Found: C, 66.31; H, 6.05; N, 8.19.A solution of 2  in THF-methanol (MeOH)  and 1 M NaOH (1.5 mL) was refluxed for 4 h. The mixture was evaporated to dryness, and the residue was dissolved in a mixture of EtAcO-HRdKW20; Protein Data Bank entry 3IL3) as receptor and FabHi as ligand. In detail, the protein was considered rigid and the ligand flexible. FabHi structure was carefully built using the PyMOL visualizer  from the atomic coordinates of ligand cocrystallized with E. coli FabH in PDB entry 5BNR  and diluted 1:100. Next, 50-μL bacterial aliquots were transferred to each well. A vehicle solution control consisting of a volume of DMSO equivalent to that of the highest FabHi concentration tested was performed in parallel (indicated as 0 [FabHi] μg/mL). Blank controls (sBHI or CDM added) were used. Plates were incubated for 24 h at 37°C, 5% CO2, without shaking. Culture samples were serially diluted and plated on sHTM agar. Data are shown as log CFU/mL; experiments were performed in duplicate at least three times (n ≥ 6). Alternatively, FabHi antimicrobial effect was assessed on H. influenzae biofilm grown in sBHI. For this purpose, assays were set as indicated above, and bacterial growth was determined by measuring OD600 on a SpectraMax 340 microplate reader. The liquid portion in each well (containing planktonic bacteria) was then discarded, and plates were washed 3 times through gentle submersion in distilled water and allowed to air dry. Next, 150 μL/well 0.5% crystal violet dissolved in dH2O was added, and plates were incubated for 20 min with gentle shaking at room temperature, followed by plate washing as previously described. Finally, 150 μL/well 95% ethanol (Merck) was added, plates were incubated for 20 min with gentle shaking at room temperature, and the OD570 was determined as a measure of biofilm growth. The OD570/OD600 ratio for each strain and independent assay was calculated to normalize biofilm biomass to overall growth; experiments were performed in triplicate at least three times (n ≥ 9).A 25-mg/mL FabHi stock solution in DMSO was used to prepare 40-, 200-, 300-, 400-, 500-, 600-, 800-, 1,000-, 1,100-, 1,200-, 1,600-, 2,000-, and 2,400-μg/mL working solutions in CDM or in sBHI. Fifty-microliter aliquots of sBHI or CDM were transferred to individual wells. Next, 50 μL of each FabHi working solution was added and mixed and 50 μL was discarded. A suspension of PVX agar with freshly grown bacteria was generated in sBHI or CDM, adjusted to 0.5 MacFarland  index of each FabHi-Amp or FabHi-Azm combination was calculated to determine the existence of synergy (ΣFIC ≤ 0.5), additive (0.5 > ΣFIC ≤ 1), indifferent (1 > ΣFIC < 4), or antagonic (ΣFIC ≥ 4) effects. Three independent assays were performed (n = 3).A 7-mg/mL FabHi stock solution was used to prepare 300-μg/mL working solutions in sBHI; 10-mg/mL Amp or Azm stock solutions were used to prepare 4-μg/mL and 2-μg/mL working solutions in sBHI, respectively. After generating each FabHi-Amp or FabHi-Azm concentration matrix, each well of a 96-well microtiter plate contained 80 μL final volume. A suspension of PVX agar and freshly grown bacteria was generated in sBHI, adjusted to OD2, without shaking. Cultures were then passaged (50 μL in 50 μL fresh sBHI with FabHi or vehicle solution) every day for 12 days. At each time point throughout the cycling, each well’s absorbance (OD600 at 24 h minus OD600 at 0 h) was measured. Nine replicates per condition were made (n = 9).A 14-mg/mL FabHi stock solution was used to prepare 550-, 1,100-, and 2,200-μg/mL working solutions in sBHI. A vehicle solution control (indicated as V) consisting of a volume of DMSO equivalent to that of the highest FabHi concentration tested was performed in parallel. Blank controls (sBHI) were used; 50 μL sBHI was transferred to individual wells and mixed with 50 μL of each FabHi working or vehicle control solution, and 50 μL was discarded. A suspension of PVX agar and freshly grown bacteria was generated in sBHI, adjusted to 0.5 MacFarland, and diluted 1:100. Next, 50-μL bacterial aliquots were added to individual wells. Plates were incubated for 24 h at 37°C, 5% CO600 of 0.07, and grown to half of each strain’s maximal OD600 for ∼5 h. Bacterial total RNA was isolated using TRIzol reagent (Invitrogen) and total RNA quality evaluated using RNA 6000 Nano LabChips . Reverse transcription (RT) was performed using 1 μg RNA by PrimerScript RT reagent kit (TaKaRa). To amplify fabH, 1:10 diluted cDNA was used as the template (including gyrA endogenous control). In all cases, 20-μL reaction mixtures containing 1× SYBR Premix Ex Taq II (Tli RNaseH Plus) (TaKaRa) and the adequate primer mix were used. Fluorescence was analyzed with an AriaMx real-time PCR system (Agilent Technologies). The comparative threshold cycle (TC) method was used to obtain relative quantities of mRNA that were normalized using gyrA as an endogenous control. The fabH gene was amplified with primers fabH-F and fabH-R in all cases except for the P851 and P642 strains, where fabH-P851-P642-F and fabH-P851-P642-R primers were used (n = 3).Two to three colonies of bacterial strains grown on PVX agar were inoculated in 10 mL CDM or sBHI, grown for 11 h, diluted into 25 mL fresh CDM or sBHI to ODere used . Samples10.1128/msystems.01459-21.4TABLE S2Table S2, DOCX file, 0.06 MB.Primers used in this study. Download Copyright © 2022 López-López et al.2022López-López et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 5 cells/well for 32 h. To assess FabHi cytotoxicity, a 35-mg/mL FabHi stock solution was diluted in serum-starved RPMI 1640 (Sigma-Aldrich) to 125, 275, and 500 μg/mL (1 mL/well), cells were incubated for 16 h at 37°C, 5% CO2, and cytotoxicity was determined by measuring the release of lactate dehydrogenase  and microscopy. To assess the effect of FabHi during epithelial infection by NTHi, cells were maintained and seeded as indicated above and treated with FabHi in serum-starved RPMI 1640 for 16 h before infection. For infection, PBS-normalized bacterial suspensions (OD600 of 1) were prepared by using NTHi grown on PVX agar. Adhesion and invasion assays were performed as previously described (n = 9).A549 cells were maintained as described previously  and seedescribed , 48. Conhttps://www.ikanbiotech.com) animal housing facility were performed as previously described (n = 10/group). Infected groups were intraperitoneally injected with 10 μL of an exponentially grown (OD600 of 0.3) H. influenzae suspension containing 7 × 1010 CFU/mL (7 × 108 CFU/zebrafish) prepared in PBS. At 1 hpi, one infected and one uninfected group were intraperitoneally administered FabHi at a dose of 4 μg/zebrafish in 10 μL PBS by using a 35 mg/mL FabHi stock solution; the other two groups were administered PBS or vehicle solution. The survival rate for each group was monitored three times per day for 4 days after infection. Experiments were performed on three independent occasions.Animal experiments conducted at the Ikan Biotech , and are detailed in each figure legend.In all cases, a


Antimicrobial resistance (AMR) is a growing global problem and there were an estimated 4.95 million deaths associated with bacterial AMR worldwide in 2019. Vaccines can impact AMR by preventing infections and reducing the need for antibiotics which will inadvertently slow the emergence of AMR. Effective infection prevention and control (IPC) has been identified as the cornerstone action to combat AMR by the World Health Assembly and the Global Action plan on AMR. Similarly, the Immunization Agenda 2030 highlights vaccines as critical tools to combat AMR. This article summarizes the strategy of the World Health Organization to understand, articulate and communicate the important role of vaccines in countering AMR. The work is organized around developing a strategy, understanding the pipeline of vaccines in development, articulating the value of vaccines against AMR, and assuring sustainable impact of vaccines at a country level to combat AMR. In 2019, 4.95 million deaths were associated with drug-resistant bacterial infections globally.3 The antimicrobial resistant infections are more expensive to treat, are associated with higher mortality and morbidity rates as well as high socioeconomic impact. Estimates suggest that 28 million people will fall into poverty worldwide due to AMR with an increase in health-care costs of up to US$1 trillion worldwide by 2050,4 with low-income countries expected to be the most impacted. A rapid and multifaceted response is needed to prevent the significant disease burden and socio-economic cost.5Antimicrobial Resistance (AMR) occurs when bacteria, viruses, fungi and parasites no longer respond to antimicrobial agents. As a result of drug resistance, antibiotics and other antimicrobial agents become ineffective and infections become difficult or impossible to treat, increasing the risk of disease spread, severe illness and death. The prevalence of AMR is growing rapidly6The Global Action Plan on AMR lists five strategic objectives to contain AMR: optimizing the use of antimicrobials, preventing infections (including the use of vaccines), strengthening surveillance and research, improving awareness and understanding of AMR, and investing in new medical products.8 antibiotic use, a key driver of AMR.10 Vaccines therefore can contribute to reducing selection for AMR in both the target pathogen  as well as in bacterial species that are not directly targeted by the used antibiotics (bystander effect) , and reducing effect) . A study16 To develop a strategy on vaccines and AMR, the World Health Organization (WHO) has published an action framework18 - a technical annex to the Immunization Agenda 2030. The action framework describes a vision for vaccines to contribute fully, sustainably and equitably to the prevention and control of AMR by preventing infections and reducing antimicrobial use. To achieve this vision, the document articulates a list of priority actions to be taken by AMR and immunization stakeholders in three areas: expanding the use of licensed vaccines to maximize impact on AMR, developing new vaccines that contribute to the prevention and control of AMR, and expanding and sharing knowledge about the impact of vaccines on AMR (Panel 1). This document aims to support alignment of activities among international, regional and national vaccine and AMR implementing partners, and to structure and articulate key priority actions needed to articulate the value of vaccines against AMR and lead to implementation and impact.Immunization was highlighted as one of the tools to combat AMR in the Immunization Agenda 2030: A global strategy to leave no one behind.Salmonella typhi, Streptococcus pneumoniae, and Hemophilus influenzae tybe b; and to bring to market vaccines for Shigella, non-typhoidal Salmonella, and enteric Escherichia coli.8 Gavi, the Vaccine Alliance analyzed the value of vaccines against AMR to inform their Vaccine Investment Strategy in 2018. They found that pneumococcal, typhoid and malaria vaccines have the highest value against AMR.19There is growing recognition that vaccines are powerful tools to combat AMR. For example, the Wellcome Trust recommends to increase uptake of vaccines for 20 and has recently analyzed the preclinical and clinical pipeline for vaccines against these pathogens, in addition to Clostridioides difficile and Mycobacterium tuberculosis.21 The analysis builds on previous initiatives to evaluate the pipeline of antibacterials in preclinical22 and clinical24 development against the priority pathogens. In clinical development, 61 vaccine candidates were identified that target the priority pathogens, which were also classified into four groups  calling for the rapid introduction and expansion of already existing vaccines at country level, acceleration of clinical trials for pathogens with vaccines in late-stage development, research to understand the value of vaccines against pathogens in early stages of clinical development, and alternative ways to tackle AMR for pathogens with no vaccines in clinical development.The WHO has identified 12 priority pathogens for which new antibiotics are most urgently neededr groups . Group A25 (2) vaccine averted AMR economic burden, (3) vaccine averted antibiotic use, (4) sense of urgency to develop antimicrobial approaches, and (5) resistant pathogen impact on equity and social justice. The feasibility of vaccine development is being assessed for each of the 30 pathogens in scope. These pathogen-specific assessments will then be presented in the context of other available approaches to contain AMR. The overall goal of the value attribution framework is to support the prioritization of decisions and investments relating to vaccine development, introduction and use. The framework, once published, will be a platform to synthesize best available evidence for the impact of vaccines against AMR, and will highlight critical knowledge data gaps. Subsequently, this will also inform the development of a core set of evidence-based AMR interventions in the human health sector, including vaccines, that countries should consider in the revisions of their national action plans on AMR. In addition, the role of vaccines in preventing AMR will be included as a key intervention within the new “people-centered framework” for addressing AMR in the human health sector that is being developed by WHO.Despite the aforementioned WHO, Wellcome Trust and Gavi reports and analyses, the impact of vaccines on AMR is often not incorporated into evaluations of vaccine value. This means vaccines are often undervalued in terms of their impact on AMR across the public health community. To systematically incorporate the impact of vaccines on AMR, a value attribution framework is needed to guide ways of articulating vaccine impact on AMR. To this end, WHO is developing a value attribution framework for the impact of vaccines on AMR. The document considers the value of vaccines for 30 pathogens including bacteria, viruses, fungi and parasites, across five criteria: (1) vaccine averted AMR health burden,21 and to include indicators on immunization coverage within their national action plan implementation monitoring and evaluation frameworks.22 The proposed indicators are based on the indicators included in the monitoring and evaluation framework of the Global Action Plan on AMR that monitor immunization coverage for pneumococcal conjugate vaccine (PCV), rotavirus vaccine, measles-containing vaccine, and Haemophilus influenzae type b containing vaccine (Hib).26 However, only a handful of national action plans on AMR appropriately include vaccines.27 To improve this, governance, coordination and implementation of the national action plans on AMR should be closely aligned with national immunization programs, budgets and strategies.To ensure vaccines are optimally utilized to reduce the emergence and spread of AMR, WHO is engaging stakeholders in policy and decision-making at global and national levels. At the global level, WHO will work closely with the Strategic Group of Experts on Immunization (SAGE) to ensure the value of vaccines against AMR is systematically considered whenever decisions relating to vaccine introduction and use are made. To drive impact at the country level, WHO is working with countries and implementing partners to consider scaling up investment in vaccines as part of their National Action Plans on AMR,AMR is a complex global problem that requires multiple approaches to prevent and contain it, including vaccines. As such, WHO has outlined key strategies for vaccines to contribute fully to the prevention of AMR; evaluated the pipeline of vaccines in development for WHO priority pathogens; continues to estimate the value of vaccines in preventing AMR; and works with member states, academic institutions, non-governmental organizations, the pharmaceutical industry, and funders to better understand and communicate the impact of vaccines on AMR and implementation at country level. However, there remain challenges and opportunities for vaccines to achieve its full potential in preventing AMR. Expansion of equitable coverage to licensed vaccines should be accelerated; recommendations and guidance in vaccine and AMR sectors should include the role of vaccines in preventing AMR; awareness of the role of vaccines in preventing AMR should be increased through communication, education and training; the funding for research and development (R&D) of new vaccines needs increasing; and lastly, research to produce data on vaccine impact on AMR, especially in low-resource settings should be prioritized.


A theoretical-computational procedure based on the quasi-Gaussian entropy (QGE) theory and molecular dynamics (MD) simulations is proposed for the calculation of thermodynamic properties for molecular and supra-molecular species in the gas phase. The peculiarity of the methodology reported in this study is its ability to construct an analytical model of all the most relevant thermodynamic properties, even within a wide temperature range, based on a practically automatic sampling of the entire conformational repertoire of highly flexible systems, thereby bypassing the need for an explicit search for all possible conformers/rotamers deemed relevant. In this respect, the reliability of the presented method mainly depends on the quality of the force field used in the MD simulations and on the ability to discriminate in a physically coherent way between semi-classical and quantum degrees of freedom. The method was tested on six model systems imide ionic pairs), which, being experimentally characterized and already addressed by other theoretical-computational methods, were considered as particularly suitable to allow us to evaluate the method’s accuracy and efficiency, bringing out advantages and possible drawbacks. The results demonstrate that such a physically coherent yet relatively simple method can represent a further valid computational tool that is alternative and complementary to other extremely efficient computational methods, as it is particularly suited for addressing the thermodynamics of gaseous systems with a high conformational complexity over a large range of temperature. The ideal gas condition (no interactions among the molecules) allows us to obtain the system thermodynamics considering a single molecule, either in the canonical ensemble within a fixed volume corresponding to the system molecular volume or in the isobaric–isothermal ensemble with a fixed pressure identical to the system equilibrium pressure. In the isobaric–isothermal  ensemble, the Gibbs free energy of such a single molecule system . molecular quantum vibrational partition function defined by the vibrational energies V of the integral sign means that integration is performed within the volume V. Moreover, h being Planck’s constant, n being the number of the q-generalized semi-classical coordinates and Let us consider a homogeneous macroscopic  gas-state system, made of flexible molecules or molecular complexes  that we always consider chemically stable Δ=−k,T)e−βpVQ≅ΘQIn order to obtain the thermodynamics as a function of the temperature , we can use Equation  to expreΔβ=β−β0)βμ−βy obtainβμ′−q coordinates are well described as semi-classical degrees of freedom and, thus, Equations (Equations –12) pro pro12) pquations  and 9),,T0, the capacityh≅h0−e−βhνj)cp≅cmolecule within the reference conformation at M is the molecular mass, molecular rotations, k in each system). Note that the factorization in Equation ≅,ref(T0)Qrt,ref, th, thcp0′ Finally, it is worth remarking that we assume that within the whole temperature range considered, no mixing of quantum and semi-classical coordinates occurs, meaning that within each harmonic basin, the semi-classical modes obtained by the mass-weighted Hessian are always defined within the same configurational subspace, even if they can change from one basin to another. We actually estimate the number molecular species is simulated resembling the ideal gas state. For this purpose, we utilized Gromacs software [k by comparing N is the total number of internal degrees of freedom of the simulated system . For each studied system, we chose the largest k such that, within the noise, k values providing deviations between The core of the computational part of the work is the production of reliable semi-classical simulations of the isolated species along an isobaric path. Note that, obviously, the isobaric condition is automatically fulfilled when a single software ,45 versiermostat , the bonermostat  and the quations –23)) as) asmolecEquation  is a prolsewhere . Such a molecule. These calculations were performed in the framework of the density functional theory using the wB97XD functional [Obviously, the reliability of the present method in the form presented in this study, i.e., making use of a purely computational approach without any support from experimental data , entirelnctional  in conjusion C.1  program After producing the MD simulations ..T0Pref The obtained excess properties  equation of state, providing the explicit temperature dependence of all the relevant thermodynamic properties, proved to rather accurately reproduce the experimental thermodynamics of six molecular and supramolecular systems of different complexity. In particular, we tested the method on the following systems: (i) n-butane, an extensively investigated system both experimentally and computationally; (ii) the ion pairs known to mainly represent the vapor-phase in equilibrium over the ionic liquids 1-butyl-3-methylimidazolium hexafluorophosphate (BmimPF