Neonatal seizures are a major challenge for clinicians because of inconspicuous clinical presentation, variable electro-clinical correlation, and poor response to antiseizure drugs. It is well recognized that fever and infection can trigger seizures in young children and that this risk is enhanced in children with epilepsy. As immunization may cause a fever, vaccination can be a non-specific trigger for seizures in children 1.1.1The reported prevalence and incidence of neonatal seizures vary considerably due to differences in study methodology, especially in the identification of neonatal seizures, and geographic setting Incidence. The reported incidence of neonatal seizures worldwide varies from 1.0–4.4 per 1000 livebirths in high-income countries (USA) 1.1.2The etiology of neonatal seizures is heterogeneous, and sometimes unknown, although the majority are due to hypoxia-ischemia, stroke or infections in term infants. In preterm infants, intraventricular hemorrhage is the commonest cause of seizure The heterogeneity in the etiologic profile of neonatal seizures across geographies and economic strata is due to two main factors: differences in obstetric/perinatal care and access to electrodiagnostic techniques leading to differing rates of detection and diagnosis .1.1.3The onset of neonatal seizures depends on etiology and is most common within the first week of life, with 25–55% occurring in the first 24 h 1.1.4Maternal risk factors for neonatal seizures include maternal age >40 years, nulliparous, diabetes mellitus, chorioamnionitis, traumatic delivery, prolonged second stage of labor, fetal distress, placental abruption, cord prolapse, and uterine ruptureNeonatal risk factors for seizures include the etiologies for seizure listed in 1.1.5While a normal neurological outcome after neonatal seizures is reported in 25–40% of infants 1.1.6Developmental age-specific mechanisms influence the generation and phenotype of seizures. While there are some limitations in the use of animal models to study neonatal seizures, conclusions can be reached with consideration of the species-specific maturation rates in the system of interest The neonatal period is a time of intense brain development. While cortical lamination is fully developed in the term infant, neurite outgrowth and synaptogenesis are continuing and are in their elementary stages. Brain myelination is immature. These factors limit the rapid propagation of neonatal seizures and their clinical presentation  + channel super-family and important for maintenance of resting membrane potential and dendritic excitability, are also developmentally regulated. The immature brain has relatively low expression of the HCN1 isoform, which serves to reduce dendritic excitability in the adult brain In the neonatal brain, the balance between excitatory versus inhibitory synapses is tipped in favor of excitation to permit robust activity-dependent synaptic formation, plasticity, and remodeling. Glutamate is the major excitatory neurotransmitter in the CNS with the involvement of AMPA and NMDA receptors and more expression and function than in the adult brain. For example, while, in the adult brain, γ-amino-butyric acid (GABA) usually induces membrane hyperpolarization, early in the developing brain it induces membrane depolarization by causing Cl¯ efflux rather than influx. The HCN channels, which are members of the KGenetic epilepsies with onset in the neonatal period reflect the structural and physiologic factors that can lead to neonatal seizures. These include ion channel function (e.g. KCNQ2), excitation-inhibition balance (e.g. pyridoxine-dependent epilepsy), brain development (e.g. ARX) and synaptic function (e.g. STXBP1) 1.1.7The clinical diagnosis of neonatal seizures is challenging because many neonatal seizures either manifest with subtle clinical signs or remain entirely subclinical despite the presence of clear electrographic seizure activity on EEG.Clinical manifestations of neonatal seizures may include focal motor movements or non-motor signs The diagnosis of neonatal seizures may be made by cEEG, amplitude-integrated EEG (aEEG) or by clinical signs alone. Gold-standard is capturing a seizure on cEEG  because it provides the most direct and comprehensive assessment of neuronal activity. In comparison, aEEG is less accurate because it employs fewer electrodes over a smaller spatial area and the aEEG display is filtered and time-compressed making it harder to identify brief seizures. When aEEG is used together with a real-time EEG channel, the median sensitivity for seizure identification is 76% (range: 71–85%), and the median specificity is 85% (range: 39–96%). When aEEG was used without a real-time EEG channel, the median sensitivity is 39% (range: 25–80), and specificity is 95% (range 50–100) Among neonates who present with clinically apparent seizures, antiseizure drugs commonly suppress clinical activity, but ongoing electrographic seizures persist, a phenomenon termed uncoupling 1.1.8Early recognition and accurate diagnosis of seizures in the neonatal period is essential for optimal management. However, the clinical diagnosis of seizures in neonates is also challenging because infants may present with abnormal movements that are non-epileptic but are mistaken for seizures leading to inappropriate treatment and unwarranted prognostic concern 1.1.9Maternal vaccination. A literature search conducted by the authors did not identify any reports of seizures among newborns born to women who received tetanus-diphtheria-acellular pertussis (Tdap), tetanus toxoid, tetanus-diphtheria (Td), seasonal or pandemic influenza vaccines, or in randomized controlled trials of investigational Group B Streptococcus or respiratory syncytial virus vaccines. A retrospective cohort study of pertussis among infants <63 days of age reported no seizures among 34 infants (median age 45 days) whose mothers received Tdap during pregnancy, while 14/336 (4%) infants of unvaccinated mothers developed seizures with pertussis infection  Neonatal vaccination. In a study of claims in the United States National Vaccine Injury Compensation Program of seizures and/or encephalopathy allegedly caused by an immunization among children younger than two years during 1995–2005, a total of 90 claims (60%) concerned babies between 0 and 6 months of age but the number of neonates was not reported 1.1.10Several definitions of neonatal seizures exist . Neonata1.1.11Neonatal seizures are focal, often subclinical 1.1.12There is no uniformly accepted definition of neonatal seizures. This provides the opportunity to offer a definition that is practical and useful in the context of neonatal seizures following maternal and neonatal immunization, as data comparability across trials or surveillance systems will facilitate data interpretation and the assessment of vaccine safety, as well as promote the scientific understanding of neonatal seizures.1.2http://www.brightoncollaboration.org/internet/en/index/process.html, the Brighton Collaboration Neonatal Seizures Working Group was formed in 2018 and included members with clinical, academic, public health, industry backgrounds.Following the process described in the overview papers To guide the decision-making for the case definition and guidelines, we conducted a literature search using Medline, Embase and the Cochrane Central Register for English language articles reporting on seizures among neonates born to women vaccinated during pregnancy. In addition, we searched for clinical trials, passive and active surveillance reports, cohort and case-control studies of specific vaccines evaluated in pregnancy to capture additional reports of neonatal seizures and confirm the findings of our primary literature review. Only English language articles and articles referring to humans were selected for review. The primary search identified 82 articles excluding duplications of which 80 were excluded based on review of the title of abstract. The remaining two articles were excluded after review of the full text as they did not provide information regarding neonatal seizures and vaccines. A search for adverse events after maternal Tdap vaccination identified one relevant article that mentioned neonatal seizures.We extended the search to include reports of neonates with seizure after immunization at birth, following the same methods described above. A total of 194 articles excluding duplications were identified. Based on abstract content we selected 12 articles for complete reading. Articles were excluded mainly because they presented no detailed information about the age of the vaccinated infants (e.g. “infants 0–6 months”) or the specific vaccination schedule. Finally, only one original article was selected for inclusion in our systematic review 1.3The working group agreed that electrographically documented seizures with or without clinical manifestations represent the most accurate concept of neonatal seizures. There are several operational definitions for electrographic seizures in the newborn. According to the American Clinical Neurophysiology Society (ACNS), an electrographic seizure in a newborn is defined as a sudden, abnormal EEG event characterized by a rhythmic and evolving pattern with a minimum 2 µV peak-to-peak voltage and duration of at least 10 s. “Evolving” is defined as an unequivocal evolution in frequency, voltage, morphology, or location Amplitude-integrated EEG (aEEG) can be a useful instrument but less accurate : is a clinical term that applies to the estimated age of the fetus during pregnancy, generally given in weeks and days from the first day of the last menstrual period. According to the International Statistical Classification of Diseases and Related Health Problems (ICD-10) Neonatal seizures: relate to epileptic seizures in the neonatal period. It includes terms such as neonatal convulsions, neonatal fits, neonatal epilepsy and neonatal convulsive disorder . The preferred term is neonatal seizure.Epilepsy refers to a disorder with at least two unprovoked (or reflex) seizures occurring greater than 24 h apart or one unprovoked (or reflex) seizure and a probability of further seizures similar to the general recurrence risk (at least 60%) after two unprovoked seizures, occurring over the next 10 years 1.3.2The focus of the working group was to agree on a harmonized definition of neonatal seizures and the criteria to identify them, with different levels of diagnostic certainty. This will be useful also for the identification of neonatal seizures in the context of vaccination of mothers during pregnancy or neonatal vaccination.1.3.3It needs to be emphasized that the grading of definition levels is entirely about diagnostic certainty, not the clinical severity of an event. Thus, a very severe clinical event may appropriately be classified as possible (level 3) or probable (level 2), rather than definite (level 1), if it could reasonably be of a non-epileptic etiology. Detailed information about the severity of the event should additionally always be recorded, as specified by the data collection guidelines.The number of symptoms and/or signs that will be documented for each case may vary considerably. The case definition has been formulated such that the level 1 definition is highly specific for the condition. As maximum specificity normally implies a loss of sensitivity, two additional diagnostic levels have been included in the definition, offering a stepwise increase of sensitivity from level 1 down to level 3, while retaining an acceptable level of specificity at all levels. In this way, it is hoped that all possible cases of neonatal seizures can be captured.1.3.4The working group agreed to a definition of neonatal seizures (see below) and to give different levels of certainty in the diagnosis  in order to be effective and applicable in high-, middle- and low-income countries.Pathology, radiology and laboratory findings are not included in the case definition, although they can provide important information regarding the causes of neonatal seizure.1.3.5The working group decided against using “treatment” or “treatment response” towards the fulfillment of the case definition of neonatal seizures.A treatment response or failure is not in itself diagnostic, as less than 50% of neonatal seizures respond to the first line treatment  1.3.6Specific time-frames for the onset of symptoms of neonatal seizures following maternal immunization are not included. No information is available regarding the potential relevance of the timing of maternal immunization and the occurrence of neonatal seizures.We postulate that a definition designed to be a suitable tool for testing causal relationships requires ascertainment of the outcome  independent from the exposure . Therefore, to avoid selection bias, a restrictive time interval from maternal immunization to onset of neonatal seizures should not be an integral part of such a definition. Instead, where feasible, details of this interval should be assessed and reported as described in the data collection guidelines.Furthermore, neonatal seizures often occur outside the controlled setting of a clinical trial or hospital. In some settings, it may be impossible to obtain a clear timeline of the event, particularly in low resource and rural settings. To avoid exclusion of such cases, this Brighton Collaboration case definition avoids setting arbitrary time-frames between maternal immunization and occurrence of the defined event.1.4As mentioned in the overview, the case definition is accompanied by guidelines which are structured according to the steps of conducting a clinical trial, i.e. data collection, analysis and presentation. Neither case definition nor guidelines are intended to guide or establish criteria for management of ill infants, children, or adults. Both were developed to improve data comparability.1.5Similar to all Brighton Collaboration case definitions and guidelines, review of the definition with its guidelines is planned on a regular basis (i.e. every three to five years) or more often if needed.2Case definitionneonatal seizure is defined as a transient electrographic change in the brain due to an abnormal, excessive or synchronous neuronal activity either with the occurrence of clinical signs  or without them (electrographic-only), in the first 28 days of life in full-term infants. In the preterm infants (born <37 weeks of gestation), this definition applies up to 44 weeks of post menstrual age (PMA), considering the pattern of brain maturation.A Seizures confirmed by conventional EEG (cEEG) with or without clinical manifestations represent the most accurate concept of neonatal seizures; cEEG is considered the gold standard for neonatal seizure diagnosis (Level 1 – “definite” diagnosis). Ictal EEG refers to the epileptiform activity seen during a seizure in contrast to interictal discharges seen between seizures which are not diagnostic in neonates. Concomitant video recording is helpful although not a necessity and may be replaced by clinical observation during the EEG to determine a clinical-electrographic correlation.Amplitude-integrated EEG (aEEG) or cerebral function monitoring can be a useful instrument but is less accurate than cEEG see . The ideAs mentioned above, the clinical diagnosis of neonatal seizures is challenging and without EEG it is difficult to differentiate seizure from physiological or abnormal, but non-epileptic, movements see . HoweverAs discussed in https://www.epilepsydiagnosis.org/index.html.For further information on clinical manifestations and definitions of seizure types and epilepsy syndromes see LEVELS OF CERTAINTYFor All Levels of Diagnostic CertaintyAge 0–28 days in a full-term infantORPostmenstrual age of <44 weeks in a preterm infant (born <37 weeks of gestation)Level 1 of diagnostic certaintyLevel 2 of diagnostic certaintyLevel 3 of diagnostic certaintyLevel 4Level 5Notes for Levels of Certainty2sudden, abnormal EEG event characterized by repetitive and evolving pattern 3seizure confirmed with EEG and with clear clinical manifestation4seizure confirmed with EEG without clear clinical manifestation5regularly rhythmic jerking, that involves the same muscle groups and not influenced by manual restraint6focal sustained stiffening/sustained increase in muscle contraction lasting a few seconds to minutes and not influenced by manual restraint7someone who routinely cares for neonates and is familiar with the clinical presentation of neonatal seizures through training or clinical practice. Ideally this is a physician , but in different settings also other professionals  could diagnose “probable or possible seizures”, depending of their specific training in neonatal care8such as myoclonic, epileptic spasm, automatism, autonomic changes, behavioral arrest, but non-seizure events cannot be excluded without EEG [79]3Neonatal Seizures Working Group to recommend the following guidelines to enable meaningful and standardized collection, analysis, and presentation of information about neonatal seizures. However, the implementation of all guidelines might not be possible in all settings. The availability of information may vary depending upon resources, geographical region, and whether the source of information is a prospective clinical trial, a post-marketing surveillance or epidemiological study, or an individual sporadic report of neonatal seizures. Also, these guidelines have been developed by this working group for guidance only and are not to be considered a mandatory requirement for data collection, analysis, or presentation.It was the consensus of the Brighton Collaboration 3.1These guidelines represent a desirable standard for the collection of data on neonatal seizures following maternal immunization to allow for comparability of data and are recommended as an addition to data collected for the specific study question and setting. The guidelines are not specifically intended to guide the primary reporting of neonatal seizures to a surveillance system or study monitor, but they could potentially be adapted for these purposes. Investigators developing a data collection tool based on these data collection guidelines also need to refer to the criteria in the case definition, which are not repeated in these guidelines.Guidelines numbered below have been developed to address data elements for the collection of adverse event information as specified in general drug safety guidelines by the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use, and the form for reporting of drug adverse events by the Council for International Organizations of Medical Sciences. These data elements include an identifiable reporter and patient, one or more prior maternal immunization, and a detailed description of the adverse event, in this case, of neonatal seizures following maternal immunization. The additional guidelines have been developed as guidance for the collection of additional information to allow for a more comprehensive understanding of neonatal seizures following maternal immunization.3.1.1(1)Date of report.(2)10 and/or diagnosing the neonatal seizures as specified by country-specific data protection law.Name and contact information of person reporting(3)Name and contact information of the investigator responsible for the subject, as applicable.(4)Relation to the patient .For all cases and/or all study participants , the following information should be recorded:3.1.23.1.2.1(5)Case/study participant identifiers  or code (or in accordance with country-specific data protection laws).(6)Date of birth, age, and sex.(7)For neonates: gestational age and birth weight, twin status.For all cases and/or all study participants (including mothers and infants as appropriate), the following information should be recorded:3.1.2.2(8)Past and current gynecological/obstetric history, medical history, including hospitalizations, underlying diseases/disorders, pre- immunization signs and symptoms including identification of indicators for, or the absence of, a history of allergy or other reactions to vaccines, vaccine components or medications; food allergy; allergic rhinitis; eczema; asthma. Any family history of seizure, neonatal/infant death (sibling), or congenital/genetic conditions should be recorded.(9)Any medication history (other than treatment for the event described) prior to, during, and after maternal immunization during pregnancy including prescription and non-prescription medication as well as medication or treatment with long half-life or long-term effect. .(10)Maternal and infant immunization history (i.e. previous immunizations and any adverse event following immunization (AEFI), in particular occurrence of neonatal seizures after a previous immunization).For all cases and/or all study participants (including mothers and infants as appropriate), the following information should be recorded:3.1.3(11)Date and time of maternal and infant immunization(s).(12)Description of vaccine(s)  and number of dose if part of a series of immunization s against the same disease).(13)The anatomical sites (including left or right side) of all immunizations .(14)Route and method of administration .(15)Needle length and gauge.For all cases and/or all study participants (including mothers and infants as appropriate), the following information should be recorded:3.1.4(16)For all cases at any level of diagnostic certainty and for reported events with insufficient evidence, the criteria fulfilled to meet the case definition should be recorded.(17)7, and/or testing performed).Clinical description of signs and symptoms of neonatal seizures, seizure type (18)11, first observation12 and diagnosis13, duration and frequency of seizures (seizures/hour or seizures/day), last seizure14 and final outcome15.Date/time of onset(19)•Measurement/testing •Minimum EEG standards for cEEG are described in the American Clinical Neurophysiology Society (ACNS) guidelines •https://doi.org/10.1136/adc.2004.062745) https://www.acns.org/UserFiles/file/Guideline5-MinimumTechnicalStandardsforPediatricEEG_v1.pdf) Minimum aEEG standards are described by de Vries and Hellström-Westas •Results of electrolytes, blood gas, and serum glucose, calcium, magnesium, bilirubin as well as complete blood count and blood culture.•Other investigations depend on clinical presentation, history and availability and may include lumbar puncture, urine culture and toxicology , screen for relevant congenital infections, metabolic screen, and genetic testing.•Ultrasound and neuroimaging (MRI or CT scan) if available.Concurrent signs, symptoms, and diseases.(20)Treatment given for neonatal seizures, especially specify drug(s) and dosing.(21)15 at last observation. Persistence beyond the neonatal period should be noted, ideally as late as 12–18 months.Outcome(22)16.Objective clinical evidence supporting classification of the event as “serious” according to regulatory standards(23)Maternal and infant exposures other than the maternal immunization, including those 24 h before and after immunization, and until delivery  considered potentially relevant to the reported event.Specifically document:3.1.5The duration of surveillance for neonatal seizures should be predefined based on the neonatal period (see case definition – up to 28 days in term and up to 44 PMA in preterm infants). Events with onset of seizures after this time are not considered neonatal seizures although it is recognized that seizures may persist (onset of epilepsy).(24)The duration of follow-up reported during the surveillance period should be predefined likewise. It should aim to continue to resolution of the event.(25)Methods of data collection should be consistent within and between study groups, if applicable.(26)Follow-up of cases should attempt to verify and complete the information collected as outlined in data collection guidelines 1–23.(27)Investigators of patients with neonatal seizures should provide guidance to reporters to optimize the quality and completeness of the information provided.(28)Reports of neonatal seizures should be collected throughout the study period regardless of the time elapsed between maternal or infant immunization and the adverse event. If this is not feasible due to the study design, the study periods during which safety data are being collected should be clearly defined.Biologic characteristics of the vaccine (e.g. live attenuated versus inactivated component vaccines), biologic characteristics of the vaccine-targeted disease, biologic characteristics of the vaccinee  are not considered relevant for the choice of the duration of the surveillance for neonatal seizures.3.2(29)Reported events should be classified in one of the following five categories including the three levels of diagnostic certainty. Events that meet the case definition should be classified according to the levels of diagnostic certainty as specified in the case definition. Events that do not meet the case definition should be classified in the additional categories for analysis.Event classification in 5 categories17Event meets case definitionLevel 1: Criteria as specified in the neonatal seizures case definitionLevel 2: Criteria as specified in the neonatal seizures case definitionLevel 3: Criteria as specified in the neonatal seizures case definitionEvent does not meet case definitionAdditional categories for analysis18Level 4: Reported neonatal seizures with insufficient evidence to meet the case definition19Level 5: Not a case of neonatal seizures(30)11 of the first symptoms and/or signs consistent with the definition. Additionally, the occurrence of neonatal seizures in relation to the infant’s date of birth should be reported. If few cases are reported, the specific time course could be analyzed for each; for a large number of cases, data can be analyzed in the increments based on trimester of maternal immunization 14 and/or final outcome15. If seizures persist beyond the neonatal period, this has to be noted. Whatever start and end are used, they should be used consistently within and across study groups.The period of occurrence is defined as the interval between the date of onset of the first seizure consistent with the definition and the last seizure(32)If more than one measurement of a particular criterion is taken and recorded, the value corresponding to the greatest magnitude of the adverse experience could be used as the basis for analysis. Analysis may also include other characteristics like qualitative patterns of criteria defining the event.(33)The distribution of data (as numerator and denominator data) could be analyzed in predefined increments , where applicable. Increments specified above should be used. When only a small number of cases are presented, the respective values or time course can be presented individually.(34)Data on neonatal seizures obtained from subjects born to mothers receiving a vaccine should be compared with those obtained from an appropriately selected and documented control group(s) to assess background rates of neonatal seizures in non-exposed populations and should be analyzed by study arm and dose where possible, e.g. in prospective clinical trials.Furthermore, it is useful to analyze time of onset of seizure because some etiologies have a definite time of onset. For preterm infants the age of onset is recorded as the corrected age and chronological age b.(31)The3.3(35)All reported events of neonatal seizures should be presented according to the categories listed in guideline 29 or other classification that is considered appropriate.(36)Data on possible neonatal seizures events should be presented in accordance with data collection guidelines 1–23 and data analysis guidelines 29–34.(37)Terms to describe neonatal seizures such as “low-grade”, “mild”, “moderate”, “high”, “severe” or “significant” are highly subjective, prone to wide interpretation, and should be avoided, unless clearly defined.(38)Data should be presented with numerator and denominator (n/N) (and not only in percentages), if available.(39)Although denominator data are usually not readily available for immunization safety surveillance, attempts should be made to identify approximate denominators. The source of the denominator data should be reported, and calculations of estimates be described . The incidence of cases in the study population should be presented and clearly identified as such in the text.(40)If the distribution of data is skewed, median and range are usually the more appropriate statistical descriptors than a mean. However, the mean and standard deviation should also be provided.(41)•The study design;•The method, frequency and duration of monitoring for neonatal seizures;•The trial profile, indicating participant flow during a study including drop-outs and withdrawals to indicate the size and nature of the respective groups under investigation;•The type of surveillance (e.g. passive or active surveillance);•The characteristics of the surveillance system ;•The search strategy in surveillance databases;•Comparison group(s), if used for analysis;•The instrument of data collection ;•Whether the day of maternal immunization was considered “day one” or “day zero” in the analysis;•2 and/or the date of first observation3 and/or the date of diagnosis4 was used for analysis; andWhether the date of onset•20.Use of this case definition for neonatal seizures, in the abstract or methods section of a publicationAny publication of data on neonatal seizures after maternal immunization should include a detailed description of the methods used for data collection and analysis as possible. It is essential to specify:These guidelines represent a desirable standard for the presentation and publication of data on neonatal seizures following maternal immunization to allow for comparability of data and are recommended as an addition to data presented for the specific study question and setting. Additionally, it is recommended to refer to existing general guidelines for the presentation and publication of randomized controlled trials, systematic reviews, and meta-analyses of observational studies in epidemiology  Notes for guidelines10If the reporting center is different from the vaccinating center, appropriate and timely communication of the adverse event should occur.11The date and/or time of onset is defined as the time within the neonatal period when the first sign or symptom indicative of neonatal seizures occurred. This may only be possible to determine in retrospect.12The date and/or time of first observation of the first sign or symptom indicative for neonatal seizures can be used if date/time of onset is not known.13The date of diagnosis of an episode is the day within the neonatal period when the event met the case definition at any level.14The end of the occurrence of neonatal seizures is defined as the time the subject no longer meets the case definition at the lowest level of the definition.15E.g. recovery to pre-event immunization health status, spontaneous resolution, therapeutic intervention, persistence of the event, sequelae, death.16An adverse event after immunization (AEFI) is defined as serious by international standards 17To determine the appropriate category, the user should first establish, whether a reported event meets the criteria for the lowest applicable level of diagnostic certainty, e.g. Level three. If the lowest applicable level of diagnostic certainty of the definition is met, and there is evidence that the criteria of the next higher level of diagnostic certainty are met, the event should be classified in the next category. This approach should be continued until the highest level of diagnostic certainty for a given event could be determined. If the lowest level of the case definition is not met, it should be ruled out that any of the higher levels of diagnostic certainty are met and the event should be classified in categories four or five. The highest possible level of classification should be recorded for each event.18If the evidence available for an event is insufficient because information is missing, such an event should be categorized as “Reported neonatal seizures with insufficient evidence to meet the case definition”.19An event does not meet the case definition if investigation reveals a negative finding of a necessary criterion (necessary condition) for diagnosis. Such an event should be rejected and classified as “Not a case of neonatal seizures”.20Use of this document should preferably be referenced by referring to the respective link on the Brighton Collaboration website (http://www.brightoncollaboration.org).4The findings, opinions and assertions contained in this consensus document are those of the individual scientific professional members of the working group. They do not necessarily represent the official positions of each participant’s organization . Specifically, the findings and conclusions in this paper are those of the authors and do not necessarily represent the views of their respective institutions.The authors declared that there is no conflict of interest.


Generally, sub-Saharan countries possess abundant energy resources including renewables and fossil sources, with natural gas potentially being among the more abundant resource second only to solar power. For conventional electrical energy generation, gas turbines are one of the most prominent technologies being adopted in producing electricity from natural gas. Nigeria, for instance has the largest natural gas reserves in Africa, and the 9th largest in the World. Thus, more than 80% of her electricity generation utilizes gas turbines. To effectively monitor the state of these gas turbines, several sensors are located on the turbines to acquire data in real time. In this data article, we present the acquired data from a 5.68-MW gas turbine installed as an independent power producing unit in a community in Ogun State, Nigeria over a period of six months. Performing various descriptive analysis on the dataset, the real power measurements were taken as the target parameters, and based on a threshold correlation co-efficient of 0.5, only sixteen (16) parameters were shown to be more closely positively correlated with the real power measurements. Thus, any variation in the real power supplied by the gas turbine would have a commensurate effect on any of the other 16 parameters identified, and could thus help in troubleshooting or scheduling maintenance. A total of 50 features were recorded by the Turbomach Turbotronic 4 SCADA application running on a core i5 2.40 GHz workstation with 1TB of hard-disk space and 4GB RAM. The Turbomach Turbotronic 4 application monitored the gas turbine in real time over a 24-h time period (H1 to H24) and at times when the turbine was shut down due to gas constraints or scheduled maintenance, no values were recorded for the total 50 parameters monitored. The data was recorded by various temperature and pressure sensors installed at various points on the turbine during operation. The data recorded by each sensor changed with every change in load demand and ambient temperature. A transmitter transfers the measured data to a remote Human-Machine Interface (HMI) in the control room via an ethernet cable and the displayed data is collated hourly from the HMI. The complete R markdown code utilized in running the descriptive analysis on the raw turbine dataset is shown in Ref.


To satisfy an increasing demand for dietary protein, the poultry industry has employed genetic selection to increase the growth rate of broilers by over 400% in the past 50 years. Although modern broilers reach a marketable weight of ~ 2 kg in a short span of 35 days, a speed twice as fast as a broiler 50 years ago, the expedited growth has been associated with several negative detrimental consequences. Aside from heart and musculoskeletal problems, which are direct consequences of additional weight, the immune response is also thought to be altered in modern broilers.Given that identifying the underlying genetic basis responsible for a less sensitive innate immune response would be economically beneficial for poultry breeding, we decided to compare the genomes of two unselected meat control strains that are representative of broilers from 1957 and 1978, and a current commercial broiler line. Through analysis of genetic variants, we developed a custom prioritization strategy to identify genes and pathways that have accumulated genetic changes and are biologically relevant to immune response and growth performance. Our results highlight two genes, TLR3 and PLIN3, with genetic variants that are predicted to enhance growth performance at the expense of immune function.Placing these new genomes in the context of other chicken lines, reveal genetic changes that have specifically arisen in selective breeding programs that were implemented in the last 50 years. Clostridium perfringens. Global losses due to NE are estimated to be $2B/year and they are expected to rise 1, while the Silkie line, was originally bred for ornamental purposes in the 19th century2. Of note, the RJFswe line did not group with the RJF line, suggesting that pooling of samples from two sources may have impacted variant calls in this dataset.To further examine the potential impact of sequencing platform on variant calls, we performed a phylogenetic analysis on the variants common to lines sequenced by SOLiD and Illumina platforms Fig. . ReassurFocusing specifically on the three broiler lines , we found that while each line possessed ~ 1.7–1.8 million SNPs not present in the other two lines, they share a common core of ~ 3.9 million SNPs. Amongst this core were 14,686 non-synonymous SNPs, together with 1796 frameshifts and 80 stop gains – variants that were likely introduced in the common ancestor of the three lines. Further, we found that the 1978 and Ross308 genomes share more SNPs  than the 1957 and the Ross308 genomes . In addition to analyzing single-nucleotide polymorphisms (SNPs), we also compared copy number variants . CNVs that encompass coding regions have the potential to alter gene expression , with a Notable genes associated with CNVs include two copies of CD8alpha in Ross308, two copies of tumor necrosis factor superfamily member 13b (TNFS13B) in both 1978 and Ross308, and the loss of one copy of Apolipoprotein-AII (APO-AII) in both 1978 and Ross308. CD8alpha is a co-receptor on CD8+ T cells, a specialized group of T-cells that recognizes antigens presented by MHC class I molecules, then respond to damaged cells by triggering production of cytokines and the release of cytotoxic molecules . RelatedSNP density as the number of mutations divided by the coding sequence length. We then constructed a set of SNP dense genes as the 10% of genes displaying the greatest SNP density (and hence most likely impacted by genetic variation) and performed a gene set enrichment analysis for enriched functional categories as defined through GO and pathways defined by the Kyoto Encyclopedia of Genes and Genomes  defines transcripts that have accumulated the greatest number of SNPs over the past 60 years. Here we compared increases in non-synonymous SNP density between the 1957 and the 1978 lines, and the 1978 and Ross308 lines. Under this strategy, we select transcripts that are in the 95th percentile in terms of increase in SNP density either for the 1957/1978 comparison or the 1978/Ross308 comparison. We additionally specify that both changes in SNP density must be positive (i.e. there are more SNPs in more recently bred lines). In Strategy 2 we define transcripts based on unique SNPs in faster-growing lines. Transcripts are selected based on possessing the highest (90th percentile) density of SNPs that are unique to the 1978 and/or Ross308 lines relative to all other lines. Strategy 3 focuses on transcripts that possess high impact variants  restricted to either the 1978 or Ross308 lines, but not the 1957 line. Similarly, Strategy 4 identifies transcripts based on decreasing scores from application of the Sorting Intolerant From Tolerant  algorithm [Strategy 5) we identify transcripts that have accumulated deleterious SNPs. Here we select for transcripts that show an increase in the number of deleterious variants (SNPs with SIFT scores less than 0.05) when comparing the 1957, 1978, and Ross308 broilers and, additionally, possess the highest (99th percentile) density of deleterious variants.The first strategy (lgorithm . Under t). This final filtering step ensures the selection of transcripts with SNPs that result in functional consequences (use of only one or two strategies might result in the selection of transcripts that have simply accumulated many low-impact variants) and identified a total of 78 transcripts. From this list, manual reviews of GO and domain annotations allowed us to identify 12 transcripts with mammalian homologs involved in immune response and/or promoting growth, that have likely been compromised in the selection of the 1978 and Ross308 lines . After manual review of the potential functional impact of their associated SNPs, we identified TLR3 and a putative homolog of mammalian PLIN3 (ENSGALT00000003248), for more focused analysis.Applying each strategy resulted in the generation of five complementary sets of transcripts Fig. . Strateg3TLR3 is part of the Toll-like receptor family that plays a crucial role in innate immunity by recognizing double stranded viral RNA and promoting anti-viral defenses in other cells . In the ENSGALT00000003248, a PLIN3 homolog, is the only gene that was identified by all five strategies; of the ten SNPs found in the Ross308 line, five were unique, including the deleterious variant G199R and a stop codon W298X predicted to cause premature termination  for a long period of time in response to the same stimulus. Thus, we may expect production traits are interlinked with immune traits. To address this question, we developed a selection strategy to identify genes that have acquired variants over the course of selective breeding programs, impacting their function. Our initial expectation is that this strategy would result in the identification of genes that contribute to both immune and growth-related processes.Our combinatorial strategy incorporates three considerations neglected by other studies. First, different genes could experience different levels of selective constraints which has been observed in domestication of cattle  and maizSalmonella enterica serovar Typhimurium and Escherichia coli, is sufficient to induce anti-inflammatory signals via TLR3. Another study that used a high-density SNP array to investigate polymorphisms in a commercial broiler line also found that TLR3 falls within a region of positive selection [It should be appreciated that rather than sampling across each population, only a single individual from each line was sequenced. Consequently, given their heterogeneous genetic backgrounds, we expect that the variants identified here may only represent a small fraction of those that distinguish the three lines. Nevertheless, we are encouraged by the enrichment of variants in genes associated with immune functions, with many of the genes that passed our final filter (prioritized by more than 3 strategies) contributing to both immune response and growth Figs.  and 3. Telection . This raPLIN3 belongs to the perilipin family of proteins that associate with lipid droplets to regulate lipase access to lipid storage. With known roles in exercise-induced lipid degradation  and lipoSalmonella revealed the fast-growing chickens relied on T-cell activation and were slower at clearing S. enterica serovar Enteritidis infection than slow growing chickens, which induced macrophage activation post infection [In addition to identifying genes of interest, we observed that all three broilers that we sequenced are over-represented in SNP dense genes from several functional categories such as phagosome, cytokine-cytokine receptor interaction, and intestinal immune network for IgA production. That suggests that commercial broiler immune response, irrespective of lineage, is distinct from the Red Jungle Fowl immune function. The Ross308 line, however, is the only one of the three broilers that we sequenced with SNP dense genes enriched in the GO term: “Defense response to other organisms” (GO:0098542). This result is supported by previous studies; one study found that commercial broilers had a lower febrile response (ability to generate fever) and lower cytokine expression in response to gram-negative bacteria than commercial layers . Comparinfection . TherefoTogether this study illustrates how comparisons of the genome sequences derived from temporally related lines can help reveal the genes and biological processes that help drive livestock breeding programs.The genomes of two female chickens from each of the University of Alberta Meat Control strains unselected since 1957 and 1978, respectively, and a commercial Ross 308 broiler  were generated as follows:Genomic DNA was isolated from 30 ul of chicken blood samples using the Promega Wizard Genomic DNA Purification Kit  according to the manufacturer’s instructions. Genomic DNA was quantified with a Qubit 2.0 fluorometer using the Qubit dsDNA BR assay kit  and diluted in buffer EB to the 20 ng/ul input for library preparation. DNA samples were also assessed for quality by gel electrophoresis. Library preparation was performed following the Illumina Truseq DNA PCR-Free LT Library Prep Kit  protocol using 1μg of isolated genomic DNA. The input DNA was sheared to 350 bp using Covaris microTUBES and the S2 Ultrasonicator . End repair, A-tailing, adaptor ligation and library nomalization were performed according to Illumina’s instructions. Libraries were quantified for normalization by qPCR using the KAPA Library Quantification Kit for Illumina sequencing platforms . The final libraries were also assessed using the DNA1000 kit for the Bioanalyzer 2100 . The final libraries were denatured and diluted to a final loading volume of 1.3 ml at a concentration of 1.8pM. Sequencing was performed on the NextSeq500 sequencer , according to the manufacturer’s instructions, using the 300 cycle High Output V2 sequencing kit and a spike-in of 8% PhiX control library, generating 150 bp paired end reads. Sequence data is available through the sequence repository archive, hosted by the NCBI, under BioProject PRJNA561435.Additional genomes were downloaded from the sequence read archive (SRA) at the National Center for Biotechnology Information (NCBI)  and inclGallus gallus reference genome  removed duplicates in the alignment. Genome analysis toolkit (GATK) [For lines sequenced using the Illumina platform , adapters and low quality reads were identified and filtered using FastQC  and Trimry, 2016 ) with BWry, 2016  using det (GATK)  was usedPutative copy number variants (CNVs) of the 1957, 1978, and Ross 308 genomes were detected using CNVnator    convertevcf-isec applied to compare broiler variants to the merged file to identify unique variants. SNP density for each gene was defined as the number of SNPs normalized by gene length, global SIFT score was defined as the average SIFT score of all variants in a gene normalized by gene length, and number of variants predicted to be deleterious for each gene. Ortholog [SNPs were annotated with both ANNOVAR (v2016-02-01)  and VariOrtholog , Gene OnOrtholog , and funOrtholog  and UnipOrtholog .p-value < 0.05 after Benjamini-Hochberg false discovery rate [Genes with SNP densities in the 90th percentile in each breed were subject to pathway enrichment analysis based on Gene Ontology (GO)  and Kyotery rate  correctiBased on SNP analyses described above, we defined five prioritization strategies to define transcripts of interest Fig. :Strategy 1. Transcripts that have accumulated most SNPs in the last 50 years. Nonsynonymous SNP density increase between the 1957 and the 1978 broiler was compared to the nonsynonymous SNP density increase between the 1978 and Ross 308 broiler. For a transcript to be prioritized by this strategy, the difference in SNP density between the 1957 and 1978 broiler OR between the 1978 broiler and Ross 308 must be in the 95th percentile. Additionally, all SNP density changes between 1957 and 1978 and 1978 and Ross 308 must be positive (i.e. more SNPs in more recently bred lines).Strategy 2. Transcripts with the most SNPs unique to fast-growing broilers. This strategy selected for transcripts with the highest number of variants  that belonged exclusively to the 1978 and/or Ross 308 subset OR the subset of SNPs exclusive only to Ross 308. The former subset (transcripts with high number of unique variants in 1978 and Ross 308 combined) includes transcripts that have not gained unique variants between 1978 and Ross 308 but have accumulated unique variants since the 1950s.Strategy 3. Transcripts containing high impact variants  only in Ross 308 or the 1978 broiler.Strategy 4. Transcripts with decreasing SIFT score. For every transcript, the mean SIFT score is calculated, only transcripts that show a decrease in SIFT score when comparing the 1957, 1978, and Ross 308 broilers and are also in the bottom 50th percentile for either the 1978 or Ross 308 broiler are selected.Strategy 5. Transcripts that have accumulated deleterious SNPs. Transcripts that show an increase in number of deleterious variants (SNPs with SIFT scores less than 0.05) when comparing the 1957, 1978, and Ross 308 broilers and possess the highest number of deleterious variants (99th percentile) after normalizing by transcript length.Additional file 1: Table S1. Breeds and Naming Convention for the 15 Samples Analyzed in this Study. The main text uses the abbreviation to refer to each breed. Genomes sequenced from pooled blood samples are specified in brackets in the “Name” column.


Global agriculture is under pressure to meet increasing demand for food and agricultural products. There are several global assessments of crop yields, but we know little about the uncertainties of their key findings, as the assessments are driven by the single best yield dataset available when each assessment was conducted. Recently, two different spatially explicit, global, historical yield datasets, one based on agricultural census and the other largely based on satellite remote sensing, became available. Using these datasets, we compare the similarities and differences in global yield gaps, trend patterns, growth rates and changes in year-to-year variability. We analyzed maize, rice, wheat and soybean for the period of 1981 to 2008 at four resolutions . Although estimates varied by dataset and resolution, the global mean annual growth rates of 1.7–1.8%, 1.5–1.7%, 1.1–1.3% and 1.4–1.6% for maize, rice, wheat and soybean, respectively, are not on track to double crop production by 2050. Potential production increases that can be attributed to closing yield gaps estimated from the satellite-based dataset are almost twice those estimated from the census-based dataset. Detected yield variability changes in rice and wheat are sensitive to the choice of dataset and resolution, but they are relatively robust for maize and soybean. Estimates of yield gaps and variability changes are more uncertain than those of yield trend patterns and growth rates. These tendencies are consistent across crops. Efforts to reduce uncertainties are required to gain a better understanding of historical change and crop production potential to better inform agricultural policies and investments. The demand for crops is anticipated to double by 2050, putting more pressure on global agriculture to achieve the production goal . ClimateFor earlier assessments, either of two different global, historical yield datasets is utilized. One is compiled by Ray et al.   to another. In contrast, the I14 dataset is thought to have more “consistent” spatial representativeness of yields across grid cells owing to the use of satellite data. However, grid-cell yields in the I14 dataset are modeled data and those in minor crop-producing regions could be less reliable than in major crop-producing regions due to the inherent limitations of satellite remote sensing in capturing crop status in areas where crop is sparsely grown. The resolution of the I14 dataset is higher than that of the R12 dataset when the R12 dataset uses national census data, although the quality of grid-cell yield data of the I14 dataset varies by the extent of cropland within a grid cell. Therefore, a consistent result across the two datasets improves the reliability of assessment results.It is therefore useful to address whether findings of earlier studies derived from one single dataset are robust when another dataset is analyzed. Such a practice improves our understanding of uncertainties in estimated potentials and changes in global yields. Furthermore, the difference in spatial resolution is an important aspect that differentiates the datasets. It is well documented that use of yield datasets with different resolutions often leads to different conclusions , 11. We Two yield datasets–the R12 dataset  and an uYield gaps were estimated according to the method of Mueller et al. . Yield tYield gaps are estimated as a percentage of attainable yield achieved circa the year 2000 1997–2003). Attainable yields are assumed as the upper limit of yields likely achievable using current agronomic technology and management. They are more conservative than biophysical limits of yields and are determined by identifying high-yielding areas within bins of similar climate [. Attainath percentile of the data was used as the attainable yield for a given climate bin. This calculation was repeated from one climate bin to another to complete all climate bins in which a crop of interest is harvested. The calculated attainable yields at a resolution of 0.083° were aggregated to coarser resolutions using the harvested areas in 2000 [We recalculated the attainable yields for the R12 and I14 datasets because they seemed to be sensitive to the yield data used for the calculation, and the attainable yields used in Mueller et al.  were cal in 2000  as the wRay et al.  categoriThe distinction between these yield trend patterns is guided by the type of regression model selected. Although we followed the method described by Ray et al. , our anaThe percentage rates of average yield growth for the past 20 years (1989–2008) were calculated by fitting a linear regression curve to yield a time series over that period, as was done by Ray et al. , for eacAlthough an earlier study  used comFor the four crops, the major geographical patterns of average yields (represented by a percentage of attainable yield) calculated using the R12 and I14 datasets showed a close match with the references , 14 and th percentile of the grid-cell yields per climate bin) calculated using the I14 dataset were thus far larger than those calculated using the R12 dataset. As a result, the percentages of average yields for the globes and major producers, relative to attainable yields, estimated using the I14 dataset were almost always smaller than those estimated using the R12 dataset  were comparable to those of soybean (1.5% yr-1), whereas rice yields increased more rapidly (1.7% yr-1) than soybean yields (1.5–1.6% yr-1) in the I14 dataset regardless of the resolution. These tendencies were different from that given in the reference [The uncertainties in yield growth rates associated with different datasets and resolutions were small. The major geographical patterns of yield growth rates derived from the datasets were similar . Among toybean 1.% yr-1, wFor both datasets, our estimates of yield growth rate were always more rapid than those of earlier work  Table 3Table 3. -1 in the R12 dataset and decreased at a rate of 0.005%SD yr-1 in the I14 dataset at 0.083° resolution). Such contrasts across the datasets appeared for maize in the northwestern part of Mexico, soybean in the southeastern part of the US and rice in the northeastern part of China Click here for additional data file.


Facial shape is the basis for facial recognition and categorization. Facial features reflect the underlying geometry of the skeletal structures. Here, we reveal that cartilaginous nasal capsule (corresponding to upper jaw and face) is shaped by signals generated by neural structures: brain and olfactory epithelium. Brain-derived Sonic Hedgehog (SHH) enables the induction of nasal septum and posterior nasal capsule, whereas the formation of a capsule roof is controlled by signals from the olfactory epithelium. Unexpectedly, the cartilage of the nasal capsule turned out to be important for shaping membranous facial bones during development. This suggests that conserved neurosensory structures could benefit from protection and have evolved signals inducing cranial cartilages encasing them. Experiments with mutant mice revealed that the genomic regulatory regions controlling production of SHH in the nervous system contribute to facial cartilage morphogenesis, which might be a mechanism responsible for the adaptive evolution of animal faces and snouts. The shape of a face strongly depends on the geometry of skeletal elements directly under the skin, adipose tissue and muscles. Our adult cranial and, in particular, facial skeleton consists mostly of bony elements. Cartilaginous parts are rather minor. However, during embryonic development bone forms after the cartilage, and the initial phases of facial and skull shaping proceed with the cartilaginous skeleton only. The entire functional and evolutionary meaning of the chondrocranium, that is, the early cartilaginous elements of the embryonic skull, is not clear. Some parts of the chondrocranium will undergo endochondral ossification . However, the majority of the bones, especially in a facial compartment, will form in a close spatial association with the chondrocranium independently through dermal membranous ossifications . Many quFgfr3 mutations suggest that a correctly shaped chondrocranium is essential for proper facial bone geometry and general facial outgrowth. However, Fgfr3 is expressed also at membranous ossification sites (please see Fgfr3(mRNA) expression at E15, http://developingmouse.brain-map.org/), as well as in sutural osteogenic fronts  develops from 14 pairs of independently induced large cartilaginous elements that fuse together during later development . SculptiThe frontonasal prominence and other facial regions are enriched in signaling systems. Activity in these systems leads to progressive induction and shaping of craniofacial structures, including chondrogenic mesenchymal condensations that turn into cartilage . The sigShh in the forebrain turned out to be important for the correct formation of FEZ and early steps of facial shaping in general Cjt/J) model, and found that Shh is expressed in very discrete regions of the face between E11.5 and E14.5 at the time of induction of facial cartilages  followed by micro-CT scanning. Chelation of tungsten is uneven in various cell types and creates reliable contrast highlighting different tissues.Shh in the brain to test its role in facial cartilage induction. For this, we took advantage of floxed/floxedNkx2.2-Cre/Shh  animals to delete Shh in the floor plate cells since the beginning of central nervous system (CNS) development and patterning. We analyzed two different stages: E12.5 as a stage of condensations of cartilaginous mesenchyme   . It is c as FEZ) . However as FEZ) .foramina transversariae)  . Notablyrsariae) . Similar animals .Shh expression in the forming olfactory epithelium and tested the role of the forming olfactory epithelium and olfactory neurons in the process of nasal capsule induction. The possibility that olfactory epithelium controls cartilage shaping is supported by the fact that the conchae of nasal labyrinth geometrically correlate with the folded structure of the olfactory epithelium, which they mechanically support.Next, we determined the Shh from the olfactory epithelium does not exist. To define the importance of any signal derived from the olfactory epithelium we utilized Six1 and Six4 double knockout to specifically ablate the development of the olfactory epithelium . This suggests that solid cartilage elements in the forming face depend on joint activities of multiple regulatory zones (sources of SHH) during their induction and shaping.This experiment provided a complementary, non-overlapping phenotype to the one with a nasal capsule resulting from the excision of Ascl1 (Mash1) knock-out embryos  for other components of the Hedgehog pathway (including Ptch1(mRNA), Ptch2(mRNA), Gli1(mRNA), Gli2(mRNA)) showed no enrichment within potential and actual chondrogenic areas , in which SHH signaling is constitutively reduced  with impacts on the distal regulatory landscape of Shh (Shh coding region and mark 6b (INV(−500 C1), demonstrated selectively localized mild defects in the nasal septum and also in the tips of Meckel’s cartilage, but not in the roof of nasal capsule. This septum-specific phenotype appeared more pronounced in a homozygous INV(−500 C1) as compared to the heterozygous genotype. Interestingly, this allele does not change the relative position to Shh of most known enhancers that could be responsible for SHH-dependent facial skeletal development. The phenotype observed in this experiment showed that septal cartilage is sensitive to minor changes in SHH patterning signals that result from either removal of regulatory sequences distal to the C1 position, or indirect effects affecting distant enhancer-promoter- communications.Tissue-specific expression of cterized . To modue of Shh . We paide of Shh . Current mark 6b  , which removes additional enhancers required for ventral forebrain expression of Shh in E10.5 embryos , and displacement of major regulatory elements at a position which prevent their interaction with Shh (Shh expression is much reduced in the forebrain of mutant embryos at E10.5 as evident from the results of in situ hybridization for Shh (Such strong phenotypes are most likely due to disruption of the with Shh . Accordi for Shh .INV(6b-C2) did cause minor defects in septum, while the same inversion over a full Shh deficiency (DEL(−90 C2) led to a strong holoprosencephaly similar to HypoShh  x DEL(−90 C2), the nasal septum did not develop at all, and the rest of the nasal capsule (roof) was present although severely disturbed in shape. The gradual increase in severity of the malformations from INV(−500 C1) to INV(6b-C2) may indicate the presence of several distinct enhancers related to face morphology distal to position 6b (which likely includes yet uncharacterized enhancers). It could also mean that the efficiency of the interactions of the known enhancers (SBE2-4) with the Shh promoter is modulated by the presence of distal elements, which may contribute to organize the Shh TAD  showed a powerful phenotype similar to the homozygous INV(6b-C2). This, in a combination with previous phenotypes, reveals that the key regulatory regions essential for the facial cartilage patterning are located between marks 6b and C1. These regulatory regions are likely SBEs since the LacZ expression disappears from the forebrain of reporter embryos at E12.5 according to previous results (DEL(C1-Z) did not show any gross abnormalities in the nasal capsule cartilage. Embryos carrying this mutation showed abnormal Meckel’s cartilage and a generally affected mandible. results . DEL seemingly controls discrete steps of chondrocranial patterning, which may represent a key evolutionary mechanism shaping animal snouts and faces.+/-/R26DTA+Col2-CreERT2 embryos) result in significant changes of adjacent membranous bones and facial shape. In turn, the facial cartilage geometry is controlled by the signals coming from neurosensory structures such as brain and olfactory epithelium. Altogether, these results might provide a new explanatory framework revealing how the integrated development of neural and non-neural tissues results in the precise and evolutionary adapted shape of the bony cranium and corresponding facial appearance.In addition to the obvious functional aspects, facial shape is important in many ways. For example, recognition of individual facial features assists social interaction and affects numerous important aspects of our everyday life . PatholoShh in that area by non-inducible Nkx2.2-Cre causes a highly selective loss of the nasal septum cartilage. The soft non-cartilaginous tissue of the nasal septum, however, remains intact, rendering this part of the phenotype highly cartilage-specific. The roof of the nasal capsule stays nearly unaffected. The analysis at E12.5 stage suggested that this phenotype must be related to pre-chondrogenic, early SHH-dependent patterning signals enabling cartilage formation.Previously, Marcucio and Hallgrimsson laboratories demonstrated the co-variation of brain and face as well as the impact of brain-emitted SHH on avian facial development . Yet, thHypoShh embryos, which demonstrated a profound phenotype in the nasal capsule cartilage. This was in contrast to wild type embryos treated with the SHH-inhibitor Vismodegib strictly between chondrogenic stages (E11.5-E13.5). These embryos showed no significant phenotype in the nasal capsule cartilage. Correspondingly, lineage tracing with Gli1-CreERT2 and expression analysis of SHH pathways members starting from E11.5 did not show any association with development of facial cartilages.This reasoning is further supported by the line of experiments involving ‘early on action’ GBS-GFP. We also found high expression levels of SHH pathway members, including numerous traced progeny in Gli1-CreERT2/R26Tomato animals. As expected, in embryos treated with Vismodegib between E11.5-E12.5, the palate was almost missing, in contrast to contrary to the nasal capsule cartilage that stayed virtually intact. The presence of abnormal palate clefts in Vismodegib-treated and also in floxed/floxedNkx2.2-Cre/Shh  embryos agrees with previous findings, which have established a general role of SHH in the patterning of the face and in development of pathological midfacial clefts , Nkx3.2 shows distinct expression in a range of cranial cartilages or their future locations at pre-chondrogenic (E11.5) and chondrogenic (E13.5) stages. The knockout of Nkx3.1 and Nkx3.2 yields changes in the facial shape, as evident from Shh resulted in cartilage overproduction and similar segmentation defects , which causes a collapse of olfactory epithelium. Despite the specific loss of nasal capsule roof, other cartilages appeared intact in locations corresponding to the expression sites of Six1 and Six4 (including Meckel’s cartilage). The perfect match between the shape of cartilaginous olfactory turbines and the epithelium, as well as the coordinated time course of their development  does not interfere with shaping of the nasal capsule and confines the shaping role of presumably olfactory progenitors to the developmental period before their differentiation into the mature olfactory neurons. Alternatively, other cell types in the olfactory epithelium may play a cartilage-inducing role .During evolution of vertebrates, cartilages forming the neurocranium and the future upper jaw appear before Meckel’s cartilage attains a function of a lower jaw skeleton, and the animals acquire articulated hinged jaws . TherefoIn addition to the evolutionary aspect, the role of different neurosensory structures (mainly the brain) in coordinated cartilage induction may suggest a new connection between neurological and craniofacial symptoms in numerous genetic syndromes. Examples of such conditions are Williams syndrome, Down syndrome and others that are manifested by behavioral and morphological abnormalities in the central nervous system  and rev. This brShh in the craniofacial epithelial linings ), SBE3 and SBE4  These results are also consistent with the phenotype of Six1/Six4 double knockout embryos at E12.5 , and corroborate the notion of an early pre-cartilage onset of the phenotype.”“To check if SHH from the brain and presumably from the olfactory epithelium acts directly on facial mesenchyme inducing chondrogenic differentiation or during cartilage growth, we analyzed embryos carrying a SHH-activity reporter 2) Col2a-DTA ablation experimentsThe authors should define what they mean by "mild chondrocyte ablation". It would be helpful to have a more quantitative and temporal understanding of how many cells are killed after the E12.5 induction in the DTA treatment and at what stage they are killed (as the difference between E13.5 to E15.5 can be dramatic). This would provide us with a better understanding of the precise stages at which the supposed SHH signal is required.We agree and we followed the advice of the reviewers and defined the proportion of cartilage loss in presented nasal capsules from the DTA animals. We also performed new experiments  and analyzed them. For this, we quantified the surface and volume of nasal capsule cartilage in control and DTA embryos analyzed at E15.5 and E17.5. For E17.5, the loss of cartilage in DTA sample was mean 30,7% of the cartilage surface decrease at E15.5 and mean 35,2% at E17.5. The new results can be found in Figure 1 and Figure 1—figure supplement 1.The cartilage of the nasal capsule is fully set for the first time only at E15.5 , and, thus, in the new experiment with another scheme of treatment aimed at higher cartilage loss, we analyzed the nasal capsule at E15.5 and not at E17.5. The principal results were consistent with the original experiment and showed how bone development becomes affected. Despite the reduction of nasal capsule cartilage, the membranous bones were induced adjacently in a tight association with chondrocranial parts. Please see Figure 1P,Q,R.3) A timeline of the genetic perturbationsIt would be helpful to consolidate all the data with a timeline scheme depicting the steps in facial cartilage formation and place all the different genetic variants onto this scheme – this would need to include the time point at which each set of mutants is first have an effect. For example, the Nkx2.2 driver comes on much earlier than E12.5, and we know that there will be overall changes to the neuroepithelial tissues in this context. Similarly, the other mutants such as Six1/Six4 are going to have major pathological effects much earlier than the cartilage condensation phases. Therefore, it seems difficult to eliminate the possibility that there is an earlier effect of SHH loss  that depicts the timeline of all experiments and the beginning of effects. We cross-compare the phenotypes in Results and also in Discussion, so there is a clarity of when the action of mutation takes place and how different conditions are similar to each other or different.4) Shh expressing cells in the olfactory epitheliumThe experiment involving the ablation of the olfactory epithelium is difficult to interpret given that the whole tissue is being targeted. Thus it is possible that other signals coming from these cells are required for the proper patterning of the nasal capsule. Furthermore, Six1/Six4 are expressed in the neural crest lineage and the head mesenchyme at earlier stages of development and thus we cannot rule out that the effects observed are indeed cell autonomous. This should be addressed.Shh exclusively in the nasal placode is currently impossible. In the revised version of the manuscript, we discussed this issue in a more clear way since we totally agree that other signals derived from olfactory placode in principle can guide the development of nasal capsule roof.We agree and understand the reviewer’s concerns. This is indeed great advice, and we discussed this moment extensively. It might be good to mention that we never claimed that it is SHH from the olfactory epithelium that induces the nasal capsule roof. Targeting “The induction of such 3D-shapes is unlikely to be achieved by signals from a single site and might require more sources including spatially opposed brain and olfactory structures. […] This will require further investigations.”Six1/Six4 double knockout (E12.5) and also by exploring a single knockout of Six1 (Six4 knockout showed no phenotype). Here is the new text that we have in a revised version of the manuscript:We provided additionally new data by generating and checking the key early stage of Six1 and Six4 are expressed in different parts of early facial mesenchyme .”“In addition to the expression in olfactory placodes, Shh by Nkx2.2-Cre, the loss of the olfactory epithelium in Six1/Six4 double knockout mutants leads to the absence of the nasal capsule roof, while the nasal septum cartilage stays largely in place. […] Already at the earliest steps, they are laid down as highly complex 3D-geometries .”“Complementary to the ablation of 5) Enhancer experimentThe enhancer experiments are tantalizing but very preliminary. First, the authors should clarify where these enhancers were published and what expression domains they produce. Have they been published (I could not locate a reference)?This is clarified in the main text. The initial discovery of these genomic regulatory regions is described in a recent manuscript from Francois Spitz laboratory .Furthermore, they would have to show how enhancer deletions results in changes of SHH expression. Unless the authors bridge the enhancer experiments with the previous data showing regional regulation of the nasal capsule, this section seems disconnected from the rest of the manuscript.Shh enhancers influence facial cartilage development. For the most critical mutations of Shh regulatory regions, we either report Shh-LacZ reporter data showing the shift of Shh expression in the forebrain or we cite the data of this kind obtained and published previously. In any case, we decided to move all data about enhancers to the supplementary information in this manuscript because of its accessory function.We did our best to run more experiments and to show the plethora of the phenotypes that explain more clearly now how brain-specific Here are the major updated results also outlined in two supplementary figures :Shh is known to be controlled by multiple enhancers. Some, which may regulate Shh expression in the cranial region, have been characterized . […] Taken together, local tuning of SHH expression by various enhancers (including brain-specific regulatory elements) seemingly controls discrete steps of chondrocranial patterning, which may represent a key evolutionary mechanism shaping animal snouts and faces.”“Tissue-specific expression of 6) The Discussion does the data a great disservice. There is a wealth of information in these animal models that is glossed over, in part because not enough detail is given for us to understand the different mutants . Furthermore, because there is never a direct comparison between the different lines, it is difficult to interpret the data in total. I think this is crucial because effectively, the conclusions are drawn upon a series of correlations – if this is the bulk of the data, it will help the reader if all of the correlations are laid out in one unified vision at the end.We expanded on the direct comparisons between the relevant mutant lines and we also added a new whole figure outlining the phenotypes and timing of effects .Instead, the authors discuss some thoughts rationalising the evolution of the structures, which is rather a distraction from the findings. I don't see that it is necessary to make any evolutionary arguments; instead, I would like a bit more scholarship and context in the Discussion and some thought toward future directions.We agree and we expanded the discussion towards molecular mechanism of SHH action on chondrogenesis, as well as provided some additional lines about future directions. We also kept the evolutionary tilt in the Discussion text since we hope that our findings will be considered important for EvoDevo aspect of craniofacial research.


Fuchs Endothelial Corneal Dystrophy (FECD) is a late onset, autosomal dominant eye disease that can lead to loss of vision. Expansion of a CTG trinucleotide repeat in the third intron of the transcription factor 4 (TCF4) gene is highly associated with FECD. However, only about 75% of FECD patients in the northern European population possess an expansion of this repeat. The remaining FECD cases appear to be associated with variants in other genes. To better understand the pathophysiology of this disease, we compared gene expression profiles of corneal endothelium from FECD patients with an expanded trinucleotide repeat (RE+) to those that do not have a repeat expansion (RE-). Comparative analysis of these two cohorts showed widespread RNA mis-splicing in RE+, but not in RE- samples. Quantitatively, we identified 39 genes in which expression was significantly different between RE+ and RE- samples. Examination of the mutation profiles in the RE- samples did not find any mutations in genes previously associated with FECD, but did reveal one sample with a rare variant of laminin subunit gamma 1 (LAMC1) and three samples with rare variants in the gene coding for the mitochondrial protein peripheral-type benzodiazepine receptor-associated protein 1 (TSPOAP1). Fuchs endothelial corneal dystrophy (FECD) is a common, heritable degeneration of the corneal endothelium with considerable locus heterogeneity. Mutations in 7 genes  have been shown to be either causal or highly associated with the disease . A recenn RNA which sequesters critical RNA splicing factors and leads to widespread splicing changes  also segregated with disease in this family and would clearly influence the function of the affected allele. Additionally, the PLEKHF1 gene is known to bind with ZEB1, a well-characterized FECD gene. However, PLEKHF1 is expressed at very low levels in our RNASeq data from the corneal endothelium.Within this family, the TSPOAP1 variant was one of eleven rare variants that co-segregated with FECD . Of thesn and sequestered muscleblind protein further suggests a shared pathogenic mechanism.FECD is a debilitating eye disease with genetic association to multiple genes. Of these genes, an expansion of a CTG repeat sequence in the TCF4 gene is by far the most common genetic anomaly found in FECD patients in the United States , 11, 12.The 10 alternative splicing events identified in previous comparisons  between Reproducible quantitative changes in gene expression between RE+ and RE- corneal endothelium samples were identified for a set of 39 genes. These represent a set of genes that are differentially regulated by the repeat expansion in TCF4, but changes in the expression of these genes do not appear to be critical for the development of FECD. These data demonstrate that expansion of the CTG repeat in TCF4 leads to widespread changes in gene expression. While this dataset did not reveal any common pathway involvement, several genes can be found in the same pathway. For example, FRZB and SFRP4 are two modulators of Wnt signaling that are upregulated in RE+ samples. Further analysis of the complete dataset may provide novel insights into their molecular and physiological associations.The identification of rare variants in TSPOAP1, a gene exhibiting mis-splicing in both FECD and DM1, in three RE- patients from two separate families is also noteworthy. TSPOAP1 is a TSPO-binding protein, and it has also been shown to bind to TCF4. TSPO is thought to be important in the regulation of mitophagy, which is thought to be important in the pathogenesis of FECD –19. AlthAlthough the family data also revealed alternative variants that could be related to the pathogenesis of FECD, the segregation of the TSPOAP1 variants with disease in our family data is intriguing and suggests possibilities for common mechanisms between RE+ samples and at least a subset of RE- samples. Specifically, RE+ patients also produce significant amounts of a variant TSPOAP1 mRNA that codes for 280 fewer amino acids compared to the predominant form found in the corneal endothelium from RE- and control patients. Thus, the majority of all FECD patients in our population are likely to be producing a variant TSPOAP1 isoform as a result of the splicing changes that result from expansion of the CTG repeat in TCF4.Clearly the RE- group of FECD patients is heterogeneous, because there is considerable locus heterogeneity in the 20–25% of FECD patients in our population that are not RE+. Within the RE- group characterized by RNASeq studies here, we did not identify any samples with rare variants in well-established FECD genes  but we did find that one patient had a rare variant in LAMC1, which has been implicated as an FECD gene by a recent large GWAS effort .Of note, we did not identify expression of LOXHD1 or AGBL1 in any of our samples, whether RE+ or RE-. These genes have been implicated in FECD in prior studies, but this lack of expression in corneal endothelial tissue in adults brings into question whether genetic variants in these genes could have a functional role in the development of the disease.The limitations of this study include limited statistical power due to the analysis of small groups of samples and the heterogeneity of the RE- group. Additionally, due to consecutive sample collection and processing, batch #2 only had 1 RE- sample to perform analysis with. While this analysis appears limited, it is strengthened by the fact that we took data obtained from each batch and cross compared them to each other and only reported genes that were identified in 2 out of 3 comparisons and in 12 or more independent comparisons. This analysis plan would have eliminated most if not all of the false positives obtained within batch 2 due to a single RE- comparison. While the findings of differences in splice patterns and gene expression are likely to be significant, we cannot ensure that pathologically important differences were not missed. Resolving this limitation will depend not only on larger sample sizes but also in the further elucidation of the true causative genetic variants in the RE- group. Given the genetic mutations and implicated genes described in prior literature, the absence of those variants in this current RE- study group, and our finding of two novel variants, RE- FECD may very well represent a phenotype resulting from a very diverse source of genetic variants and pathways.The work presented here supports our previous conclusion that corneal endothelium from RE+ patients exhibit characteristic mRNA splicing events, and we now confirm that many of these abnormalities are not present in the corneal endothelium from RE- patients. These qualitative differences in gene expression are supplemented by quantitative differences in the expression of at least 39 genes. These findings suggest that there are real biological differences between RE+ FECD and RE- FECD corneas and lend support to a hypothesis that mis-splicing is a key pathogenic feature of RE+ FECD but not RE- FECD. Therefore, future efforts to identify genetic influences on the development of FECD should consider stratification of study populations according to repeat status. This should also apply to studies of the basic science of the disease, such as cell culture systems in which RE+ or RE- models may express differential biology and differential responses to interventions or therapeutics. In addition, we identified a novel candidate locus, TSPOAP1, and confirm that a rare variant in a recently described locus, LAMC1, was found in a subset of RE- FECD.


Escherichia coli to successfully complete its life-cycle by passing through the stomach to colonize the digestive tract. To develop a fundamental understanding of this response, we established a molecular mechanistic description of acid stress mitigation responses in E. coli and integrated them with a genome-scale model of its metabolism and macromolecular expression (ME-model). We considered three known mechanisms of acid stress mitigation: 1) change in membrane lipid fatty acid composition, 2) change in periplasmic protein stability over external pH and periplasmic chaperone protection mechanisms, and 3) change in the activities of membrane proteins. After integrating these mechanisms into an established ME-model, we could simulate their responses in the context of other cellular processes. We validated these simulations using RNA sequencing data obtained from five E. coli strains grown under external pH ranging from 5.5 to 7.0. We found: i) that for the differentially expressed genes accounted for in the ME-model, 80% of the upregulated genes were correctly predicted by the ME-model, and ii) that these genes are mainly involved in translation processes (45% of genes), membrane proteins and related processes (18% of genes), amino acid metabolism (12% of genes), and cofactor and prosthetic group biosynthesis (8% of genes). We also demonstrated several intervention strategies on acid tolerance that can be simulated by the ME-model. We thus established a quantitative framework that describes, on a genome-scale, the acid stress mitigation response of E. coli that has both scientific and practical uses.Response to acid stress is critical for  E. coli has important implications in the food, health care and biotechnology industries. The ability of E. coli to tolerate acid stress can be attributed to its various regulatory, metabolic and physiological mechanisms. Although different acid resistance mechanisms have been well characterized, few studies are focused on understanding how these mechanisms work together to protect E. coli from acid stress. A mathematical representation of the metabolic flux state and proteome allocation of E. coli allows the characterization of how these mechanisms interact and function on a systems-level. Here, based on an existing E. coli model framework, we characterize three acid stress mitigation responses of E. coli: 1) change in membrane lipid fatty acid composition, 2) change in periplasmic protein stability over external pH and periplasmic chaperone protection mechanisms, and 3) change in the membrane protein activities. The predictions of our framework with the integrated mechanisms demonstrated good agreement with RNA sequencing data of E. coli on gene expression changes under acid stress. The efforts here open up various opportunities for practical applications, e.g. intervention strategies that challenge acid stress tolerance and enhancement of acid resistance during organic acid production in a cell factory.Understanding the acid resistance mechanisms of  Escherichia coli to tolerate acid stress  and [D] are the concentrations of the oxidized and reduced forms of the electron-donating half reaction, [A] and [A−] are the concentrations of the oxidized and reduced form of the electron-accepting half reaction. Since we were only interested in the relative change of activity for the electron transport chain components, we focused on calculating the term after v+ in E° is calculated based on the standard redox potential of the half-reactions from multiple sources Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Based on an existing framework, this study characterizes the three acid stress mitigation responses of E. coli: 1) change in membrane lipid fatty acid composition, 2) change in periplasmic protein stability over external pH and periplasmic chaperone protection mechanisms, and 3) change in the activities of membrane proteins. The predictions by the ME model with the three integrated mechanisms demonstrated good agreement with RNA sequencing data of E. coli on gene expression changes under acid stress. It is valuable to mathematically reproduce genome-wide changes, but I have several comments.1) It is hard to understand how the authors integrate chaperone protection mechanisms. How did they estimate the HdeB amount necessary for folding acid-induced unfolded proteins at different pHs? Did they estimate or measure the total protein amounts in periplasm? While Eq.4 does not show any rate equations, how did they estimate the synthesis flux of HdeB at different pHs?2) More explanation on the ME model is required. Especially, how does it estimate macromolecule or chaperone synthesis flux? Does it consider transcriptional regulations (e.g. sigma factors)? Because readers like to know how chaperone synthesis regulation quantitatively affects the genome-scale metabolism.3) Did the authors evaluate or measure decreased activities of membrane proteins (e.g. transporters)? Membrane protein activity would decrease because the periplasm sides of the proteins are exposed to a low pH.4) The simulated cell growth decreased under acid stress. Is it caused by increased chaperone synthesis? Are there any other reasons for the decreased growth?5) In Fig 4A, the ATP synthesis rate was very high at pH 5. Can they explain how the produced ATP is used within a cell?6) The ATP synthesis flux  is critically important in this study, thus it seems necessary to experimentally measure the ATP synthesis flux.7) In Fig 5B, “Others” are correctly predicted by the ME model. However, “others” are meaningless. Can they specify the gene names or their functions?Reviewer #2: The authors extend their genome-scale model of E. coli metabolism to account for the effects of mildly acidic extracellular conditions. The model development is well explain and the comparisons to published data are promising. I have no major concerns about the study, but I do believe that the manuscript might be improved if the following comments are addressed:1. The author summary reads too much like the abstract and should be rewritten from a broader perspective.2. The five E. coli strains used should be mentioned earlier in the paper, like line 129.3. The authors should reconcile the statement on page 18 "We found the rates of electron transport chain components to be almost unchanged (<1%) from neutral pH to acidic pH. We were unable to calculate the reaction rates for most of the membrane transporters, due to missing metabolite concentration data. However, we found that the change of their activities had minimal impact on cellular growth rate and processes (<1%) through the sensitivity analysis using the ME-model" with the comment on page 20 "Additionally, we found upregulated expression for a number of proteins on the inner and outer membranes of E. coli. These proteins include the electron transport chain components,...".4. Is there some reason that the DEGs were not analyzed for false negatives ? See lines 316-319.5. The reason for limiting the modeled pH range from 5.5-7.0 should be mentioned earlier in the paper. See lines 416-431.Reviewer #3: In the present manuscript, Du et al aim to get a systems-level understanding of the acid stress response of E. coli. Towards this end, the authors incorporate three known acid resistance mechanisms into a previously developed genome-scale metabolic and expression (ME) model of E. coli. In a first, step, they consider these mechanisms  separately and show that their model recapitulates previously reported behavior. For example, the authors predict that an acid-adapted fatty acid profile is accompanied by a reduction in growth rate, as is also observed experimentally. Finally, the authors combine all three acid resistance mechanisms to predict the response of their ME model to mild acid stress. As a metric of predictive power, the authors compare predicted and measured changes in gene expression, and show for 80% of the genes predicted to be upregulated in acid stress the experimental data shows a consistent upregulation.Overall, the manuscript tackles an interesting biological problem, and presents the model implementation of different acid resistance mechanisms in a very clear fashion. However, several points in the final part of the manuscript were unclear to me:1) The stated goal of this manuscript is to provide a “fundamental understanding” (lines 18-19) of the acid stress response. However, I felt that the manuscript struggles to provide an actual systems-level understanding of this response. For example, the authors report numerous expression changes in amino acid metabolism and translation genes, which are not obvious (at least to me). Can the authors provide a rationale for these changes? Are these a consequence of e.g. massive overexpression of HdeB (which according to the authors accounts for up to 25% of the protein mass at pH 5)? Currently, the manuscript relies too heavily on merely describing the (predicted or observed) changes in expression, without necessarily providing insights on why these changes might occur.2) It was unclear to me why the authors focus on upregulated genes . Given that the ME model includes proteome allocation, I would expect some parts of the proteome to shrink as well, right? In general, I would like to see a table with the actual model predictions and the experimental RNAseq data in the supplementary material of the final manuscript.3) Ultimately, in its current form this manuscript is a well-executed consistency check: based on what we know about the molecular mechanisms of acid stress resistance, do we predict a cellular response to acid stress that is consistent with experimental data? But as the authors state themselves (lines 433-434), a key asset of such a model is its ability to design intervention strategies to reduce acid tolerance. However, the authors currently refrain from predicting intervention strategies (barring the short discussion in lines 434-438), which is a pity. I would suggest that the authors include a results section, in which they examine at least some potential intervention strategies, with a focus on those interventions that are also experimentally feasible (even if the authors decide not to test any of these interventions themselves).Additional points:1) Figure 1S: it was unclear to me what exact earlier results are being reproduced in this figure. Maybe add some more explanation in the figure caption, or include these previous results in the figure?2) I was intrigued by the model predicting that a change in FA composition is enough to reduce growth rate by ~10% . Do the authors have an explanation for this? Along the same lines, the authors predict that Cfa has the largest change in expression in the acid-adapted profile. Would overexpression of Cfa be sufficient to a) change lipid composition and b) reduce growth rate?3) In lines 155 to 162 the authors refer to expression changes in various genes, without providing a pointer to a supplementary table of figure. Please make sure to include such a pointer. The same accounts for the text in lines 231 to 242.4) Lines 201-202: “…indicating that protein stability might be an underlying factor that influences the protein expression in the periplasm.” This sentence was unclear to me: do the authors suggest that designated regulatory mechanisms that expression the respective proteins in a pH-dependent manner?5) The authors identify LptA as “the major factor causing the drop in growth rate and increase in HdeB mass fraction” (lines 236-237). Do the authors predict that overexpression of LptA, or expression of an LptA protein variant from a related bacterial species with better lower pH tolerance, would alleviate the growth defect? If yes, are data on such a validation experiment available in the literature?6) To validate their predictions, the authors compare gene expression with RNA-seq data. I’m curious whether the authors can also use published proteomics data  for validation?7) Can the authors include the numbers of DEGs for the different categories mentioned in lines 316 – 318?8) The authors should include the full list of DEGs in their final model  in the supplementary material.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: No: As described above, there are several instances in which the authors highlight example results, without providing a pointer to the full results/analysis.**********what does this mean?). 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