The Chemical Information Science Gateway (CISG) of F1000Research was originally conceptualized as a forum for high-quality publications in chemical information science (CIS) including chemoinformatics. Adding a publication venue with open access and open peer review to the CIS field was a prime motivation for the introduction of CISG, aiming to support open science in this area. Herein, the CISG concept is revisited and the development of the gateway over the past four years is reviewed. In addition, opportunities are discussed to better position CISG within the publication spectrum of F1000Research and further increase its visibility and attractiveness for scientific contributions. The more narrowly defined chem(o)informatics discipline focuses on the organization and systematic analysis of small molecules, associated experimental data, and structure-property relationships as well as on the design and prediction of new chemical entities with desired properties4. In addition, chemoinformatics addresses emerging ‘big data’ trends in chemistry6 and covers artificial intelligence methods, which experience a new wave of excitement in many scientific fields including chemistry8. Notably, machine learning methods have a long history in chemoinformatics and are a mainstay for compound property prediction4. Given its scope and methodological repertoire, chemoinformatics can be rationalized as a part of the CIS spectrum9. Chemical information science (CIS) includes all computational approaches to represent and analyze chemical structures and chemical data or retrieve chemical information from any source9 to provide a new open access and open peer review10 platform for broadly defined CIS publications. It was considered a much-needed extension of the CIS journal landscape with emphasis on open science and transparent peer review9. In 2017, the CIS Channel became a Gateway, its scope was further refined, and its advisory board extended11, leading to CISG in its current form. Without doubt, CISG filled a void within the journal landscape. In addition to fostering open science and post-publication communication, the CISG initiative strongly emphasized contributions of high scientific quality. Therefore, a two-stage review was established. Submissions to CISG were pre-reviewed by its advisory board to determine if the work had scientific quality and potential to advance the field. If so, the manuscript was published in CISG and subjected to open peer review using referees agreed upon by the authors. If not, authors still had the opportunity to publish their work in F1000Research outside CISG. Furthermore, the submission of off-the-beaten-path contributions to CISG was strongly encouraged11, for example, studies showing why a computational method was not capable of solving a particular problem. From the beginning, the CISG concept was enthusiastically received by investigators in the field and a number of leading experts joined CISG’s advisory board. Hence, CISG was off to a good start, with strong support from F1000Research.In 2015, the CIS Gateway (CISG) was originally launched as a CIS publication ‘Channel’ by F1000Research12. On the other hand, open review might occasionally also come across as too timid. Taken together, these points indicate that more efforts –and more publications– will be required to establish an open review culture in the CIS field that rivals anonymous peer review. In the past four years, CISG has not attracted the attention in the greater CIS community we initially had hoped for. So far, only 16 papers have been published in CISG (including only one in 2018). The number of papers was never regarded as a primary success criterion for CISG, but steady growth was desirable, as an indicator of increasing awareness and acceptance in the field. While it is generally difficult to establish new journals or publication platforms, more positive resonance was anticipated. Some CISG advisors have explored reasons for the apparent reluctance to publish in CISG and a few likely causes emerged. First and foremost, the absence of an impact factor (IF) –consistent with the publication philosophy of F1000Research– presents a problem for scientists whose advancement largely depends on IF criteria. This affects entire academic communities (such as in China). While views concerning IFs greatly differ in the field, IF-dependent publication restrictions cannot be disregarded and inevitably confine the number of potential authors. In addition, required article processing charges for open access (which are moderate at F1000Research) appear to play a role for many in the CIS field, more so than one might expect. Moreover, perhaps surprisingly, a number of investigators do not feel comfortable with open peer review, whereas others strongly support it. However, there still is a tendency to shy away from exposing scientific work to public evaluations, although open review and scientific discourse need to follow defined criteria and standardsThe considerations above concerning IF, article processing charges, and open review are general in nature and not specific to CISG. However, in combination, these issues have made it difficult to establish CISG on the CIS publication landscape, although it is rather sparsely populated with specialized journals (with low to moderate IFs). Moreover, there are at least two CISG-specific issues that might have hindered its evolution. First, the two-stage review process is frequently viewed controversially and might be considered as overly strict. Second, CISG has occasionally been regarded as a quasi “private” or “elite” publication venue, which was, of course, far from being the intention. The quest for high scientific quality was never considered the privilege of just a few. Regardless, these issues have most likely also contributed to the slow development of CISG over the past four years, while a flagship journal in the CIS field is currently missing.13, major funding agencies including the EU commission will now require open access dissemination of research results including the chemical sciences. F1000Research in particular has seen a growth in submissions since 2015 as open access publishing becomes more ‘mainstream’.On a positive note, since launching CISG, open science in the traditionally conservative chemical sciences has further progressed. For example, new open access journals have been introduced by major publishers and there is a clear trend to make chemical data and software tools publicly available on open access platforms (in part due to more stringent data deposition requirements for publications). In addition, many academic-industry collaborations have been initiated in drug discovery, material science, and nanoscience, and CIS , which also help to support open science initiatives. Furthermore, with the recent announcement of Plan SDespite the complications discussed above, reasons for further supporting and promoting CISG remain unchanged and are as attractive as at the time of CISG’s inception: open science, open access, fast publication, transparent reviews, and ample opportunities for unconventional contributions. While we will not be able to address IF-related publication constraints, going forward, some adjustments will be made to further increase the visibility of CISG and its attractiveness as a publication venue.CISG will continue to build upon its high-calibre advisory board and emphasize high-quality science. However, we will soften the two-stage review process and pre-review criteria. Only if there is a consensus view among advisors that a submission is scientifically not suitable for CISG, it will be forwarded to F1000Research for further consideration. Otherwise, the manuscript will be published immediately in CISG and subjected to open peer review, with the help of advisors. Several early career researchers will be added to the advisory board to further increase its range of scientific interests, motivations, and experiences. Cytoscape Apps14 andRPackage15 Gateways or theBioinformatics Education and Training16 and thePython17 Collections. For example, Python has become the preferred programming language for CIS. These related Gateways and Collections including CISG may be more closely linked going forward and positioned in context, offering opportunities for cross-over calls for papers or other joint initiatives. For example, given the increasing emphasis put on data science and artificial intelligence in chemistry, biology, and drug discovery, themed calls for contributions focusing on data science and artificial intelligence in different fields might be considered. F1000Research’s staff will launch and coordinate such efforts, which will provide many opportunities for submission of Method Articles, Software Tool Articles, Data Notes, or other types of papers, allowing researchers credit for the range of research outputs they are producing. Given the close connection between the Python Collection and programing in CIS, there should also be significant potential for growth in this area, especially for publication of Software Tool Articles. F1000Research have worked closely with software communities to help support computational paper submissions more broadly by allowing authors to submit in LaTeX18, integrate interactive figures into articles via Plotly19 and be able to run code re-analysis on the article page20. In addition, Data Notes will become increasingly important for CISG, given its close link to data science, similar to developments in computational biology.Importantly, other Gateways and Collections have emerged on the F1000Research platform, which are related to CISG from a methodological viewpoint, such as theWe are looking forward to seeing CISG entering its next phase, hopefully with more resonance from the CIS field. It is noted that CISG continues to provide the only opportunity for open review in CIS and we hope that more investigators will see the benefits of the open post-publication review culture. From the CISG access statistics, it is clearly evident that articles published there are seen and read. From our own contributions to CISG, I know that they are widely cited as well, which is more important than an IF label. Hence, there are good reasons to give it a try and submit to CISG, with the added bonus of efficient handling of papers by F1000Research and its streamlined editorial procedures. No data are associated with this study.


Background: Drug probe phenotyping is used extensively in academic and industry research to evaluate cytochrome P450 (CYP) phenotype in order to account for sources of between- and within- subject variability in metabolic clearance. In terms of application, CYP3A is the most important drug metabolizing enzyme the most frequently studied. Currently, phenotyping studies for CYP3A involve the administration of midazolam and collection of timed blood samples up to 24-48 hours in order to determine an area under the plasma concentration time curve (AUC). The key challenge that limits the use of midazolam-based phenotyping for CYP3A in academic research settings and preclude the use of this approach in a clinical setting is the logistical burden of collecting frequent blood samples for up to 48 h post dose following the administration of a probe drug ± an interacting drug.Aim: The current study sought to validate if a reduced sampling interval could be used to accurately define both between-subject variability in CYP3A phenotype and the magnitude of changes in CYP3A activity due to either induction or mechanism-based inhibition.Methods: The area under the curve (AUC) for midazolam was assessed under baseline, induction  and, following a washout period of 4 days, mechanism based inhibition  conditions in a cohort of 30 health males. The capacity of normalized reduced sampling interval AUCs measured over 0 to 1, 0 to 2, 0 to 3, and 0 to 4 h to accurately define the AUC0-6 was evaluated with respect to precision (R2 for correlation), bias , agreement  and proportional bias (linear regression of Bland Altman parameters).Results: Robust concordance was observed between the AUC calculated from PK collection intervals of 0 to 3 and 0 to 6 h in terms of both the measurement of between-subject variability in midazolam AUC and changes in midazolam AUC due to induction and mechanism-based inhibition of CYP3A4.Conclusion: On this basis, it is proposed that a 3-h assessment of midazolam AUC (AUC0-3) represents a viable strategy to reduce the logistical burden associated with the assessment of CYP3A phenotype. In vivo drug probe phenotyping is the current gold standard approach to evaluate cytochrome P450 (CYP) phenotype in order to account for sources of between- and within- subject variability in metabolic clearance (learance . This aplearance . By asseH = 27 L/h) drug that is extensively metabolized by CYP3A4, CYP3A5, and CYP3A7  are the most clinically important drug metabolizing enzymes thus the most frequently investigated in phenotyping studies . By way d CYP3A7  making id CYP3A7 .ex vivo activity or mRNA biomarkers)  . Similarmarkers) , evaluatmarkers) , and limmarkers)  have beemarkers) . The notmarkers) . Likewismarkers) .max). The importance of this omission being that the time course and magnitude of CYP3A induction and mechanism-based inhibition in the liver and intestine are not equivalent . Samples were vortexed for 3 min, then centrifuged at 16,000 g for 5 min. Analytes in a 2.5 μl aliquot of the resulting supernatant were separated from the sample matrix by ultra-performance liquid chromatography (UPLC) performed on a Waters ACQUITY™ BEH C18 column  using a Waters ACQUITY™ UPLC system. Column elutant was monitored by mass spectrometry, performed on a Waters Q-ToF Premier™ quadrupole, orthogonal acceleration time-of-flight tandem mass spectrometer operating in positive electron spray ionization (ESI+) mode. Selected ion data was extracted at the analyte [M+H]+ precursor m/z. Resulting pseudo-MRM spectra were analyzed using Waters TargetLynx™ software. Plasma analyte concentrations were determined by comparison of normalized peak areas in participant samples to those of external calibrators. As described previously, rifampin and clarithromycin plasma concentrations were also determined pre-midazolam dosing on day 8 and day 15, respectively  for PK sampling intervals of 0–1, 0–2, 0–3, 0–4, and 0–6 h post midazolam dosing on study day 1 (baseline), day 8 (following a 7-day course of rifampin), and day 15 (following a 3-day course of clarithromycin). Similarly, the pharmacokinetic parameters maximal concentration (Cmax), time to maximal concentration (Tmax), elimination half life (t1/2), elimination rate constant (k) and full area under the curve (AUC0-inf) were determined using non-compartmental methods . AUC0-inf was calculated as AUC0-6 plus AUC6-inf, where AUC6-inf (the extrapolated AUC) was calculated by dividing the final measured concentration (Clast) the elimination rate constant (ke) determined over the period tmax to 6 h. In all cases the % extrapolated (AUC6_inf/AUC0-inf) was less than 20%.Non-compartmental methods were used to calculate the area under the midazolam plasma concentration time curve  and the 1-OH midazolam to midazolam ratio measured at 3, 4, and 6 h were evaluated with respect to agreement of the normalized reduced sampling interval AUCs (or metabolite ratios) with the midazolam AUC0-6. Participant AUC data grouped by study phase was randomised 1:2 into training and validation data sets. The training dataset (n = 10 participants) was used to derive linear scaling factors (Equation 1) to normalize each of the AUC0-1, AUC0-2, AUC0-3 and AUC0-4 to the AUC0-6.The performance of test sampling intervals  on the difference in AUC .Validation datasets (n = 60) for midazolam AUCday8/AUCday1) and inhibition (AUCday15/AUCday1). Bland-Altman analyses were performed to evaluate the correlation between the respective AUC ratios for the reduced sampling interval AUCs and the corresponding AUC0-6 ratio. Linear regression was performed to assess proportional bias based on the influence of mean of the normalized reduced sampling interval AUCs and AUC0-6 parameters on the difference between the normalized reduced sampling interval AUCs and the AUC0-6 parameters. Bland-Altman plots were created by plotting the difference between the normalized reduced interval AUC and the AUC0-6 on the y-axis and the mean of the reduced interval AUC and the AUC0-6 on the y-axis.The performance of each reduced sampling interval AUC was also evaluated with respect to the concordance of the change (Δ) in midazolam exposures due to induction and mechanism-based inhibition of CYP3A. Using the same validation data set described for the assessment of the performance of the absolute AUC, midazolam AUC ratios were calculated for induction  with the AUC0-6 parameter of >0.85 with and without applying a forced zero intercept.An R0-6 parameter in the range 0.9 to 1.1 when applying a forced zero intercept.A slope for the correlation of the test parameter with the AUCt-test of the difference in test and AUC0-6 parameters versus 0.A lack of statistical significance for a one sample 0-6 on the difference between the normalized reduced sampling interval AUC and the AUC0-6. That is to exclude a proportional bias in the dataset, and to ensure that the magnitude and direction of difference between the normalized reduced sampling interval AUC and the AUC0-6 is independent of the magnitude of the mean of these AUCs.A lack of statistical significance for the linear regression of the effect of the mean of normalized reduced sampling interval AUC and AUC0-6 and Cmax, respectively. Dosing of clarithromycin (250 mg twice daily) for 3 days resulted in mean 49% and 40% increases in midazolam AUC0-6 and Cmax, respectively.A summary of the geometric mean (95% CI) midazolam AUC baseline, post- induction and post- mechanism-based inhibition are reported in 0-1, AUC0-2, AUC0-3, and AUC0-4) to midazolam AUC0-6 . Linear regression analysis demonstrated that there was no effect of the mean midazolam AUC on the difference in midazolam AUC, indicating a lack of proportional bias within each dataset (p ≥ 0.621). Based on fulfilment of the criteria for acceptance of precision and accuracy, the shortest acceptable sampling interval for assessment of absolute midazolam AUC was 0 to 3 hours. For absolute AUC, precision was also assessed at each data point in the validation dataset  as the difference between the normalized reduced interval AUC and the corresponding AUC0-6. In all cases the difference between the normalized reduced interval AUC estimates in the AUC0-3 and AUC0-4 datasets were <20% of the corresponding mean parameter estimate. Similarly, analysis of the correlation of the raw midazolam AUC measured over 0 to 3, 0 to 4, and 0 to 6 h versus the full midazolam AUC (AUC0-inf) demonstrated that in all cases the R2 for the correlation was greater than 0.875  was used to derive equations that normalized each reduced sampling interval AUC (AUCm AUC0-6 Table 4.0-3 (R2 = 0.94) and AUC0-4 (R2 = 0.98) ratios with the AUC0-6 ratio fulfilled the criteria of an R2 > 0.85, whereas the R2 for the correlation of the AUC0-1 and AUC0-2 ratios with the AUC0-6 ratio failed to meet this criteria. One-sample t-tests demonstrated that in all cases the difference between the reduced interval AUC ratios and the AUC0-6 ratio were not significantly different from 0 (p ≥ 0.062). Linear regression analysis demonstrated that there was no effect of the mean AUC ratio on the difference in the AUC ratio, indicating a lack of proportional bias within each dataset (p ≥ 0.065). Based on fulfilment of the criteria for acceptance of precision and accuracy, the shortest acceptable sampling interval for assessment of induction of midazolam AUC was 0 to 3 h. The correlation of the Day 8/Day 1 (induction) AUC ratio for the AUC0-3 reduced sampling interval compared to the AUC0-6 ratio is presented in Correlation of the AUC0-3 (R2 = 0.87) and AUC0-4 (R2 = 0.97) ratios with the AUC0-6 ratio fulfilled the criteria of an R2 > 0.85, whereas the R2 for the correlation of the AUC0-1 and AUC0-2 ratios with the AUC0-6 ratio failed to meet this criteria. One-sample t-tests demonstrated that in all cases the difference between the reduced interval AUC ratios and AUC0-6 ratio were not significantly different from 0 (p ≥ 0.148). Linear regression analysis demonstrated that there was no effect of the mean midazolam AUC ratio on the difference in midazolam AUC ratio, indicating a lack of proportional bias within each dataset (p ≥ 0.277). Based on fulfilment of the criteria for acceptance of precision and accuracy, the shortest acceptable sampling interval for assessment of induction of midazolam AUC was 0 to 3 h. The correlation of the Day 15/Day 1 (mechanism-based inhibition) AUC ratio for the AUC0-3 reduced sampling interval compared to the AUC0-6 ratio is presented in Correlation of the AUC0-3 with respect to defining midazolam exposure. The capacity of midazolam AUC0-3 to define midazolam exposure was also evaluated with respect to comparative performance by reproducing previously proposed metrics in the current validation dataset.Results described above evaluate the absolute performance of the midazolam AUC0-6 varied between time point and study phases; R2 values ranged from 0.357 (6 h control phase) and 0.917 (3 h inhibition phase) , and for the inhibition study phase . In comparison, the R2 vales for the correlation of midazolam AUC0-3 with AUC0-6 for the control, induction, and inhibition study phases were 0.931, 0.906, and 0.971, respectively .Similarly, consistent with the conclusion of 0-3 data to prior or alternate studies where AUC0-6 has been measured.Here we describe, for the first time, the validation of a 3-h midazolam sampling interval to support the assessment of between-subject variability in CYP3A phenotype across specific sub-populations as well as the evaluation of changes in CYP3A function due to induction and mechanism-based inhibition of this enzyme. The key distinctions between the current analysis and prior analyses  are a) tmax), which, given the low midazolam basal fg of 0.26, is a major factor driving the reduction in midazolam AUC associated with induction of CYP3A  to infinity accounted for 82%, 83%, and 80% of the AUC0-inf for the baseline, induced, and inhibited study phases, respectively. By comparison, extrapolation of the AUC0-4 accounted for only 66%, 68%, and 64% of the AUC0-inf for the baseline, induced, and inhibited phases, respectively. This limitation is important to consider when determination of midazolam apparent oral clearance (CL/F) is the primary variable being considered, as calculation of CL/F requires robust determination of AUC0-inf. Under such circumstances the use of a reduced sampling interval AUC may not be appropriate as this strategy does not meet the criteria for extrapolation of AUC0-inf. It is worth noting however, that data presented in 0-inf. Two further limitations that are specific to the current analysis were 1) that while the cohort contained individuals of an appropriate age range for healthy volunteer studies , the cohort was exclusively male, and 2) the full AUC (AUC0-inf) was estimated by extrapolation of the AUC0-6 data. However, 1) there is no reason to suspect a gender difference in the performance of a reduced sampling interval AUC given that there is not a statistically significant gender difference observed for the change in midazolam AUC0-6 due to induction of CYP3A4  and magnitude (none-weak-moderate-strong), such protocols make use of all available data points to calculate midazolam AUC (within assay limits). There may however be industry applications to a reduced sampling interval AUC for midazolam-based CYP3A phenotyping, in terms of reducing blood draws when phenotyping pediatrics, elderly or other vulnerable populations.The reduction in logistical burden associated with reducing the sampling interval for midazolam-based CYP3A phenotyping is likely to have the greatest impact for research performed in academia and clinical sites. Typically, in the pharmaceutical industry, drug-drug interaction (DDI) studies with midazolam are typically designed to assess drug concentration out to 24–48 h to calculate AUC over this PK sampling interval as well as AUCA number of recent studies have attempted to remove the requirement to dose a drug probe to assess CYP3A phenotype by measuring either an endogenous small molecule substrate for this enzyme , or endo0-3) represents a viable strategy to reduce the logistical burden associated with the assessment of CYP3A phenotype and perturbations of phenotype caused by CYP3A induction or mechanism-based inhibition.In conclusion, the current study demonstrates a robust concordance between midazolam AUC calculated by either 0 to 3 and 0 to 6 h PK sampling intervals in terms of both the measurement of between subject variability and changes in midazolam exposure due to induction and mechanism-based inhibition of CYP3A4. On this basis, it is proposed that assessment of a 0 to 3hr midazolam AUC , and written informed consent was obtained from each participant prior to study enrolment. The study was conducted according to the principles stated in the Declaration of Helsinki, was compliant with CPMP/ICH/135/95 GCP standards, and is registered with the Australian New Zealand Clinical Trials Registry (ACTRN 12614001289606).Designed the research: MD, ADR, MV, MS, and AR. Sample collection and analysis: MD and AR. Data analysis: AK, AH, and AR. Wrote the manuscript: MD, AK, AH, ADR, MV, MS, and AR.This study was supported by a project grant [1100179] from the National Health and Medical Research Council of Australia. AR is supported by a Beat Cancer Mid-Career Fellowship from Cancer Council, Australia. AH is supported by a Postdoctoral Fellowship from the National Breast Cancer Foundation, Australia.MS and AR are recipients of industry funding for investigator initiated clinical trials from Pfizer Inc. Pfizer Inc provided support with study design and preparation of the manuscript. ADR and MV are employees and stock holders of Pfizer Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Nusinersen is the first approved drug for the treatment of spinal muscular atrophy (SMA). Treatment of SMA with nusinersen is based on a fixed dosing regimen. For other motoneuron diseases, such as amyotrophic lateral sclerosis (ALS), biomarkers are available for clinical diagnostics; however, no such biomarkers have yet been found for SMA. Serum and cerebrospinal fluid (CSF) samples of 11 patients with adult SMA type 3 were prospectively collected and analyzed during loading with nusinersen. Neurofilament heavy chain, tau protein, S100B protein, and neuron-specific enolase were investigated as potential biomarkers of motor neuron destruction. No significant pathological alterations in levels of neurofilament heavy chain, tau protein, or S100B protein were detected in the CSF or blood samples under baseline conditions or during loading with nusinersen. Neuron-specific enolase was marginally elevated in CSF and blood samples without significant alteration during treatment. In a mixed cohort of adult patients with SMA type 3, neurofilament heavy chain, tau protein, S100B protein, and neuron-specific enolase do not serve as potential biomarkers during the loading phase of nusinersen. The slow progression rate of SMA type 3 may not lead to detectable elevation of levels of these common markers of axonal degradation. SMN1) [SMN2, also encodes for a truncated SMN protein that is dysfunctional due to a splice site variant in SMN2, leading to exclusion of exon 7 from the mature RNA [Spinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disorder characterized by atrophy and weakness of skeletal muscles caused by mutations of the survival motor neuron 1 gene (SMN1) ,3. The sSMN1) ,4. A parture RNA .The classification of SMA is based on age at symptom onset and the most advanced milestone attained during motor development. Patients with SMA type 3 reveal symptoms after 18 months of age and gain the ability to walk independently at some point ,7.SMN2 premessenger RNA and modifies its splicing to promote the expression of full-length functional SMN protein. In clinical trials, treatment with nusinersen significantly increased motor function and survival in infants and children with SMA type 1 or 2 [The first approved drug for the treatment of SMA, nusinersen, is an antisense oligonucleotide, which is intrathecally administered. Nusinersen binds to a specific sequence within the e 1 or 2 ,9. BasedTreatment with nusinersen involves a fixed dosing regimen based on trials in infants and children with SMA type 1 or 2 and does not consider the clinical course of SMA, the progression rate of disease, or laboratory findings. In infants with SMA type 1, phosphorylated neurofilament heavy chain (NF-H) and neurofilament light chain were elevated before treatment and decreased during treatment with nusinersen ,11. To dWe prospectively collected and analyzed serum and cerebrospinal fluid (CSF) samples from the first 11 adult patients with SMA type 3 treated with nusinersen in our cohort in Germany. Similar to the biomarker studies of ALS, NF ,15,16,17Physical and neurological examination, lumbar puncture, and nusinersen administration were carried out at the Department of Neurology of the University Hospital in Essen, Germany. All 11 patients showed stability or a trend toward improvement on the Hammersmith Functional Motor Scale Expanded (HFMSE) for SMA, which was measured before the first and the fifth administration of nusinersen .In all CSF samples but one (Patient 11), red blood cell count (RBC) was below 500/µL. Eight CSF samples showed RBC below 100/µL . RBC wasCSF tau protein (reference range: <290 pg/mL), CSF NF-H (reference range: <0.69 ng/mL), and serum NF-H (reference range: <0.17 ng/mL) levels were within the reference range in all patients . Serum SAnalysis of CSF to serum ratio of NSE, NF-H, and S100B revealed no further significant information (data not shown). There was no correlation between CSF NSE level and clinical presentation.This is the first pilot study examining blood and CSF samples as possible biomarkers during loading with nusinersen in adult patients with SMA type 3.The stability or trend toward improvement on the HFMSE measured before the first and the fifth doses of nusinersen may indicate a positive response to the nusinersen treatment. However, a larger group of patients and a longer time of treatment are needed to clarify treatment response to nusinersen in adult patients with SMA type 3.While the five older patients showed elevated CSF total protein levels, the younger patients had CSF total protein levels within the reference range. Elevated CSF total protein can be found in patients with neuroinflammatory or neurodegenerative disorders like multiple sclerosis, chronic inflammatory demyelinating polyneuropathy (CIDP), or Alzheimer’s disease ,23,24,25In our patients, tau protein and NF-H levels were not elevated in the CSF. Both proteins are well-established markers of possible neural destruction. Elevated levels of NF in the CSF facilitate diagnosis of ALS ,13,14,17S100B level in the CSF was slightly elevated in Patient 3, and NSE levels were at some point marginally elevated in all patients except the youngest (Patient 4) and the oldest (Patient 11) patient. S100B and NSE are used as biomarkers for neuronal damage and are directly related to disease progression in patients with acute brain injury ,33. FurtSMN1  and copy number of SMN2 in all patients  technology on a KRYPTOR analyzer  ,40. S100The Clinical Immunological Laboratory Dr. Stöcker, Lübeck, Germany, analyzed NF levels in frozen CSF and serum samples. Phosphorylated NF-H level was measured using an established ELISA  .Reference range was defined and established by each of the German reference laboratories compared to the normal population.Mean values and SEM of laboratory results were calculated with PRISM 8 .In this cohort of adult patients with SMA type 3, neurofilament heavy chain, tau protein, S100B protein, and neuron-specific enolase did not seem to serve as potential biomarkers in the early treatment phase during loading with nusinersen. However, the varying clinical presentation of our patients may mask subgroup changes that will need examination of larger cohorts to clarify. Furthermore, the relatively slow progression rate of SMA type 3 compared to that of ALS may not lead to detectable elevation of levels of these common markers of axonal degradation. We propose that unbiased proteome studies and identification of neuroinflammatory markers or markers of synaptogenesis may prove more useful.


Retinol binding protein 4 (RBP4) facilitates the transport of retinol in the body but is also an adipokine and fatty acid transporter. Our study was aimed at investigating the associations between RBP4 abundance and fat deposition in cattle. Blood samples of 246 crossbred bulls were taken at 8 months of age and at slaughter at 18 months of age for the determination of RBP4, hormone levels, and fatty acid composition. Significant correlations between plasma RBP4 abundance at 8 months of age and carcass traits at 18 months of age were detected . Furthermore, RBP4 abundances in the plasma and subcutaneous fat were higher (P < 0.05) in bulls with increased fat deposition, whereas the liver RBP4 expression was not (P > 0.05). Retinol binding protein 4 was immunohistochemically localized in or close to adipocytes within muscle and adipose tissue and in liver stellate cells but not in hepatocytes. Overall, our results indicate that increased RBP4 levels were associated with increased fat deposition and altered fatty acid composition, but not with altered glucose tolerance, in crossbred bulls. Moreover, our results suggest that adipose-tissue-derived RBP4 may contribute to the circulating RBP4 level. Many cellular processes depend on the availability of retinol, in particular the regulation of gene expression, through binding to nuclear transcription factors involved in the maintenance of epithelial surfaces, immune competence, and reproduction3, thus making RBP4 an essential factor for the organism. Retinol transport was long assumed as the only function of RBP44. More recently, RBP4 was shown to also act as an adipokine that is involved in the modulation of insulin sensitivity in mice and humans5. However, data in humans revealed conflicting results regarding relationships between circulating RBP4, its gene expression and obesity or insulin resistance . This may indicate differences between rodent models and humans in this respect6. Moraes-Vieira et al.7 demonstrated that increased RBP4 activates innate immunity, with a subsequent adaptive immune response leading to the inflammation of adipose tissue. This finally results in systemic insulin resistance. There is still controversy, however, about the origin of the increased circulating RBP4 levels during insulin resistance8.Retinol binding protein 4 (RBP4) is the only circulating member among four proteins of this family and facilitates the transport of retinol (vitamin A) from the liver to the peripheral organset al.9, RBP4 can bind and transport fatty acids. Furthermore, RBP4 functions as a hepatokine and mediates the effects of the molecular clock on glucose metabolism in mice10.The function of RBP4 may be even broader than currently known. As recently demonstrated by Perduca 12. Only recently, hepatocytes were identified as the principal source of circulating RBP4 in mice8.Beside hepatocytes as the main source of RBP4, it is expressed in adipocytes, macrophages, myocytes and further tissues and cells13 its physiological role is still not well understood. Abd Eldaim et al.14 determined circulating RBP4 concentrations of approximately 45 µg/mL in the plasma of non-pregnant, non-lactating cows with quantitative western blots. The plasma RBP4 concentrations were similar in pregnant cows at 3 to 6 d before parturition but decreased immediately after parturition to ~25 µg/mL14. RBP4 was obviously released into colostrum but was almost undetectable in milk, whereas the plasma levels then returned to values observed in control cows. RBP4 protein was also shown to increase in subcutaneous fat (SCF) with age when comparing the SCF proteome in 12- and 15-month-old steers15.Although bovine RBP4 was characterized at the biochemical level decades agoRBP4 was related to the intramuscular fat (IMF) content in M. longissimus of cattle16. De Jager et al.16 identified RBP4 as a member of a gene set involved in intramuscular adipocyte lipid metabolism in Piedmontese × Hereford and Wagyu × Hereford crosses. Furthermore, the analysis of Brahman cattle also revealed a positive correlation between RBP4 expression and intramuscular fat deposition16. Additionally, several studies found an inverse relationship between vitamin A supply and IMF content in different cattle populations20. However, the magnitude of this relationship is unclear, and the physiological mechanism behind this phenomenon remains unaddressed. Due to the recently proposed functions of RBP4 beyond retinol transport, such as fatty acid transport9, its potential involvement in fat deposition and composition in cattle is of interest. Consequently, RBP4 may be a candidate factor for the variation in carcass composition in cattle and the fatty acid composition of beef. Thus, our study was aimed at the elucidation of RBP4 expression at mRNA and the protein level in bovine tissues and relationships between circulating RBP4, carcass parameters, fatty acid composition and hormone concentrations in the plasma of phenotypically diverse cattle. An F2-cross, generated from Charolais bulls and German Holstein cows in the founder generation21, was the experimental basis for this investigation. The founder breeds represent cattle of comparable stature but different growth rates and accretions of protein and fat. Charolais bulls grow faster and have higher protein accretion than German Holstein bulls, which accrete more fat in the carcass22. Bulls of the F2 generation varied widely in carcass fat deposition, providing an excellent model to study the mechanisms involved in carcass composition and, in particular, the role of RBP4 as one potential factor. To this end, we used reverse transcription – quantitative PCR (RT-qPCR) for the measurement of tissue-specific mRNA abundance. The RBP4 protein amount was determined semi-quantitatively in the serum and tissues by western blot analyses of protein extracts with antibodies for RBP4. Antibodies were tested for specificity, and one of the antibodies was also used to visualize the tissue localization of RBP4.The mRNA abundance of RBP4 mRNA was compared between two groups of bulls differing in their total amount of carcass fat selected from 246 F2 generation bulls of a Charolais × Holstein cross, as described in the Materials and Methods section. The high carcass fat group (HCF) accreted 94.2 ± 6.4 kg fat in the carcass, and the low carcass fat group (LCF) accreted 36.0 ± 6.4 kg. Higher (P < 0.01) concentrate consumption (HCF: 7.5 ± 0.6 kg/d vs. LCF: 5.9 ± 0.7 kg/d), residual feed intake (HCF: 7.0 ± 4.8 vs. LCF: −3.5 ± 6.1), and feed conversion ratio (HCF: 70.2 ± 3.6 MJ ME/kg vs. LCF: 65.4 ± 6.2 MJ ME/kg) were recorded for bulls with higher carcass fat deposition. Further phenotypic traits of both groups are presented in Supplemental Table et al.23.The abundance of r P < 0.0 concentrRBP4 was slightly upregulated in subcutaneous (SCF), omental (OF), and intestinal fat (IF) and in the liver of bulls with high carcass fat deposition . Normalized mRNA values of different types of tissues were obtained using different reference genes . Therefore, those values are not directly comparable between liver, muscle and adipose tissues. However, considering the raw Cp-values and amplification efficiency, the data indicate the highest RBP4 expression in the liver, slightly lower but similar expression in SCF and PF, and a clearly lower expression in OF, IF, and muscle tissue  but not in other tissues.The protein abundance was determined as band volume normalized with total protein in the respective lane as a loading control and divided by the mean intensity of all signals in the blot to enable a blot-to-blot comparison. Values are referred to as normalized protein abundance Fig. . The norImmunohistochemistry was applied to localize RBP4 in different tissues. In subcutaneous adipose tissue Fig. , the flu14. To test whether the plasma RBP4 levels are related to body composition or to the amount of accumulated fat in the body, the RBP4 levels were determined in all available plasma samples of the F2-crossbred bulls , but no correlations with the percentages of individual fatty acids exist (data not shown).The hormone levels determined at 8 months of age and at slaughter at 18 months of age are presented for the two groups of bulls in Table n-6). In the case of SFA, the C12:0, C14:0 and C15:0 proportions in the plasma of the HCF group were significantly lower compared with the LCF group. The main fatty acid in bull plasma was C18:2n-6 up to a proportion of 46% of the total fatty acids. The sum of monounsaturated fatty acids (MUFA) was not different between both groups, but trans vaccenic acid (C18:1trans-11) and C15:1cis-10 levels were significantly higher in the HCF bulls.The plasma lipid content Table  was not Bulls were subjected to a glucose tolerance test at an age of 8 months. The course of the plasma glucose and glucagon concentrations during this test was similar between both groups of bulls Fig. . There wet al.24). Studies in cattle described an increased intramuscular fat deposition as a result of decreased vitamin A supply20. Consequently, the abundance and availability of RBP4, as the sole transporter of vitamin A in the organism, may indirectly affect the abovementioned processes. The results in humans and rodent models established links between RBP4 expression and obesity, diabetes and other metabolic diseases. However, there are many controversial discussions regarding the mechanism of action and the origin of RBP4 in different species . In contrast, data on RBP4 in cattle are rare. We demonstrated here that RBP4 is expressed at the mRNA and protein level in different adipose depots and the livers of adult cattle. RBP4 mRNA is stably expressed in SCF, OF, PF, and IF, in concordance with Locher et al.25, and in the liver and skeletal muscle tissue. Western blot data revealed a similarly robust protein expression in all tissues except in skeletal muscle. This is in accordance with data in humans and mice indicating liver cells and adipocytes as major sources of RBP4 expression27. Thompson et al.8 showed that the liver is the exclusive source of circulating RBP4 in mice, and adipocyte expression of RBP4 seemed not to result in secretion, suggesting an auto- or paracrine function for adipocyte-derived RBP4. This in turn contradicts the hypothesis that adipose depots, especially visceral fat, are major contributors to circulating RBP4 with increasing obesity27. We measured significantly higher plasma RBP4 levels in cattle with high compared to low body fat deposition at an age of 8 months as well as 18 months. Since there were no different protein levels of RBP4 in the livers of HCF and LCF bulls at an age of 18 months, we speculate that subcutaneous and intestinal fat could be major contributors to increased plasma RBP4 in HCF bulls. This assumption is supported by the positive correlation between RBP4 levels in plasma and in subcutaneous fat in our study. Chen et al.28 identified RBP4 as secreted from rat adipocytes in a proteome study, further supporting adipocytes as a possible source of circulating RBP4. However, a significant positive correlation between liver weight and plasma RBP4  does not preclude that the larger amounts of liver tissue in HCF bulls may have contributed to the increase in plasma RBP4.Vitamin A (retinol and its metabolic products) is of crucial importance for many physiological processes, such as vision, growth, development, reproduction, and lipid and glucose metabolism, mostly by activation of the transcription factors . The authors concluded that RBP4 should be considered a lipid-binding protein rather than a specific retinol transporter. If RBP4 binds and transports mainly SFAs9, this could contribute to the observed significant differences in plasma fatty acid profiles of bulls with high and low fat deposition in our study. However, the proportions of saturated fatty acids were decreased in the plasma of HCF bulls (less myristic and pentadecanoic acid), whereas C18:2n-6 was significantly increased, leading to a significantly higher proportion of PUFAs. There was no difference in the total lipid content of the plasma of both groups; thus, a clear relationship between different concentrations in circulating RBP4 and the fatty acids in plasma seems to be unlikely. Nevertheless, RBP4 should be considered in its new function9 in further studies.Transcriptome studies in bovine muscle with varying intramuscular fat content and a proteome study of bovine subcutaneous fat, before and after 3 months of fattening, have identified 5. The study of Graham et al.34 showed that the elevation of serum RBP4 occurred before diabetes development and that it was correlated with insulin resistance and cardiovascular risk factors in subjects with obesity, type 2 diabetes, impaired glucose tolerance and in non-obese, non-diabetic subjects with a strong family history of type 2 diabetes. They found that the level of circulating RBP4 is linked more specifically with insulin resistance than that of other adipokines such as leptin and adiponectin. Since adipose tissue is considered a contributor to elevated serum RBP4 levels in the state of insulin resistance, we tested the glucose tolerance of the bulls. We found no differences in glucose clearance or glucagon levels between HCF and LCF bulls and only slightly increased insulin levels in HCF bulls during the first minutes of the test, indicating that even the bulls with higher carcass fat were in a metabolic healthy state at an age of 8 months. Increased glucagon and similar insulin levels in HCF compared to LCF bulls at slaughter are signs of an increased gluconeogenesis but still do not indicate a diabetic state in these bulls. Leptin was not different at 8 months of age; it increased during growth but did not result in significant differences at slaughter between HCF and LCF bulls. This underlines that the intensive fattening system, which is widely used in beef production, does not evoke metabolic diseases. Even bulls with the highest carcass fat deposition from our population were not obese in terms of a pathological condition.In humans, the increased serum RBP4 levels were also associated with obesity and insulin resistanceRBP4 in adipose tissues contributes to circulating RBP4 in cattle. We found significant relationships between body composition and RBP4 levels in cattle as described for other species. Further studies are required to elucidate how RBP4 is involved in bovine adipose tissue development. Blocking the insulin stimulated phosphorylation of the insulin receptor, as shown in primary human adipocytes35, could be a possible mechanism.The results of our study suggest that the protein expression of RBP4 gene expression on mRNA and protein level as well as cellular localization of RBP4 in cattle. Our results indicate differences in localization (and possibly secretion) of RBP4 in cattle compared to those reported for rodents. Circulating RBP4 is increased already at juvenile age in bulls, with elevated fat deposition at a later age, even without any sign of metabolic disturbances. Consequently, increased circulating RBP4 may be an indicator for higher fat accumulation in cattle.In summary, we provided data of 2-generation bulls of a Charolais × Holstein cross, raised under standardized feeding and housing conditions and slaughtered at 18 months of age in the abattoir of the Leibniz Institute for Farm Animal Biology36. Animal care and experimental procedures followed the guidelines of the German Law of Animal Protection. The protocols were approved by the Animal Protection Board of the Leibniz Institute for Farm Animal Biology as well as by the Animal Care Committee of the State Mecklenburg-Western Pomerania, Germany .This study involved 246 Fet al.23. Blood samples were taken as described by Weikard et al.37 at an average age of 8 months and at slaughter. Briefly, blood was collected from the left jugular vein into EDTA tubes  by a standardized procedure at 7:30 AM after a fasting period of 12 h. Plasma was obtained from blood samples by centrifugation within 30 min after sampling. Plasma samples were stored at −80 °C until further use. Tissue samples from subcutaneous fat (SCF), perirenal fat (PF), omental fat (OF), intestinal fat (IF), the liver, and M. longissimus dorsi (MLD) were taken immediately after slaughter, frozen in liquid nitrogen and stored at −80 °C.The bulls were extensively phenotyped after slaughter, including carcass composition, meat quality, chemical analyses, muscle structure and marbling, as described previously by Liu et al.36.Two groups of bulls with either high or low carcass fat were retrospectively assigned to enable comparisons between animals with extreme differences in body composition. Bulls were sorted according to their carcass fat mass, and each of the 20 animals with the highest and lowest mass were assigned to the high carcass fat group  and low carcass fat group , respectively. Two bulls from the LCF group were excluded from further analyses due to health problems. Thus, the LCF group was comprised of 18 bulls. Higher (P < 0.01) concentrate consumption (HCF: 7.5 ± 0.6 kg/d vs. LCF: 5.9 ± 0.7 kg/d), residual feed intake (HCF: 7.0 ± 4.8 vs. LCF: −3.5 ± 6.1), and feed conversion ratio (HCF: 70.2 ± 3.6 MJ ME/kg vs. LCF: 65.4 ± 6.2 MJ ME/kg) were recorded for bulls with higher carcass fat deposition, using calculations described by Widmann et al.38. Briefly, glucose (1 g/kg of BW0.75) was infused into the jugular vein via a jugular cannula after a period of 12 h fasting. Blood samples were taken before (0) and 7, 14, 21, and 28 min after glucose infusion. Blood samples for hormone  measurements were collected in tubes with potassium-EDTA . Samples were centrifuged at 1,500 × g for 20 min at 4 °C and supernatants were aliquoted and stored at −80 °C until analysed.Bulls were subjected to an intravenous glucose tolerance test (GTT) at the age of 8 months as described by Lahann et al.39). A standard curve was prepared at concentrations from 2 to 200 μU/mL. Cross-reactivity with bovine insulin was 90%. Previous findings have shown that this assay exhibited good linearity for concentrations in the range of 25 to 100 μU/mL in bovine plasma samples. The sensitivity of the insulin RIA (the lowest detectable level) was 2 µU/mL. Intra- and interassay coefficients of variation (CV) were 8.2 and 4.3%, respectively. Glucagon was measured by RIA using a kit from Linco  as described by Hammon et al.40. Intra- and interassay CV were 9.6 and 5.8%, respectively. The assay is specific for the determination of pancreatic glucagon in serum or plasma in most mammals. Cross-reactivity to oxyntomodulin, which is the primary gut glucagon, is less than 0.1%.Plasma glucose was analysed spectrophotometrically  by a kit  using the hexokinase method. Insulin was measured in the plasma by RIA using a porcine kit  of t-butylhydroxytoluene (BHT) to prevent the oxidation of PUFAs. The extraction mixtures were stirred two times for 15 min and stored at 5 °C for 18 h in the dark and subsequently washed with 0.02% CaCl2 solution. After centrifugation , the organic phase was dried with Na2SO4 and K2CO3 , and the solvent was subsequently removed under a gentle nitrogen stream at room temperature. The total lipid contents were stored at −18 °C until transmethylation of fatty acids. The lipid extracts were redissolved in 150 µL of toluene for methyl ester preparation. Next, 1 ml of 0.5 M sodium methoxide in methanol was added to the samples, which were shaken in a 60 °C water bath for 10 min. Subsequently, 0.5 mL of 14% boron trifluoride (BF3) in methanol was added to the mixture, which was then shaken for an additional 10 min at 60 °C. Saturated NaHCO3 solution (2 mL) was added, and the fatty acid methyl esters (FAMEs) were extracted three times in 2 mL of n-hexane. The combined n-hexane extract was dried with Na2SO4 and K2CO3  and, after filtration, reduced to dryness using a vacuum centrifuge . The FAMEs were resuspended in 100 µL of n-hexane and stored at −18 °C until they were used for gas chromatography (GC) analysis.Plasma samples, 1500 µL, were added dropwise to 8 mL chloroform/methanol  at room temperature. The solution contained C19:0 as an internal standard. The detailed sample preparation procedure has recently been described by Dannenberger et al.43. Briefly, the initial oven temperature was 150 °C, which was held for 5 min; subsequently, the temperature was increased to 175 °C and then to 200 °C at a rate of 2 °C/min and held for 10 min. Finally, the temperature was increased to 225 °C at a rate of 1.5 °C/min and held for 25 min. Hydrogen was used as the carrier gas at a flow rate of 1 mL/min. The split ratio was 1:20, and the injector and detector were set at 260 °C and 280 °C, respectively. The quantification of fatty acids was done by the use of C19:0 as the internal standard. For the calibration procedure the reference standard mixture ‘Sigma FAME’ , the methyl ester of C18:1cis-11, C22:5n-3 and C18:2cis-9, trans-11 , C22:4n-6  and C18:4n-3  were used. The five-point calibration of single fatty acids ranged between 16 and 415 mg/mL and was checked after the GC analysis of five samples.The fatty acid analysis of the bull plasma lipids was performed using capillary GC with a CP-Sil 88 CB column  that was installed in a PerkinElmer gas chromatograph CLARUS 680 with a flame ionisation detector and split injection . The detailed GC conditions were recently described by Dannenberger et al.44. Total RNA was quantified by a NanoDrop ND-1000 spectrophotometer . The integrity of the RNA was determined with an Experion Automated Electrophoresis System using the RNA StdSens analysis chip . The samples of RNA with an RQI  value above 7 were considered as qualified for further use. First strand cDNA was synthesized from 100 ng total RNA of each sample in 20 µL reaction volume according to manufacturer’s instructions .The protocols for RNA isolation from adipose tissues, muscle and liver are described in detail by Schering 44 to analyse expression levels of RBP4. Briefly, primers  were designed with Primer 3web  and synthesized by Sigma-Aldrich. Each reaction contained 10 ng cDNA template, 0.2 µM of the respective forward and reverse primers, and 5 µL SYBR Green Supermix (Bio-Rad) in 10 µL reaction volumes. All reactions were carried out with following conditions: initial denaturation of 95 °C for 3 min and 45 cycles of 95 °C for 10 s, 60 °C for 30 s, 70 °C for 45 s. The amplicon specificity was confirmed by melting curve analysis and sequencing. The expression values for RBP4 were normalized as described by Liu et al.23 to two reference genes in each tissue: beta-2-microglobulin (B2M) and ubiquitously expressed transcript (UXT) for adipose tissues; UXT and ribosomal protein S9 (RPS9) for liver tissue; topoisomerase II beta (TOP2B) and B2M for muscle tissue. Crossing point (CP) values were adjusted manually in the iQ5 software  to obtain comparable values for the standard curves on each plate. The efficiency of amplifications was calculated from the standard curves with serial dilutions . The efficiency-corrected ΔΔCp method45 was applied for normalizations of CP values. The fold change between the two groups was determined with the REST algorithm . Normalized relative quantities of mRNA (NRQ46) were used for correlation analyses.Quantitative reverse transcription PCR with iQ detection system (Bio-Rad) was performed in duplicate as previously describedE. coli with N-terminal his-tag and was used as a positive control and for the blocking of specific binding of the RBP4 antibodies. For specificity tests, two parallel blots were incubated with the antibody or with the antibody blocked with the recombinant protein before incubation. All membranes were incubated with HRP conjugated Trueblot rabbit IgG  secondary antibody. The antibody label was detected with highly sensitive chemiluminescence substrate . Chemiluminescence was recorded with a Chemocam HR-16 imager  and quantified with Labimage 1D software . All samples were measured at least in duplicates on separate blots.Bovine plasma RBP4 was quantified by western blot analysis after albumin removal with Aurum Affi-Gel Blue  in 20 µg protein per lane. Additionally, the original plasma samples were analysed (0.5 µl per lane) together with a dilution series of recombinant bovine RBP4  using 40, 20, 10, 5, and 2.5 ng per lane to determine the absolute plasma concentration  using a Leica CM3050 S  cryostat microtome. The air dried sections were fixed with 4% paraformaldehyde for 10 min, 2 × 5 min washed with PBS and permeabilized for 10 min with PBS including 0.1% of TritonX100 (PBST). Liver samples were treated with filtrated Sudan Black  for 30 min and washed twice with PBS to reduce auto-fluorescence. After blocking with 10% goat serum in PBST for 15 min, sections were incubated with the Biorbyt antibody against RBP4 (1:100 in PBST) for 1 h at room temperature in a humidity chamber. For double labelling experiments, a monoclonal mouse CD163 antibody was added  to the incubation solution. Goat anti-rabbit or mouse IgG secondary antibodies labelled with Alexa Fluor 488  or with MFP590 (MoBiTec) were used for the detection of the specific binding of the primary antibodies. Nuclei were counterstained with 1 µg/mL Hoechst 33258 . Slides were covered with the appropriate cover-slips using ProLong Diamond Antifade Mountant (Thermo Scientific). Slides used as negative controls were either incubated without a primary antibody or with the primary antibody after blocking with recombinant RBP4. No unspecific binding of the secondary and the primary antibody was detected. Immunofluorescence was visualized on a Nikon Microphot SA fluorescence microscope  equipped with a CC-12 high-resolution colour camera  and CELL^F imaging software (OSIS).adj). Differences were considered as significant if Padj ≤ 0.05.Data were analysed with SAS statistical software . Relationships were calculated by Pearson correlation coefficients using the CORR procedure in SAS. The normalized protein values of RBP4 in tissues were analysed with ANOVA using the MIXED procedure with fixed factor group and repeated factor technical replicate (with unstructured covariance structure). The plasma data was analysed with the MIXED procedure with fixed factors group, repeated factor age (with autoregressive covariance structure), and group × age interaction. Accordingly, the plasma hormone and glucose data of the glucose tolerance test was analysed with the MIXED procedure with fixed factors group, repeated factor time (with autoregressive covariance structure), and group × time interaction. The Tukey-Kramer-correction was used to control the Family-wise Error Rate (referred to as PSupplementary Information


Home monitoring of urine protein is a critical component of disease management in childhood nephrotic syndrome. We describe the development of a novel mobile application, UrApp – Nephrotic Syndrome Manager, to aid disease monitoring.UrApp was iteratively developed by a panel of two pediatric nephrologists and three research engineers from May 2017 to October 2018 for Apple iPhones. App features were devised by this expert panel to support urine monitoring and other home care tasks. Each feature and user-app interface element was systematically reviewed by the panel and iteratively redesigned to remove anticipated use issues. The app prototype was then refined based on two rounds of usability testing and semi-structured user interviews with a total of 20 caregivers and adolescent patients. The analytic function of UrApp in providing a camera read of the urine test strip was compared to a standard urinalysis machine using 88 patient urine samples and three iPhones, model versions 6S and 7. Exact agreement and weighted kappa were calculated between the UrApp and urinalysis machine reads.The final UrApp features include: camera read of a urine test strip; analysis of urine protein trends and alerts for new disease relapse/remission; transmission of urine protein results to providers; education materials; and medication reminders. During the second round of UrApp usability testing, all users were able to perform each of the functions without error and all perceived UrApp to be helpful and indicated that they would use UrApp. UrApp camera results had 97% exact agreement and an overall weighted kappa value of 0.91  compared with standard urinalysis machine interpretation.UrApp was specifically designed to support patients and families living with nephrotic syndrome by supporting disease monitoring and home management tasks. The technically innovative feature that makes this possible is the use of a smartphone camera to read the urine test strip. This novel tool has the potential to improve disease monitoring and reduce management burden.The online version of this article (10.1186/s12911-019-0822-z) contains supplementary material, which is available to authorized users. Idiopathic nephrotic syndrome is one of the most common chronic kidney diseases in children and has a relapsing and remitting course in approximately 80% of the patients , 2. HomeThese monitoring demands are taxing in that families must remember to check the child’s urine, recall results, understand what the results mean, and follow complex medication regimens that are changed often and have significant side effects. Focus group and individual caregiver interviews with parents caring for children with nephrotic syndrome have found that parents report difficulty understanding the significance of urine protein results and knowing when to report and act on significant changes .UnfortunMobile health applications  is a rapidly growing field in disease management. Currently, 77% of U.S. adults own smartphones, and ownership is highest among younger adults  . The ubiIn nephrotic syndrome, there are numerous aspects of self-management that may be facilitated by a mobile app. First, the visual analysis of a urine test strip is subject to human error, including reading the wrong reagent block and erroneous assessments of color. This can be improved through using a smartphone’s camera and CPU to read and analyze test strip results. Apps, with their inherent interactivity, can provide reminders for urine testing, capture the results, and analyze trends to detect disease relapse/remission. Apps can also alert a caregiver to seek medical attention and directly transmit test results to providers. Lastly, apps can provide medication reminders for nephrotic syndrome patients who are on highly complex medication regimens. These mHealth capabilities support numerous components of effective management detailed by the Chronic Care Model: accurate assessment, action-planning, follow-up in self-management support, timely reminders for providers and patients, clinically relevant data tracking, and information sharing between providers and patients in clinical information system design .To support patients and families in self-managment and to improve disease monitoring, we built UrApp - Nephrotic Syndrome Manager, a mobile application (app) which uses the Apple iPhone camera to interpret urine test strip protein results. UrApp also tracks and analyzes urine protein trends, alerts users and providers of significant findings, and provides patient education and reminders. This paper describes the formative development of UrApp beginning with theory-informed design of app features; technical development and analytic validation; and iterative refinement of app features based on usability testing and user-input.In this prototype development research, UrApp was developed from concept to prototype through: 1. theory-informed conceptual design of app features by content experts, 2. technical development including the innovative feature of camera read of a urine test strip, 3. analytic validation of the UrApp camera read compared to a standard urinalysis machine, and 4. iterative refinement of UrApp features based on usability testing and user feedback  and three engineers with expertise in image processing, health app development, and human-mobile interaction  formed the UrApp expert panel. The panel brainstormed and met to conceptualize UrApp specifications to support components of effective management according to the Chronic Care Model  specific6 Unit testing was performed throughout the course of UrApp development.Technical development of UrApp was carried out by the engineers on the expert panel. UrApp was developed for Apple iPhones running the iOS 11 operating system, with Roche’s Chemstrip 2GP®  urine dipsticks, a widely available and validated urine dipstick . The appThe image processing for urine dipstick protein interpretation contains two steps. First, UrApp calibrates the camera to the environment’s lighting conditions by capturing an image of a white sheet of paper and automatically adjusts the camera’s color temperature. Next, an unused Chemstrip 2GP® test strip is imaged to test the calibration of the camera. If the camera produces a color reading within a predetermined tolerance, the calibration is deemed successful and testing may proceed.In the second step, UrApp performs a colorimetric analysis of the urine protein square on Roche’s Chemstrip 2GP® test strip. UrApp uses 30 frames of video (approximately 1 sec of elapsed time) to perform the analysis. A small region of interest (ROI) is extracted from each video frame based on alignment of the test strip to guides shown on the user’s screen  using three iPhones, model versions 6S (one phone) and 7 (two phones) in 88 patient urine samples. Exact agreement and weighted kappa were calculated between UrApp and standard machine reads. Exact agreement between the two iPhone models were also determined. All statistical analyses were performed using SAS Version 9.4 .After the camera read function was validated, the engineers built the first UrApp prototype Fig. . ExaminiThe UrApp prototype was then tested by 10 participants who were either caregivers of patients with nephrotic syndrome < 14 years-old or adolescent patients 14–18 years old. Participants were recruited from the pediatric nephrology clinic at Children’s Healthcare of Atlanta. After consent and assent (where appropriate), users were provided a research iPhone with UrApp already downloaded and asked to perform each panel-identified task (user-app interface functions) and express their opinions of each task by “thinking aloud.” Two research coordinators not involved in the app development observed the users for errors or difficulty performing each task. After the simulated use session, the research coordinators interviewed users for their overall impression of the app, whether they would use the app, and whether they had specific recommendations for improvement. After the interview and testing results, the expert panel met to discuss how UrApp could be redesigned to remove observed use issues and incorporate user suggestions, wherever feasible. After changes were made to each UrApp prototype, another round of user testing and interview was performed with new participants to ensure that all the observed use issues were removed and no additional issues were found.Figure UrApp camera interpretation of urine protein dipstick results showed excellent agreement with a standard urinalysis machine read Urisys 1100). Exact agreement occurred in 253/262 (97%) tests using 88 urine samples and three testers . The UrApp prototype was revised to resolve use issues uncovered during the first round of testing. The second round of testing showed that all 10 users were able to use all prespecified tasks of UrApp without errors. No additional revisions were made to UrApp after the second round of testing.All 20 participants indicated that they “liked” UrApp. Nineteen of the participants indicated that they thought the app would help them with nephrotic syndrome management and indicated that they would use the app. The one participant who did not think the app would be helpful was a caregiver who had already maintained a computer document for several years containing all urine protein results, medications, and symptoms, and who did not wish to switch to a new system of disease tracking. All participants indicated that they liked the features of UrApp Figs.  and 4. AWe described a novel mobile app for childhood nephrotic syndrome monitoring that accurately interprets and tracks urine protein results, facilitates communication between patients and providers, and supports patient education and medication adherence. A key innovative feature of UrApp is the iPhone camera “read” of urine dipstick protein results, which we demonstrated to have high analytic validity. This rapid and accurate interpretation of urine dipsticks ensures that the correct test square is read, removes the need for patients/caregivers to interpret the color change, and removes recall and documentation errors. Our preliminary user testing and feedback results are extremely promising. All participants gave favorable reviews of UrApp, and 19/20 indicated that they found UrApp to be helpful and would use it for nephrotic syndrome management. All 10 users in the final round of testing used UrApp without error and indicated they would be willing to use UrApp.https://www.scanwellhealth.com/). To our knowledge, UrApp is the first mobile app to utilize the smartphone colorimetric analysis of urine test strips specifically for nephrotic syndrome monitoring, supporting the standard-of-care practice of home urine protein monitoring. UrApp has the potential to improve nephrotic syndrome care by providing patient self-management support and a rapid clinical information system, two crucial componenets of chronic disease care as defined by the Chronic Care Model  [The use of smartphone-based image processing capabilities to analyze urine test strips has emerged in recent years as an inexpensive and easy-to-use method to support point-of-care testing in the home environment –16. Currovation) .http://nephroticsyndromeapp.com/). This app allows users to enter urine protein results and generates a report which can then be sent to providers. UrApp provides urine protein tracking and communication with providers, but also offers reading of urine test strips, educational information, and medication reminders.We are aware of one mobile app on the market designed for parents/caregivers of patients with NS that is available for download through Apple App Store and Google Play – Nephrotic Syndrome App by CyberGenie Solutions  versus UrApp on three iPhones. (DOCX 19 kb)


Clint1, Nbea, Smtnl2, Rin3, Dclk1, and Slc24a4.Following ischemic stroke, the penumbra, at-risk neural tissue surrounding the core infarct, survives for a variable period of time before progressing to infarction. We investigated genetic determinants of the size of penumbra in mice subjected to middle cerebral artery occlusion (MCAO) using a genome-wide approach. 449 male mice from 33 inbred strains underwent MCAO for 6 hours (215 mice) or 24 hours (234 mice). A genome-wide association study using genetic data from the Mouse HapMap project was performed to examine the effects of genetic variants on the penumbra ratio, defined as the ratio of the infarct volume after 6 hours to the infarct volume after 24 hours of MCAO. Efficient mixed model analysis was used to account for strain interrelatedness. Penumbra ratio differed significantly by strain  and was associated with 18 significant SNPs, including 6 protein coding genes. We have identified 6 candidate genes for penumbra ratio:  Upon large cerebral artery occlusion, an area of core neural tissue infarcts within minutes while tissue surrounding the core, termed the penumbra, persists in an at-risk state. Within the penumbra, diverse and competing influences mediate whether this tissue will eventually infarct3. Recent large randomized clinical trials have demonstrated the success of intra-arterial clot removal within a specified time interval in rescuing not-yet-infarcted penumbra6. While this time window has increased in recent trials8, there are still restrictive requirements for thrombectomy, with only 7% of patients with ischemic stroke that were eligible for thrombectomy in 20169. Recently, the appreciation of fast and slow progressors after large vessel occlusion has prompted discussion of future trials using imaging data to determine who may benefit from treatment10. This heterogeneity in response to ischemia suggests the time windows used currently may not predict outcome accurately in all comers. Thus, understanding factors that affect the penumbra is important in determining in whom and at what time intervention would be beneficial after large artery occlusion.Ischemic cerebrovascular disease is a leading cause of death globally, accounting for approximately 6.5 million deaths worldwide14, incomplete circle of Willis16, and susceptibility of neurons to ischemia21. However, the question of infarct volume is different from that of the proportion of penumbra to core infarct and is particularly pertinent in the era of endovascular stroke therapy. In this study, 449 male mice spanning 33 strains underwent middle cerebral artery occlusion (MCAO). Each strain contains a unique set of single nucleotide polymorphisms and by comparing the penumbra volume in each strain using a genome-wide analysis, we identified genetic polymorphisms associated with penumbral size.Discrepancy in infarct volume between strains following middle cerebral artery occlusion (MCAO) is associated with both vascular and neural variation. In particular, differences in infarct size across mouse strains is associated with native arterial collateralizationAll animal care, housing, and experiments in this study were approved by and conducted in accordance with the PHS Policy on Humane Care and Use of Laboratory Animals from the Institutional Animal Care and Use Committee of the Harvard Medical Area Standing Committee on Animals. Experiments are reported in accordance with the ARRIVE guidelines.22 and include mice that belong to 6 major groups: 1) Bagg albino , 2) Swiss , 3) Japanese and New Zealand , 4) C57-related , 5) Castle , and 6) DBA  strains23. 449 mice underwent MCAO as previously described17. The number of mice excluded for each strain due to unsuccessful surgery or death for the 6-hour MCAO is shown in Supplementary Table 17. Eight- to 10-week-old mice were used for the MCAO model. Briefly, mice were anesthetized with 1–3% isoflurane and a 7.0 monofilament threaded into the internal carotid artery to access the middle cerebral artery. Successful occlusion of the middle cerebral artery was defined as ≥80% decrease in cerebral blood flow (CBF) as measured by laser Doppler  when comparing the CBF before and after MCAO. Animals were sacrificed by cervical dislocation under 1–3% isoflurane anesthesia. 215 were sacrificed 6 hours after MCAO, and the remaining 234 were sacrificed 24 hours after MCAO. The number of mice per strain at each time point along with age, weight, and mean arterial pressure are documented in Supplementary Table 33 inbred strains from the Jackson Laboratory were included in this study. The mouse strains were chosen for genetic diversity24. The penumbra ratio was defined as the ratio of normalized infarct volume at 6 hours to that of the mean normalized infarct volume at 24 hours across all mice in that particular strain. Data for the 24-hour time point were previously reported17. Strains with a penumbra ratio closer to one were considered to have a smaller penumbral volume as compared to strains with a lower ratio.At the time of sacrifice, the brain was removed from the skull and sectioned into 1 mm coronal slices using a brain matrix. Slices were incubated in 2% triphenyltetrazolium  for 15 minutes at room temperature. Infarct volume was assessed by an investigator blinded to the genetics data at the time of measurement. Infarct volume was obtained by calculating the sum of infarct areas across all slices. Normalized infarct volumes were calculated using the indirect Swanson method: /contralateral volume25. Using these data, we calculated the average number of P1 segments per strain. We used the average P1 per strain value as a surrogate for completeness of the circle of Willis. A Spearman correlation was used to analyze the correlation between circle of Willis completeness with penumbra ratio.We have previously published the results of an analysis of circle of Willis variation between mouse strainshttp://mouse.cs.ucla.edu/mousehapmap/). SNP data were converted to a numerical format with the R package GAPIT26. SNPs that were absent in greater than 10% of strains were excluded. Minor allele frequency (MAF) was calculated using Plink v1.927 (http://pngu.mgh.harvard.edu/purcell/plink/), with the major allele defined as the most frequent allele at each location amongst the strains included in this study. SNPs with a MAF of ≤0.05 were excluded. A total of 104,890 SNPs remained after filtering. The efficient mixed model association method was utilized, which accounts for inbred strain interrelatedness and population structure in mice, to generate a genome-wide association map (emma package in R28). Covariates were excluded on the basis of univariate regression P values > 0.1. These data were then visualized with Manhattan and QQ plots using the qqman package29. P values were adjusted for multiple testing using the Benjamini and Hochberg false discovery rate (FDR) algorithm30. FDR less than 0.05 were considered statistically significant. The genetic location of each SNP was obtained using the UCSC NCBIM37 (mm9) database32. Protein coding genes within 500 kbp of significant SNPs were identified using BiomaRt33 since the majority of enhancers fall within this window34. A network analysis was conducted on genes associated with significant SNPs using StringDB v10.535 . Pathway overrepresentation analysis was performed using the package WebGestaltR36, with modifications, to search for pathways in KEGG37 (10/1/2016 release), Reactome38 (version 2016), and Wikipathway39 (5/10/18 release), for targets of miRNA and transcription factors from MSigDB40 , and for protein-protein interaction networks from BioGRID41.Following volumetric assessment of MCA territory infarcts, two genome wide association analyses were performed using normalized infarct volume at 6 hours and penumbra ratio as the phenotypes. 132,285 SNPs from the NCBIM37 mouse genotype build were obtained from the UCLA and Broad Institute Mouse HapMap project  test was used to assess differences in the infarct volume at 6 and 24 hours. ANOVA of univariate linear regression models were used to assess for inter-strain variability for each outcome. Univariate linear regression was used to screen covariates for inclusion in the mixed model. The ggrepel44, tidyr45, plyr46, dyplr47, broom48, data.table49, reshape250, RColorBrewer51, and ggplot252 packages were used for data preparation and visualization. Raw images of TTC stained brains were not modified with the exception of resizing of the whole image and cropping for purposes of organization into figures. Figures were assembled in Adobe Illustrator .Statistical analysis was performed using R v3.4A total of 449 male mice from 33 strains that underwent MCAO were included in this study, 215 of which were sacrificed at 6 hours and 234 of which at 24 hours   are targets of the Myod1 transcription factor and 3 of the 6 significant genes  are associated with miR-145. Two SNPs – rs31452396 and rs13472659 – were significantly associated with both infarct volume at 6 hours and penumbra ratio, one of which resides within Clint1 (rs13472659).18 SNPs spanning four chromosomes and encompassing 6 protein coding genes were significantly associated with penumbra ratio following FDR correction Fig. , Table 221. Keum et al. used an inbred mouse genome wide approach to identify quantitative trait loci on chromosome 7, Civq1, as an important determinant of infarct volume19. The group subsequently identified Itgal120 and Il-21r21 as candidate genes associated with this region and has also reported additional loci contributing to variation in infarct volume amongst mice53. Our own group has previously reported an integrated analysis using inbred mice and human data to identify human ANGPT1 and ZBTB7C as candidate genes associated with middle cerebral artery infarct volume17.In this study, we investigated the size of the ischemic penumbra at 6 hours by normalizing 6-hour infarct volumes to strain averaged infarct volumes at 24 hours, at which point the infarct has likely completed. Prior studies have identified SNPs associated with infarct volume at 24 hours and have linked those SNPs to genes potentially involved in stroke susceptibility54, by that of the average normalized volume at 24 hours, we generated a ratio of how much tissue had infarcted between 6 and 24 hours relative to the final infarct volume at 24 hours, which is representative of the penumbra. We utilized a proximal MCAO model that has the advantage of avoiding challenges with variations in distal MCA anatomy, but the final infarct volume may be affected by completeness of the circle of Willis16. To address this, we analyzed the association between P1 presence and penumbra ratio. The absence of a correlation suggests the penumbra volume calculated in this study is not dependent on circle of Willis completeness. The significant variation in the penumbra ratio across strains suggests that there is a genetic predisposition to penumbral size. This is, perhaps, not surprising, given the variation in infarct volume across strains18.However, the question of penumbra size has not previously been addressed. By dividing the normalized 6-hour infarct volume, which has previously been used to study penumbral gene expressionCtif gene) had been previously identified by our group to be associated with infarct volume at 24 hours17 whereas the remainder were unique to the 6-hour time point. It is plausible that the determinants of the size of the initial infarct core at 6 hours is not associated with that of the final infarct volume at 24 hours which is a function of both the core and the penumbra, therefore we would expect some of the associated SNPs to be unique to each time point. Of note, we also previously found the Ctif gene to be significantly associated with infarct volume in human stroke patients17. CTIF has been hypothesized to function as a translation initiation factor in the pioneer phase of translation, which appears to be the predominant mechanism by which mRNA is translated in the setting of hypoxia55. It follows that CTIF abnormalities could contribute to ischemic injury via decreased pioneer translation capacity.Of the 24 significant SNPs associated with infarct volume at 6 hours, one . The majority of known SNPs were found to be in introns, which suggests they may be involved in regulation of expression rather than in structural differences of the resulting protein. Two of the SNPs significant for penumbra volume were also significant at the 6-hour time point, rs31452396 and rs13472659, the latter of which is in Clint1. While rs13472659 is a synonymous coding SNP, it was found to be significant in two separate analyses is suggestive of its relevance to cerebral ischemia. CLINT1  is enriched in the brain and functions in the endocytosis of clathrin coated pits56. Clint1 mutant zebrafish have a phenotype mimicking psoriasis, suggesting it may function in mediating inflammation57. Genetic polymorphisms that result in dysregulated inflammatory responses to ischemia could potentiate the inflammatory penumbra, a concept proposed to account for secondary brain injury outside the initial ischemic territory58. Furthermore, zebrafish with Clint1 mutations have increased matrix metalloproteinase 9 (MMP-9) expression59. MMP-9, a proteolytic enzyme that normally functions in extracellular matrix remodeling that has been shown to function in blood brain barrier function, increases the degree of ischemic cerebral injury following stroke63. Increased permeability of the blood brain barrier enables immune cell extravasation and may exacerbate secondary brain injury. Another protein identified in this study, RIN3, a RAB5 guanine exchange factor, functions in early endocytosis64 and acts as a negative regulator in mast cells65. Mast cells in turn mediate early peri-infarct inflammation following MCAO in rats via blood brain barrier dysfunction and subsequent inflammatory cell extravasation66, potentially contributing to the inflammatory penumbra.To address the question of penumbral size, we performed a genome-wide association analysis with penumbra ratio as the outcome. The 18 SNPs significantly associated with penumbra volume were located within with 6 protein coding genes  located within the Dclk1 locus are overexpressed following MCAO in rats, with a possible role in the epigenetic modifications in the setting of ischemia70. However, more research is necessary to better elucidate the function of these proteins following stroke.Slc24a4  encodes a sodium/potassium/calcium exchanger and is immediately 5′ to Rin3 that is associated with lipid metabolism71. Slc24a4 knockout mice were found to be anorexic, with constitutively activated paraventricular nucleus neurons hypothesized to be secondary to elevated Ca2+ signal and melanocortin 4 receptor (MC4R) activity72. Melanocyte-stimulating hormone has been shown to have a dose-dependent neuroprotective effect via MC4R signaling following 10 minutes of bilateral carotid occlusion in gerbils via decreased TNF-α and IL-6, as well as decreased MAPK mediated apoptotic pathways73. However, this study specifically demonstrated neuroprotection in the hippocampus following global cerebral ischemia and did not investigate ischemic penumbra following stroke. Nevertheless, these findings illustrate a potential mechanism for how SLC24A4 mediates penumbra size. Additionally, SLC24A4 is part of a larger family of ion channels including NCKX2, which has been associated with ischemic brain injury74.75. JNK isoforms facilitate neuron cell death in settings of ischemic stress76, and JNK inhibition is neuroprotective following MCAO77. However, the function of SMNTL2 is still largely uncharacterized and future study is needed to define its role, if any, in neuroprotection.SMNTL2 is a downstream target of c-Jun-N-terminal kinases (JNK), predominantly in skeletal muscle although it is expressed in other tissue as well78 as well as within the central nervous system79. Additionally, NBEA loss is associated with both autism and abnormal platelet morphology secondary to large dense core vesicle secretion80. Yet, the role of NBEA following stroke is not clear. Similar to SMNTL2, future research targeted at NBEA in stroke is needed.Finally, NBEA is a scaffolding protein with a Beige and Chediak-Higashi (BEACH) domain concentrated in neurons that functions in synaptic transmission at the neuromuscular junction81, was identified as a regulator of the expression of 4 of the 6 significant genes—Dclk1, Slc24a4, Nbea, and Smtnl2. Myod1’s canonical role is to induce differentiation of fibroblasts to skeletal muscle cells, but it has also been shown to be expressed in the brain82 and may have a role in neural development83. Moreover, Dey et al. found loss of Myod1 catalyzed sonic hedgehog (Shh) driven neoplastic growth in medulloblastoma84. In the setting of ischemia, Shh activity increases in murine neural progenitor cells and neurons85. Specifically, the Shh pathway is active in cortex and striatum adjacent to the injured region86. Blocking the Shh pathway potentiates brain injury87 and Shh agonists have been found to neuroprotective after stroke88. The role, if any, of the 4 target genes identified here in Shh signaling is unclear.To further investigate potential interaction amongst these candidate genes, we performed a pathway overrepresentation analysis. Myod1 (MyoD), a helix-loop-helix transcription factor involved in myocyte differentiation89. MiR-145 has also been implicated in ischemic stroke. Using an oxygen-glucose deprivation model with primary neuronal cultures, Zheng et al. found overexpression of miR-145 lead to decreased expression of Aqp4, the gene encoding aquaporin 4 (AQP4), in astrocytes resulting in a protective effect in the setting of ischemia90. MiR-145 was also found to be overexpressed in the cortex after transient (1 hour) MCAO and subsequent reperfusion in rats by Dharap et al. and antagonism of miR-145 led to increased levels of peri-infarct superoxide dismutase as well as decreased infarct volumes91. Moreover, miR-145 expression was found to be elevated within 24 hours of ischemic stroke in human peripheral blood and its expression correlated with the volume of the ischemic infarct as well as with NIH stroke scale score92. Furthermore, CLINT1 has also been shown to be inhibited by miR-145, resulting in cell death in a bladder cancer model93 and, using TargetScan94, SLC24A4 is a predicted target of miR-145 (http://www.targetscan.org/cgi-bin/targetscan/vert_70/targetscan.cgi?mirg = hsa-miR-145). The finding of multiple genes associated with penumbra ratio and miR-145 suggest genetic polymorphisms in these genes may be modifying a common pathway regulating the rate of penumbra infarction (Supplementary Fig. Interestingly, the micro RNA miR-145 is associated with multiple proteins identified in the penumbra analysis. DCLK1 post-transcriptionally regulates miR-145 in pancreatic tumor zenografts and DCLK1 knockout mice were found to have increased expression of miR-145This study has important limitations warranting further discussion. First, we analyzed 6-hour infarct volumes in 215 mice from 33 unique strains, normalized to another 234 mice with 24-hour infarct volumes. Importantly, this method of estimating penumbra volume is a rough estimate as penumbra was not directly measured. These data should be therefore be considered carefully in the context of an estimated penumbra volume. The relatively small sample sizes may limit the statistical power of our study. The ratio of infarct volumes at six over 24 hours utilized approximate penumbra size but is not a direct measure of at-risk tissue and may result in an underestimation of the genetic effects. In addition, it is possible that other variations in blood vessel anatomy not examined in this study could impact the penumbra. It is also possible that serum pH, glucose, and other metabolites not measured in this study could affect penumbra ratio. Moreover, there are varying degrees of occlusion that can confound the results. In addition, the model used in this study, MCAO, may have limited applicability to human patients as it involves a single mechanism and does not capture the different stroke etiologies or the comorbidities commonly found in human patients presenting with ischemic stroke. Although we have minimized the number of animals necessary to achieve sufficient statistical power, a strain survey inherently involves a large number of animals. Nevertheless, these data provide an advantage over human GWAS in that a controlled experimental model, such as the MCAO, is possible whereas it is not in human subjects. Future mechanistic studies are required to investigate the role of the candidate genes identified on penumbral size.The size of ischemic penumbra is paramount in determining the extent of salvageable brain following stroke. While prior studies have investigated genetic polymorphisms associated with infarct size, this is the first genome wide association study in mice specifically investigating penumbra size. We report 18 significant SNPs in 6 protein coding genes, including proteins potentially involved in the inflammatory penumbra and neuronal susceptibility to ischemia, which fundamentally make sense but require more research to better define. A better understanding of the genetic underpinnings of penumbral size could inform more personalized application of acute stroke treatment as well as provide the foundation for potential novel therapies.The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.Supplementary Tables and Figures