A few studies have assessed the association between omega-3 polyunsaturated fatty acids (n-3 PUFA) and cognitive impairment (CI) in very old adults. The aim of this study was to evaluate the effect of a multinutrient supplementation rich in n-3 PUFA on the cognitive function in an institutionalized ≥75-year-old population without CI or with mild cognitive impairment (MCI). A multicenter placebo-controlled double-blind randomized trial was conducted between 2012 and 2013. Cognitive function was assessed at baseline and after one year using 4 neuropsychological tests. Nutritional status was assessed using Mini Nutritional Assessment (MNA). Interaction between Mini-Mental State Examination (MMSE) score and nutritional status were analyzed using linear regression models. A total of 99 participants were randomized to receive placebo or pills rich in n-3 PUFA. After 1-year follow-up, both groups decreased their MMSE score . The memory subscale of the MMSE showed an improvement  in the intervention group against a worsening in the control group . Patients at intervention group with normal nutritional status (MNA ≥24) showed an improvement in the MMSE  against a worsening in the group with malnutrition (MNA<24) . Supplementation with n-3 PUFA did not show an improvement in the global cognitive function in institutionalized elderly people without CI or with MCI. They only suggest an apparent improvement in memory loss if previously they were well nourished. According to the World Health Organization, 47.5 million people worldwide have dementia and 7.7 million new cases are developed every year . DementiDiet is an important area to explore preventive interventions. There is growing evidence showing that certain nutritional factors could be associated with cognitive impairment (CI) ,3 and thResults from epidemiological studies have suggested that diets high in PUFAs and n-3 PUFA, docosahexaenoic acid (DHA) and Eicosapentaenoic acid (EPA), might reduce the risk of CI and Alzheimer disease (AD) . HoweverTherefore, the aim of this study was first to identify if a multivitamin supplement including n-3 PUFA (Docosohexanoic Acid (DHA) and Eicosapentaenoic Acid (EPA) produces beneficial effects in the cognition of elderly institutionalized patients without CI or with mild cognitive impairment (MCI). And second, we explored a possible interaction between the supplements and the prior nutritional status.This was a multicenter, randomized, double-blinded, and placebo-controlled trial, study. Participants were randomized to take either one capsule of multinutrients rich in n-3 PUFA, 3 times a day (350 mg n-3 PUFA/capsule) or placebo (gelatin capsule). Randomization was simple with sequentially numbered containers. Patients and researchers in charged to perform the assessments at baseline and after 1 year were blinded to the intervention, MR-C generated the random allocation sequence and JB, MP, TA, JIG, CG and BI enrolled and assigned participants to intervention.Inclusion criteria were: 1) age ≥75 years, 2) a score between 1 to 3 on the Global Deterioration Scale (GDS) –31. ExclBefore each patient was included in the study we checked cognitive status using two scales the MMSE and the fast GDS. To include the patients in the study they have to had a fast GDS ≥ 3 (e.g. patients without dementia or with mild cognitive impairment). After this, patients were evaluated and if they accomplished all the inclusion criteria, and they did not have any exclusion criteria they were asked to sign the informed consent, and if they agree they were included in the study. Recruitment of patients started January 2012, and finished December 2012. All patients were followed-up for 1 year. Therefore, follow-up time finished in December 2013.th, 2011). All study participants provided written, informed consent. The trial was registered in ClinicalTrials.gov with the name “Dietary supplement for the prevention of cognitive decline in a very elderly population” and number: NCT01817101.All participants were institutionalized at one nursing home group with 3 centers in Navarra (Spain) one in Mutilva and two of them located in Pamplona. Recruitment of patients was done evaluating all the residents of the 3 included centers and including all those who accepted to participate and filled all the inclusion criteria as stated before. The AMMA Navarra Institutional Review Board approved the trial protocol  [Cognitive status was assessed at baseline and after one year of intervention using several scales: 1) The MMSE, validated test in screening for dementia and one of the most used both clinical practice and research, despite its limitations is considered a test of choice for longitudinal monitoring of patients –33. It wvo”(MEC)  that is vo”(MEC) –31. 3) Tvo”(MEC) . The cutvo”(MEC) . This tevo”(MEC) . The cutThe nutritional status was assessed using the Mini Nutritional Assessment (MNA) test . The MNAWe calculated 38 participants randomized to each group to detect a mean difference between the intervention group and the group control in 3 points on the MEC scale , referriWe used unpaired Student’s t test for continuous variables and Chi squared (χ2) test for categorical variables to compare baseline and 1-year follow-up characteristics between intervention and control groups. To assess differences in 1-yr changes between intervention and control groups, since some of the variables were not distributed normally we calculated p values using also non-parametric Mann-Whitney U test. To assess differences between 1-yr and baseline cognitive status variables we used Wilcoxon test.Multivariate linear regression models were used to analyze difference in score levels between the intervention and control group. Covariates included in these models were: cognitive stimulation ; physical activity ; educational level (3 categories), and occupation (3 categories), automatic dummies for educational level and occupation were created and we used the first category as the reference.Analyses were also stratified according to the nutritional status using the MNA score. We evaluated the interaction between cognitive level determined by MEC scale and the nutritional status. Adjusted means of MEC memory scale using ANCOVA models were calculated for baseline and 1-year of follow-up in control and intervention groups stratified by nutritional status.As secondary analysis, we calculated effect size (Cohen’s d) of mean differences of cognitive scales after 1 year of follow-up.All p values presented are two-tailed; p < 0.05 was considered statistically significant. Analyses were performed using STATA/SE version 12.1 .The results are presented in accordance with the CONSORT statement –41.The flow chart of participants is shown in The baseline characteristics of the participants according to study group are shown in There were no adverse effects reported in any of the study groups, and the compliance was 100% in both groups. None of the participants reported to change their usual routine during the study. In nursing homes the regime of life is very stable  this allowed to ensure the compliance of the treatment and also avoid changes in food intake or other type of changes.After 12 months of intervention the cognitive status showed no significant improvement in any of the study groups . All parWhen we calculated effect size Cohen’s d index of mean differences of cognitive scales after 1 year of follow-up results were consistent. All of them presented an effect size small or very small  .Similarly, we found none statistically significant differences between groups when we adjusted for potential confounding factors .We stratified participants in 2 groups according to their nutritional status, based on the MNA score, considering those patients with a score ≥24 as well-nourished and those under this score as at risk of under nutrition. We observed a statistically significant improvement in the memory subscale of the MEC scale, in those patients in the intervention group with a MNA ≥24  .All the participants tolerated well the supplement or placebo without appreciating any side effects. The main complains were the difficulty to swallow the capsules (4 subject in the group intervention and 3 in the placebo) and the excessive number of pills (11 in the group intervention and 14 in the placebo). The compliance of the essay was 100% in both groups.The results of this study found that a multinutrient supplement, whose main component is n-3 PUFA, does not improve the global cognitive function after 1 year of supplementation. Only, it was observed an apparent reduction in loss of memory in institutionalized patients aged ≥75 years old without DC or with MCI, specially among those previously well-nourished.Previous studies , 42–47 aThe results for the main analysis showed certain favorable effect in the MEC, Pfeiffer, verbal fluency and the GDS of Reisberg in the intervention group, but none of them achieved neither clinical nor statistically significant differences between study groups, this might confirm the conclusions of some studies that did not find beneficial effects in the prevention of the CI with n-3 PUFA supplements –52. NevePrevious studies provided evidence that individuals with established AD do not get any benefit from this type of intervention –54 and yDietary patterns can better predict the risk of CI than individual nutrients or foods . A recenIn our study we found that those in the intervention group and well-nourished showed a significant improvement in memory. However, this improvement was not apparent in those who had worse nutritional status. This might be explained because the possibility of better nutrient synergies between the supplement and a good nutritional status. Moreover, malnutrition in elderly is associated with worse health, a decrease in the functional and physiological reserve and fragility, these might conduct to a worse reaction to adverse factors or diseases including cognitive function –64.Only few intervention studies have included patients older than 65 years , 51–53. Two bigger studies found that higher adherence to supplementation intervention was associated with lower AD incidence , and theIn general, using a multinutrient supplements limits the possibility to attribute only to one of the components all the beneficial effects, even though, the principal component was the n-3 PUFA, however this fact allows to explore potential synergies between nutrients that, can mimic what it is found on a balanced diet –16. It iA limitation of this study was the small simple size, another limitation was the heterogeneity of the population included in our study including patients without memory impairment and patients with MCI. Probably a longer treatment period may be necessary to demonstrate the efficacy of the supplementation. Finally, the only positive finding in memory loss was based on a secondary analysis carried out to test multiple comparisons. We cannot rule out the existence of this result by chance. Some of the strengths of our study can be also considered as limitations, for example the type of studied population, in our study we included older patients institutionalized in a nursing home, this allowed to ensure the compliance of the treatment and also avoid changes in food intake or other type of changes because its regime of life is very stable  but this can be consider as a limitation in our study in order to extrapolate our results to general population that lives in the community. On the other hand, using a multinutrient complex as intervention treatment might cause interactions between the different nutrients, and also limit the possibility of attributing the effects to specifically one of them, although the main component was n-3 PUFA.Due to lack of statistical power, these observations can be considered only as preliminary results. More studies are needed with older people, applying specific tests of memory and with greater number of participants to confirm our results.The results of this randomized, double blind, placebo-controlled trial found no improvement in global cognitive function during one year of supplementation with a multinutrient supplement whose main component is the n-3 PUFA. They only suggested an apparent improvement in memory loss in institutionalized elderly people without CI or with MCI if previously they were well nourished. More studies similar to this are needed, to confirm our results.S1 Table(DOC)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 File(DOC)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(DTA)Click here for additional data file.


The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation. Bioelectrical signals were recorded while participants were driving in a car simulator while avoiding potential collisions by performing emergency braking. In addition, participants were subjected to stress, workload, and fatigue. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. Results showed significant recognition of emergency braking intention which was on average 71.1% for SVM and 71.8% CNN. In addition, the classification accuracy for the best participant was 80.1 and 88.1% for SVM and CNN, respectively. These results show the feasibility of incorporating recognizable driver's bioelectrical responses into advanced driver-assistance systems to carry out early detection of emergency braking situations which could be useful to reduce car accidents. According to the World Health Organization (WHO), every year traffic accidents cause the death of 1.3 million people around the world, additionally, about 50 million people suffer from a disability caused by accidents related to cars WHO, . By 2020Current driving assistance systems do not use driver's information such as eye or head movements, hand sweating, or hand pressure on the steering wheel, although this information might be correlated to fatigue or drowsiness  signals. The pioneer work of Haufe et al.  showed tThis work proposes the detection of emergency braking intention using driver's electroencephalographic (EEG) brain signals recorded in a simulated driving environment that includes absence and presence of stress, workload, and fatigue. The rationale is to include these cognitive factors that may affect and delay the emergency braking response. In the experimental task, participants had to naturally drive a vehicle and to perform unexpected emergency braking to avoid crashing with another vehicle. This task was repeated under different cognitive situations including combinations of stress, workload, and fatigue. EEG signals from the driver were recorded in order to evaluate the recognition between braking intention and normal driving through a conventional classifier as support vector machines (SVM) and a novel classifiers model as convolutional neural networks (CNN). In recent studies, CNN models was used to classify mental tasks from EEG signals in a controlled experiment with successful results  to know how to drive a vehicle either with manual or automatic clutch; (ii) to have a valid driver's license; (iii) to have no medical history of neurological and/or psychiatric diseases; (iv) to have normal or corrected-to-normal vision. The protocol was approved by the Comité de Ética en Investigación de la Escuela de Medicina del Instituto Tecnológico y de Estudios Superiores de Monterrey and the Comité de Investigación de la Escuela de Medicina del Instituto Tecnológico y de Estudios Superiores de Monterrey. All subjects gave written informed consent in accordance with the Declaration of Helsinki and they were duly informed about the goals of the research.Seven right-handed male students (age range 20–26 years old) from our faculty voluntarily participated in this study. All participants approved the following inclusion criteria: (i) the state of the guide vehicle rear brake lights or simply “LIGHT” ; (ii) the brake pedal level of the participant's vehicle or simply “BRAKE” . These vehicular signals were recorded at a sampling frequency of 50 Hz.The driving system consisted of a set of gas, brake and clutch pedals, a steering wheel and a gear lever (G27 Logitech Racing Wheel) assembled in a car simulator rack, a 19 inches flat-screen to visualize the driving environment and a personal computer to manage and control the execution of the experiment and the acquisition of signals  and electromyographic (EMG) activity was recorded during the execution of the experiment. These bio-electrical signals were acquired, amplified, and digitalized using the 8-channels wearable device for recording of human physiological signals BIORADIO PG . Seven EEG signals were recorded from the frontal, central, and parietal lobe  in accordance to the 10/10 international system   of ~30 min each where the participant had to drive continuously. In each session, 120 emergency braking situations were presented, therefore, 480 emergency braking situations were recorded in total per participant. The rest period between sessions was ~10 min. Each session was composed of eight blocks and each block contained 15 emergency braking situations from the same experimental combination. Each block had a duration of ~3 min while the separation between blocks was ~1 min. To avoid habituation, the order of blocks in each session was pseudo-random. Figure LIGHT and BRAKE signals were upsampled while EEG and EMG signals were downsampled, in both cases to a sampling rate of 250 Hz. EEG signals were lowpass filtered at a cutoff frequency of 45 Hz using a 2nd-order zero-phase shift Chebychev-type filter and then common average referenced (CAR). EMG signal was bandpass-filtered from 1 to 90 Hz using a 2nd-order Chebychev-type filter and stop-band filtered at 60 Hz to reduce power line interference. The shift from 0 to 1 of the LIGHT signal was used as reference to identify the time instant of each stimulus  while the first post-stimulus time for which BRAKE ≥ 0.01 was used as reference to identify time instant of each response . The time instants of all stimuli were used as reference to trim the signals into consecutive data segments that span up to 2 s after the stimulus  non-braking epochs or normal driving: signals of 1.5 s that are more than 3 s apart from any stimulus and/or response. These epochs do not include braking situations at all and do not overlap with post-stimulus or pre-response epochs. (ii) pre-response epochs: signals in the time interval  s where the reference t = 0 corresponds to the response. These epochs contain information that exclusively precede the deflection of the brake pedal; (iii) post-stimulus epochs: signals in the time interval  s where the reference t = 0 corresponds to the stimulus. These epochs contain information immediately subsequent to the switching-on of the rear brake lights; This procedure resulted in three different datasets. The dataset of post-stimulus epochs were employed to study the emergency braking situations while the datasets of pre-response and normal driving epochs were used to distinguish emergency braking intention from normal driving using brain signals.For each data segment, three epochs of 1.5 s were extracted Figure : (i) nonTo study the emergency braking situations, the dataset of post-stimulus epochs was employed to assess:Braking reaction time (BRT): This analysis measures the time required to press the brake pedal once the guide vehicle's rear brake lights turned on  the EMG signal was high-pass filtered at of 10 Hz using a 2nd-order Chebyshev-type filter; (ii) the absolute value of the filtered signal was then computed; (iii) the Hilbert transform  the magnitude of the Hilbert transformed signal was used as the muscle-based leg movement.EMG-based Leg movement (LEG): This analysis shows the right leg movement (at the muscular level) that is performed by the driver to press the brake pedal. LEG was computed as follows:  and Convolutional Neural Network (CNN). For this, pre-response and normal driving epochs were used.X ∈ ℝM×N, or equivalently, they were concatenated to construct the feature vector x ∈ ℝM·N)×1.Time-domain features of the EEG signals were computed to recognize emergency braking intention from normal driving. The EEG signal of each electrode (duration of 1.5 s) was divided in 10 consecutive intervals of 150 ms with no overlapping and the arithmetic average was computed for each interval. The values of all electrodes were used to construct a matrix of features  A Convolutional Neural Network (CNN) was employed to discriminate between emergency braking intention from normal driving. A CNN is a special type of supervised deep learning based classification algorithm , which together gradually transform an input map up to obtain class probabilities. The equation that describes the convolutional operation is:K of size m × n is convoluted  with input map of size I and sum to bias b to construct output feature map S. Convolution and pooling layers are typically connected one by one with the aim of transforming an input map into many feature maps, thus, their effect is to perform an automatic feature extraction  for the classification since this algorithm has showed good performance in applications with EEG signals  to initialize estimated parameters randomly (only for the training step 1); (ii) a batch data is sampled from training data (batch size is 20% of training data); (iii) classification model is fed with the batch data; (iv) obtaining the prediction outputs of the classification model; (v) comparison of the predicted outputs with the actual labels (to find the error trough a cost function); (vi) cost function optimizing; (vii) updating estimated parameters; and (viii) the rest of the training set was used to evaluate the model performance for each training step . The learning rate was set to 0.005 and the cross entropy was used as the cost function.The implementation and training of both classification algorithms relied on the TensorFlow software library  graphs and area under ROC curve (AUC). ROC curve provides an optimal visualization of a classifier performance and allows to compare the performances between different classifiers. ROC curve is useful to show skewed class distribution and unequal classification error costs Fawcett, . ROC graWe first show an analysis of the braking reaction time under different cognitive states. Then we present results on the detection of braking intention under these different cognitive states which include stress, fatigue and workload.This section analyses the braking reaction time (BRT) under different cognitive states in order to assess how the reaction time is affected by stress, fatigue, and workload.p < 0.05, Kruskal-Wallis test), which indicates different BRT across the experimental combinations. To better explore the effect of stress, workload and fatigue on the BRT, the distribution of BRT in combination Co (which does not include any factor) was individually compared with the other distributions .The BRT averaged across-all-participants was 718 ± 162 ms (minimum 452 and maximum 1192 ms). This is congruent with previous studies that have reported BRT in the range of 720 and 1250 ms , the median of BRT is slightly greater in presence of stress (664 ms) than in absence of it (660 ms), however, no significant differences are found between the two distributions . This shows that although BRT is marginally higher in emergency braking situations with stress, these differences are not significant. This shows that the influence of stress increased BRT in 2%, however, this increment was not significant. For the case of BRT with absence and presence of fatigue (Co and Cf), the distributions present no significant differences  and the medians are similar . Those results indicate that the presence of fatigue did not presented BRT significantly different than in the absence of it. The reason of why stress and fatigue did not resulted in different BRT may be due to the fact that participants did not perceive the high-intensity ambulance sound or the afternoon session as annoying, dangerous, and/or exhausting situations since the experiment was performed in a fully-controlled environment. This is consistent with the results obtained with the NASA-TLX index, where all participants expressed similar levels of mental, physical demand, effort, and frustration in the experiments carried out during in the afternoon and the experiments executed in the morning.For the case of BRT with absence and presence of stress (Co and Cw), the distributions are significant different  and the median of BRT is greater in presence of workload (708 ms) than in absence of it (660 ms), which shows that the workload significantly increases the BRT. In this case, the BRT was 7% higher for emergency braking in the presence of workload than in the absent of it. This shows that the execution of a secondary task (as the workload was induced in our experiments) during emergency braking situations increases the time needed to press the brake pedal. The processing of additional cognitive and peripheral tasks required by the additional workload may explain this delayed response.Nonetheless, for the case of BRT with absence and presence of workload  and greater median than Co solely when they include workload . Interestingly, the BRT of experimental combinations that include simultaneously stress, workload, and/or fatigue was also significantly different and higher only in combinations with presence of workload. This confirms the increased BRT due to the execution of a simultaneous task. To sum up, these analyses show that, in our experimental setting, the BRT does not increase in emergency braking situations that include stress and/or fatigue, however the BRT is significantly different and higher in emergency braking situations with workload.In addition, distributions of BRT that include two or three factors present significant differences . Both classifiers in all participants (except for participant P3 with the SVM) presented distributions of classification accuracy that were significantly different and higher than accuracychance . Participants P1 and P3 presented distributions of classification accuracy that were significant different and greater for the CNN than the SVM . For participants P2 and P6, the SVM presented distributions of classification accuracy that were significant different and greater than the CNN . Finally, no significant differences were found between the medians of the classification accuracy distributions of the SVM and CNN  in participants P4, P5, and P7. Figures P1 to P7). Both classifiers in all participants presented curves above the threshold. These results are also summarized in Table mean ± std of participants P1 and P3 was greater for the CNN  than for the SVM . On the contrary, the mean ± std of classification accuracy in participants P2 and P6 was greater for the SVM  than for the CNN (70.5 ± 2.5 and 60.2 ± 2.3 respectively). The remaining participants, P4, P5, and P7, presented similar average accuracy in both classifiers . Note that in all cases, the average classification accuracy is above accuracychance. Finally, the grand-average across-all-participants is similar for the two classifiers, 71.10 ± 8.69 for the SVM and 71.83 ± 9.60 for the CNN. To compare the performance of the ROC curve of the two classifiers, the AUC score was obtained for each participant and reported in the Table P3 and P6) which is considered a discriminatory standard with AUC average of 0.66. CNN presented values above 0.6 in all participants with AUC average of 0.77. To sum up, these results show significant recognition of emergency braking intention irrespective of the fact that the emergency situations were performed while driver's experienced or not cognitive factors as stress, workload, and fatigue.Figure In a second classification analysis, the detection of emergency braking intention was assessed by gathering the data from all participants (training data size = 4674 and test data size = 1556). The classification accuracy was on average 54.5 ± 0.9 for the SVM, while it was 68.4 ± 1.3 for the CNN. On the one hand, these results show that the accuracy is 13.9% greater for CNN than SVM. On the other hand, the accuracies are lower than when using the data of each participant individually . Indeed, the use of the data from all participants reduces accuracy in 16.6 and 3.5% for the SVM and CNN, respectively. Note that the across-participants driving patterns discrepancies and differences in the brain signatures may explain this different performance.accuracychance  in four out of the seven participants , while the SVM presented distributions of classification accuracy that were significantly different and higher than accuracychance  in three out of the seven participants . In addition, the classification accuracy was on average 54.1 ± 6.7 for the SVM, while it was 59.2 ± 6.0 for the CNN. These results show higher classification accuracies for CNN than SVM, however they are lower than in the case of learning a user-specific classifier  and in the case of using of the data from all participants . Figures leave−one−outparticipant 1 and 5 and for CNN in leave−one−outparticipant 5). To compare the performance of the ROC curve of the two classifiers, the AUC score was obtained for each leave-one-out participant and reported in the Table P2. CNN presented values above 0.6 in three leave-one-out participants . To sum up, both classifiers have similar accuracy values. However, CNN presented higher AUC values than SVM which indicates that CNN was more sensitive in predicting the braking intention than SVM classifier. The transferability results show a lower performance than personalized individual classifiers. From our point of view this makes sense since different people deal in different ways with different cognitive states. For example, previous works on stress detection show also better performance on personalized individual classifiers  signals to recognize the intention to perform an emergency braking in driving conditions where the drivers experienced realistic cognitive states such as stress, workload, and fatigue. The contribution of this paper is two-fold. First, our classification results indicate the possibility of detecting with high probability the emergency braking intention, which precedes the moment at which the mechanical activation of the brake pedal becomes observable. This can be used to incorporate this detection into driving assistance systems in order to avoid accidents by, for example, reducing the final braking reaction time to gain braking distance, or by maneuvering the car automatically in a sudden situation. Second, this works considers cognitive states that may negatively affect the driving. In particular we carried out experiments including stress, workload, and fatigue during the driving task. Notice that this work considers the early detection of braking intention form EEG irrespective of the cognitive state(s) experienced by the drivers. For this reason, we lumped the data of all cognitive states to carry out the classification between braking intention and normal driving. This is a novelty in our work as we are considering that the drivers experience cognitive states during driving rather than being driving in fully controlled and relaxed environment. The presented classification results provide evidence of the possibility of using the brain signals to anticipate the braking action despite the driver is experiencing typical cognitive factors that are known to negatively affect driving performance. The proposed CNN provided significant classification between emergency braking intention from normal driving. It is not possible however to compare these results with previous studies due to those related works were performed in different experimental settings without contemplating the potential effect on brain processes of different driver's cognitive states cause by fatigue, stress, workload during driving a car. It is important to say that the classification accuracy rates presented herein and the cognitive states may differ in real driving situations, thus, more research is still needed to assess the detection of braking intention during realistic driving environments. A difficulty here would be to avoid the induction of cognitive states to consider real stress, workload, and fatigue during driving. In addition, execution of experiments in real driving situations should consider critical safety procedures that are not present in simulated environments. Regarding the computational algorithm required to recognize between braking intention and normal driving, it is important to consider that an extended set of instances is advantageous; however, this requires longer experiments to record more braking situations. Finally, it is also required to study the effect of artifacts as blinking, head movements, limb movements, etc., which will be embedded in the recorded EEG signals during real driving, since they may spoil the early detection of braking intention. The future direction in this research involves, first, to estimate the total time prior to the activation of the mechanical brake pedal that can be saved, as this time define the maximum gained braking distance, and second, to validate our system in real driving scenario to assess whether realistic conditions may affect the signatures in the brain signal that allow the detection of emergency braking intention (Martínez et al., JA and LH conceived and designed the experiments. LH performed the experiments. JA, LH, and OM analyzed the data. All authors participated in writing the document.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Keratoconus is a common corneal ectasia that leads to progressive visual impairment. Numerous studies have shown abnormal protein expression patterns in keratoconic corneas. However, the specific mechanisms causing this disease remain ambiguous. This review aims to provide an update on morphological studies of the keratoconic cornea, relate these early studies with current findings from proteomic, biochemical and cell culture studies and to postulate possible pathogenic pathways. Another association that has been made was atopy, and at least one study has shown elevated serum IgE levels in KC patients when compared to non KC patients [FOXO1and FNDC3B. FOXO1 acts by regulating the TGF-β pathway, which by itself may be implicated in the pathogenesis of KC [Keratoconus (KC) is a common ectasia of the cornea , 2. Its patients . The cenpatients Specificais of KC .Additionally, evidence for the role of genetics in KC comes from prior evidence of familial aggregation of KC: unaffected relatives of KC patients may have abnormal videokeratography indices . This fi2The cornea is an avascular tissue that provides most of the refractive power of the eye . It is cThe human corneal epithelium is approximately 50 microns thick and consists of 4-6 layers of non-keratinized stratified squamous epithelial cells. Its basement membrane is composed mainly of collagen type IV, laminin and entactin; and the major proteoglycan is perlecan .Bowman’s layer is an ill-defined layer separating the epithelial basement membrane from the stroma. It is 8-12 microns thick and its collagen fibrils interweave into the stroma .The stroma which comprises the bulk of the cornea is approximately 500 microns thick and is a highly ordered network of collagen fibrils and extracellular matrix. Collagen is the most common protein in the stroma, and type I collagen is the most common of these collagens. The major stromal proteoglycans are decorin, biglycan, lumican, keratocan and osteoglycin/mimecan .3All corneal layers may be affected in KC. Early histological studies and then confocal microscopy studies have shown consistent findings: epithelial changes were more apparent in severe KC, for instance: the cells were found to be elongated or spindle shaped in severe KC patients , 13. TheEvidences for structural abnormalities have also been demonstrated in electron microscopy studies: proteoglycans were shown to be more scant in some areas of KC corneas and the orientation of the collagen-proteoglycan network was also abnormal with the proteoglycans oriented parallel to the collagen fibrils instead of perpendicular, reflecting an alignment pattern more similarly associated with tendons rather than the cornea . In an X44.1Protein studies of keratoconus originate in the 1980s: these early studies that used SDS-PAGE electrophoresis showed that corneas inflicted with KC have a reduced amount of collagen . A priorAdditional earlier evidence suggested that the culprit in KC is perhaps collagen degradation due to over-expression of gelatinases -32. OtheOther studies on molecular changes have suggested that inflammation may play a role in KC despite the fact that clinically, KC does not appear to be an inflammatory eye disease. In a pilot study that used ELISA techniques KC patients were shown to have elevated IL-6 and TNF-α compared to healthy controls . Our groKRT3) when compared to controls. Other proteins found in excess in KC were S100A4 and gelsolin; enolase 1 was down-regulated in KC. These are largely cytoskeleton-associated proteins. S100A4 is a calcium binding protein that can bind to multiple elements of the cytoskeleton. It stimulates cell migration and change in cell morphology. Gelsolin caps actin during its assembly. It is known to stimulate cell motility and has been linked to apoptosis. Enolase 1 that is mainly produced in the basal epithelial cells was reduced in KC, which suggests de-differentiation or a decreased proliferation of the basal cells. However, enolase 1 also has a role in glycolysis and in adhesion to the ECM [The first proteomic study on the keratoconus cornea was performed on epithelial specimens alone . The mos the ECM , 43. As Additional evidence confirmed the findings of reduced enolase 1 production in the epithelium of KC cornea  The studAnother proteomic study of the corneal epithelium in KC demonstrated higher levels of lamin-A/C, keratin type I cytoskeletal 14 and 16, tubulin beta chain, heat shock protein cognate 71kDA protein, and S100-A4 . Other fGiven that the main clinically observable defect in KC is the thinning of the stroma, proteomic studies of KC stroma were warranted. In the same study described above, our group yielded the most comprehensive proteomic landscape of the keratoconic corneal stroma, identifying a total of 1,157 proteins. Decreases were detected in many structural extracellular matrix proteins of the corneal stroma such as, decorin, lumican and keratocan, collagen types I and V . DecorinThe cumulative proteomic findings suggest a generalized loss of the normal quiescent phenotype of the keratocytes and an increase in the abnormal protein expression, similarly to that associated with wound healing phenotype. The global decrease in different types of collagens is consistent with the clinical observation of debulked and thinned corneas in KC.The aqueous humor provides nutrients to the posterior avascular cornea and has been recently studied by mass spectrometry . Samples5In view of the evidence obtained from proteomic studies, the involvement of a few potential pathways may be hypothesized:5.1Inflammatory cytokines have been shown to be over-expressed in KC. Our group has shown IL-17 over-expression in pooled samples from KC patients. Interestingly, the corneal epithelium is one of the targets of IL-17 and when human corneal epithelial cells lines are exposed to stress they can produce cytokines that promote Th-17 differentiation . Despite5.22 levels and ambient ultraviolet irradiation).Oxidative stress has long been implicated in the pathogenesis of KC -57. Howe5.3The ECM is produced by keratocytes. This process is regulated, amongst others, by TGF-β signaling , 61 Not Two reports from our group have shown that the TGF-β axis may be dysfunctional in keratoconus. An immunohistochemical staining showed increased pSMAD 2 staining of the keratoconus corneal epithelium. In the second, cultured stromal cells from keratoconus corneas showed an abnormally increased phosphorylation of pSMAD1/5/8 – a signaling axis that is novel to the cornea , 69. AnoAltogether, these findings suggest that the TGF-β pathway may be an important effector route in KC and warrant further research.6The hypothesis that the trigger for keratoconus lies in the epithelium was stipulated a few decades ago. The corneal epithelial basement membrane was shown to be irregular or absent in cases of severe KC using anti collagen type IV antibodies . An abnoKeratocytes are specialized tissue fibroblasts that reside within the corneal stroma and produce most of the corneal stromal proteins -80. In KMultiple in vivo confocal scanning microscopy studies have shown that keratocyte density in KC is lower than in normal subjects , 82, 83.Our studies of keratocytes in culture also point to dysregulations of the TGFβ pathway in keratoconus. Additional studies are required to pinpoint alterations in specific intermediates in TGFβ signal propagations in KC keratocytes.Overall, it appears that KC may be caused by an aberrant tissue response to one or more unidentified stimuli that subsequently leads to keratocyte depletion or dysfunction, loss of collagen, and ultimately a biomechanically weak cornea with a degenerated ECM.Despite major leaps in methodology, the origin and pathogenesis of KC remain elusive. The findings of various studies are sometimes so dissimilar that their comparison and juxtaposition become quite challenging. The fact that KC is a spectrum of phenotypes ranging from mild to severe vision disabling disease does not simplify the researcher’s task either. Naturally, studies performed on end-stage cases are less likely to demonstrate similar findings as in early and active disease – which may explain some of the heterogeneity in the findings of different studies. Other factors that need to be considered are that the disease itself may be multifactorial – caused by multiple genetic mutations; it may be affected by environmental factors such as climate and geography; and it may also be the final phenotype or pathway of multiple processes occurring simultaneously at the level of the epithelium and the stroma. Ultimately research on keratoconus is striving to identify these multiple-gene environment interactions to better understand disease processes in individual patients and tailored care.


HMS2 in Saccharomyces cerevisiae, sgRNA/dCas9 targeting to the non-template strand for antisense transcription results in antisense transcription termination, premature termination of a proportion of sense transcripts and initiation of a novel antisense transcript downstream of the sgRNA/dCas9-binding site. This redefinition of the transcriptional landscape by CRISPRi demonstrates that it is not strand-specific and highlights the controls and locus understanding required to properly interpret results from CRISPRi interventions.CRISPRi, an adapted CRISPR-Cas9 system, is proposed to act as a strand-specific roadblock to repress transcription in eukaryotic cells using guide RNAs (sgRNAs) to target catalytically inactive Cas9 (dCas9) and offers an alternative to genetic interventions for studying pervasive antisense transcription. Here, we successfully use click chemistry to construct DNA templates for sgRNA expression and show, rather than acting simply as a roadblock, sgRNA/dCas9 binding creates an environment that is permissive for transcription initiation/termination, thus generating novel sense and antisense transcripts. At  Eukaryotic genomes are pervasively transcribed but the effect of this inter- and intragenic transcription is not fully understood . Of partSUT650 at the HMS2 locus to show that SUT650 represses HMS2 sense transcription (SUT650 antisense transcription to ask (i) whether SUT650 represses HMS2 sense transcription without a genetic intervention and (ii) whether CRISPRi is strand-specific using, in addition to HMS2, an engineered GAL1 gene with a well-characterised antisense transcript  premature termination of the sense transcript and (ii) initiation of a new unstable antisense transcript in the vicinity of the sgRNA binding site. As transcription from this new antisense initiation site extends in to the HMS2 promoter, there is no net change in HMS2 sense transcript levels. Thus, CRISPRi redefines the transcriptional landscape at HMS2. This suggests that routine use of CRISPRi for gene expression analysis will require rigorous analysis of transcript integrity and function before conclusions can be drawn.The main conclusion from this study is that CRISPRi at the URA3 selection cassette in the endogenous snR52 locus for expression of a transcript that is then processed to form the mature nuclear-retained sgRNA  initiating within an ADH1 terminator inserted into the GAL1 coding region  strand next to a protospacer adjacent motif (PAM)  Figure . Firstlyg region   (SUT650 TSS (SUT650 TSS. AS+243NT/dCas9 significantly (p=0.011) reduced SUT650  . Note thT650 TSS  and thusd SUT650 . Howevers tested . CRISPR s tested , but we s tested  at each  regions . However regions . These rHMS2, sgRNA AS+243NT/dCas9 was able to reduce SUT650 transcript levels significantly . To map transcript Bt, we used a series of Northern blotting probes across the locus  was created. This is potentially due to changes in the chromatin structure caused by AS+243NT/dCas9 binding to this region .At ficantly . Since Sal block   when the GAL1 AS transcript was blocked , we detected a considerably shortened HMS2 sense transcript (At). Since the combined levels of the truncated and full-length transcripts are similar to those in the strain not expressing an sgRNA, independent of the presence of XRN1 , but the truncated transcript was undetectable using probe H4 downstream of this site. Thus, the transcriptional block caused by AS+243NT/dCas9 is not strand-specific and re-defines the transcriptional landscape at the HMS2 locus by creating a chromatin environment that is suitable for both transcription initiation and termination. To see if we could more directly target and block HMS2 sense transcripts, we designed three sgRNAs to bind the non-template strand adjacent to PAM sites near the HMS2 sense TSS  and monitored their effects on both HMS2 sense and antisense transcripts . To differentiate between these possibilities, we deleted the major cytoplasmic 5’−3’ exonuclease XRN1. Since XRN1 deletion in the TATA mutant strain only slightly increased GAL1 AS levels, this suggests that most repression was at the level of transcription rather than altering transcript stability   . This surference . Whilst  altered . The dis results . Thus, CGAL10 (GAL1 (this work), here we demonstrate that this is not true at HMS2. This may reflect the vastly different transcription levels for the galactose-inducible genes (high) compared to HMS2 (low). Furthermore, a study in human K562 cells found that CRISPRi-induced repression did not correlate with which strand was targeted at 49 genes, although the mechanism behind this observation was not investigated  to minimise any off-target effects .PAM sequences within 300 bp of the TSS in question were identified and the 20 bp immediately adjacent to these were used to design the variable regions of the sgRNAs. These sequences were run through an off-spotter algorithm .Single-stranded DNA oligonucleotides complementary to the constant and variable sgRNA regions were synthesised by standard phosphoramidite oligonucleotide synthesis with 3’-alkyne or 5’-azido modifications respectively  Supplem. The cliint DNAs . PCR wasTDH3 promoter. The DNA templates for sgRNA production were integrated in place of an exogenous URA3 cassette immediately downstream of the splice site in the endogenous snR52 locus using homologous recombination followed by 5-FOA selection. Correct insertion was confirmed by genomic DNA Sanger sequencing. SNR52 is a Pol III-transcribed C/D box small nucleolar RNA (snoRNA) gene and so does not undergo extensive post-transcriptional processing such as capping and polyadenylation, and transcripts from this locus are retained in the nucleus. Additionally, the locus contains a self-splicing site that produces a mature transcript without additional machinery, allowing precise production of a mature sgRNA without any unwanted extensions. Primer sequences for strain construction are listed in dCas9, without fusion to a transcriptional repressor domain, was expressed from a plasmid (# 46920 Addgene ) under tGAL1 locus, yeast were grown to mid-log in rich media  so that the GAL1 induction kinetics were similar to what we had observed previously  was achieved using a Bioscreen spectrophotometer that automatically measures the optical density of cultures at 30°C every 20 min for 24 hr. Doubling times were extracted from the gradient of the curves during logarithmic growth.GAL1 and HMS2, respectively. Primers for these reactions are listed in Northern blotting was performed as before  using asdCas9 expression was confirmed after each experiment using an anti-Cas9 antibody (Diagenode C15310258) at 1:5000 dilution and anti-Tubulin (Abcam ab6160) at 1:3333.Yeast cells were fixed with 1% formaldehyde  followed by the addition of glycine to 125 mM for 5 min. Cell pellets were collected by centrifugation  before washing twice with 10 ml cold PBS. Cells were re-suspended in cold FA-150 buffer  and broken using 1-ml glass beads on a MagnaLyser (Roche) at . Fixed chromatin was sheared by sonication using a biorupter , cleared by centrifugation  and incubated with 5 µl anti-Cas9 antibody (Diagenode C15310258) and 30 µl protein A-dynabeads pre-blocked with 200 µg/ml yeast tRNA, 200 µg/ml BSA and 200 µg/ml glycogen . Beads and attached chromatin were collected using a magnetic rack and washed with TSE-150 buffer  for 3 min, TSE-500 buffer  for 3 min, LiCl buffer  for 15 min and twice with TE, all at 22°C. After washing, chromatin was eluted from the beads for 30 min at 65°C with elution buffer . Addition of 350 mM NaCl and incubation for 3 hr at 65°C reversed the cross-links before treatment of samples with RNase A for 1 hr at 37°C and proteinase K overnight at 65°C. DNA was purified using a PCR-purification kit (Qiagen) and eluted in 400 µl 1 mM Tris-Cl pH 8.0. Input DNA was diluted accordingly. Real-time quantitative PCR (qPCR) was carried out using a Corbett 6000 Rotorgene and Sybr green qPCR master mix (Bioline). Data ([IP - no antibody control]/input) were expressed as a percentage of the input (relative to sgRNA +276 NT). Real-time qPCR primers are listed in GAL1 sense transcript was performed as  appeared as ideally suited for this since it has been shown to be able to trigger transcription termination in a site specific and, most importantly, strand specific manner, without altering the genomic sequence.This short report shows that this strand specificity is actually not true at all loci. In addition, it describes an example in which the binding of the guide RNA actually induces spurious transcription initiation, suggesting that it markedly perturbs the local chromatin environment.I think that the data are suitably convincing. Note, by the way, that this referee has made the similar (unpublished) observations that the termination by this system was not strand specific at yet another locus.eLife might seem appropriate for that.Although one could argue that the main results presented in this manuscript are negative results, I personally think that they are well worth being published to warn further investigators who would wish to use this system and a short report in eLife, 2015) would be located about 60 nucleotides DOWNSTREAM of the AS-44NT sgRNA binding site (position 705184). Thus, as the other SUT650 sgRNA targeting sequences, AS-44NT would actually be located within the SUT650 transcribed region, downstream from its TSS and not upstream. It could thus not be considered as a "control" sgRNA. Could the authors verify this point and, if this is correct, modify the text, The position of the 5'-end of SUT650 seems incorrectly assigned. Indeed, if one refers to the TIF-seq data from the original Pelechano paper, the main transcription start site for SUT650  in wt and Xrn1 deletion strains. Their primary conclusion is that CRISPRi works well in some cases and not in others and requires careful controls. For example, the authors show that the system does repress antisense transcription, but at one of the two studied loci (HMS2), an additional antisense transcript appears from a new initiation site, and the sense transcript is prematurely terminated. In addition, they present a clever tool based on click chemistry for quickly making gRNA. Although I don't find the main conclusion revelatory, I commend the authors for their thoroughness. Certainly, this message is one that bears reporting, although I am not quite convinced this result clears the bar for an 1) The authors show that CRISPRi represses the antisense transcript at a GAL1 model gene. They then check whether this repression has transcriptional effects on GAL1 sense transcription and they conclude there is no difference in galactose. However, previous work has shown that antisense at the GAL locus works mostly under repressive conditions. Have the authors repeated this experiment in glucose or raffinose?2) The authors show that CRISPRi repression at the HMS2 locus results in a changed transcriptional landscape. Replacement of the HMS locus by URA does not have the same effect, and they suggest that CRISPRi binding causes these additional transcripts. However, it is unclear whether the new transcripts at HMS2 appear because of removal of the antisense, which in itself could change the chromatin landscape and thus the transcriptional landscape, or whether it arises because of binding of dCas9. The same authors have previously published that removal of the GAL10 ncRNA by genetic methods (point mutations of the transcription factor binding sites) also results in additional transcripts not visible in the wildtype . Given these findings, it remains unclear whether the new antisense transcript indeed results from the CRISPRi binding. Moreover, it is noteworthy that additional transcripts are also found in some genetic mutants. Control experiments are therefore required for both methods.3) The Xrn1 sensitivity is a confounding factor, and I am not sure that strain should be the gold standard for CRISPRi not working. The whole point is that one can get blocking or perturbation in the absence of genetic manipulation. So, for example one concluding sentence "CRISPRi is not as effective as a genetic mutation in reducing levels of either the GAL1 or HMS2 AS transcripts" should end with "[…] in the Xrn1 deletion."4) In a related point, 5) Lastly, given that CRISPRi does represses the antisense transcript in both loci tested in the study in a strand-specific manner, the title is a bit misleading.In summary, despite technical issues, I nevertheless find their argument that CRISPRi can have locus-specific effects to be convincing. However, the manuscript is mostly of a technical nature. Most experiments described are control experiments that should be done anyways when using a new method. The observations that CRISPRi can result in different transcripts is good to know when designing such experiments, but there is no proposed mechanistic insight, nor new biology that is learned from this study.Reviewer #3:In this manuscript Howe and collaborators in the Mellor and Brown labs explore the molecular basis of CRISPRi transcriptional interference in the context of yeast anti-sense transcription. This reviewer agrees with the authors that understanding the basis of CRISPRi in anti-sense transcription  is an important area of research because it is extremely difficult to establish direct causality with regard to these particular RNAs using genetic perturbations.In the first section of their manuscript, Howe et al., describe synthesis of sgRNA constructs using "click-chemistry" to couple sgRNA variable regions to the constant region. The author's then use knock-in yeast strains generated using these constructs to evaluate the effects of CRISPRi on anti-sense transcription at the GAL1 and HMS2 genes. The authors observe that contrary to behaving as a transcriptional road-block when targeted to the non-template strand, dCas9 CRISPRi complexes result in unpredictable changes in chromatin and transcription, resulting in pre-mature termination of sense transcripts and shifting anti-sense transcription initiation sites. Collectively, these results clearly demonstrate that CRISPRi effects are more complex than previously reported and likely depend on locus-specific chromatin context. These observations have broad implications with respect to design of CRISPRi strategies and argue a strong case for careful characterization of transcript isoforms and promoter/termination sites before and after deploying CRISPRi. However, there are some fundamental issues with controls and some of the conclusions drawn that should be addressed before publication.1) The authors observe that only a subset of sgRNAs targeting the non-template strand are capable of mediating repression, however, the reasons for this phenomenon are unclear. A simple explanation is that some sgRNAs are not capable of mediating stable dCAS9 interaction with chromatin. The author's should test this by performing Chromatin-Immunoprecipitation (ChIP) for dCas9 in their various sgRNA expressing lines to test if dCas9 chromatin occupancy correlates with repression. In my opinion this is an absolutely essential control for interpreting the authors' experiments.2) This reviewer feels that increasing the sgRNA coverage across the sense/antisense transcripts in question would greatly strengthen the manuscript. In its current state, the authors test only 2 sgRNAs towards the GAL1-AS and SUT650 non-template strands, from which they obtain a single functional repressive sgRNA for each anti-sense transcript. As a strand specificity control they test 3 sgRNAs targeting the sense transcript HMS2 and show that none of these has an effect on sense or anti-sense. However, with a hit rate of ~1 in 2, this reviewer is not convinced by authors' claim that sgRNAs targeting the sense transcript have no effect on antisense transcription. Perhaps the coverage was simply too low to obtain a functional repressive sgRNA. The authors should perform high-density sgRNA tiling experiments (targeting both template and non-template) for either GAL1-AS or SUT650 (not necessary to do both loci). I feel this experiment will dramatically improve the manuscript and instill clarity into the context requirements for CRISPRi mediated repression. Reviewer #1:In recent years, transcriptional interference by antisense RNA transcription has been recognized as a relatively frequent means to regulate mRNA expression. Tools enabling antisense transcription arrest without directly affecting sense transcription are required to study the mechanism mediating this effect. The recently reported CRISPRi system, using a modified, partially defective, Cas9 (dCas9) appeared as ideally suited for this since it has been shown to be able to trigger transcription termination in a site specific and, most importantly, strand specific manner, without altering the genomic sequence.This short report shows that this strand specificity is actually not true at all loci. In addition, it describes an example in which the binding of the guide RNA actually induces spurious transcription initiation, suggesting that it markedly perturbs the local chromatin environment.I think that the data are suitably convincing. Note, by the way, that this referee has made the similar (unpublished) observations that the termination by this system was not strand specific at yet another locus.Although one could argue that the main results presented in this manuscript are negative results, I personally think that they are well worth being published to warn further investigators who would wish to use this system and a short report in eLife might seem appropriate for that.We thank the reviewer for recognising the importance of this work as a warning to other researchers.The position of the 5'-end of SUT650 seems incorrectly assigned. Indeed, if one refers to the TIF-seq data from the original Pelechano paper, the main transcription start site for SUT650  would be located about 60 nucleotides DOWNSTREAM of the AS-44NT sgRNA binding site (position 705184). Thus, as the other SUT650 sgRNA targeting sequences, AS-44NT would actually be located within the SUT650 transcribed region, downstream from its TSS and not upstream. It could thus not be considered as a "control" sgRNA. Could the authors verify this point and, if this is correct, modify the text, We thank the reviewer for pointing this out and apologise for our oversight. We note that there is a large heterogeneity in the position of the transcription start site for SUT650 and the one we had chosen previously  is one of the many sites. We have now discussed the transcript heterogeneity in the main body of the text and included a supplementary figure with the Pelechano TIF-seq data to illustrate this . We haveReviewer #2:In this paper by Howe et al., the authors assess the potential of CRISPRi for strand-specific transcriptional perturbation in budding yeast. They study two genes in depth – GAL1 and HMS2 – and compare the effectiveness of CRISPRi to previous approaches (for example deletion of cis-acting motifs such as the TATA box) in wt and Xrn1 deletion strains. Their primary conclusion is that CRISPRi works well in some cases and not in others and requires careful controls. For example, the authors show that the system does repress antisense transcription, but at one of the two studied loci (HMS2), an additional antisense transcript appears from a new initiation site, and the sense transcript is prematurely terminated. In addition, they present a clever tool based on click chemistry for quickly making gRNA. Although I don't find the main conclusion revelatory, I commend the authors for their thoroughness. Certainly, this message is one that bears reporting, although I am not quite convinced this result clears the bar for an eLife paper.eLife, with its reputation for thoroughness and research integrity, would be an ideal journal for publishing this work.We thank the reviewer for their support of the message we are trying to convey to the community. We feel that 1) The authors show that CRISPRi represses the antisense transcript at a GAL1 model gene. They then check whether this repression has transcriptional effects on GAL1 sense transcription and they conclude there is no difference in galactose. However, previous work has shown that antisense at the GAL locus works mostly under repressive conditions. Have the authors repeated this experiment in glucose or raffinose?As presented in 2) The authors show that CRISPRi repression at the HMS2 locus results in a changed transcriptional landscape. Replacement of the HMS locus by URA does not have the same effect, and they suggest that CRISPRi binding causes these additional transcripts. However, it is unclear whether the new transcripts at HMS2 appear because of removal of the antisense, which in itself could change the chromatin landscape and thus the transcriptional landscape, or whether it arises because of binding of dCas9. The same authors have previously published that removal of the GAL10 ncRNA by genetic methods (point mutations of the transcription factor binding sites) also results in additional transcripts not visible in the wildtype . Given these findings, it remains unclear whether the new antisense transcript indeed results from the CRISPRi binding. Moreover, it is noteworthy that additional transcripts are also found in some genetic mutants. Control experiments are therefore required for both methods.The point made here by this reviewer is a valid one – we are unable to infer whether it is the binding of sgRNA/dCas9 or the loss of the antisense that is causing the initiation of the new transcripts. It would be experimentally very difficult to show this and we have now clarified the two possible reasons for new transcripts in the text.However, the main point is that, independent of what is causing the initiation of the new antisense transcript, in the CRISPRi strain, transcription is occurring in the antisense direction into the HMS2 sense promoter and so no conclusions could be made about the function of SUT650 in sense regulation in this strain. The fear is that some investigators may use the CRISPRi system without the proper controls and the current set of experiments is intended to highlight how important these controls are.3) The Xrn1 sensitivity is a confounding factor, and I am not sure that strain should be the gold standard for CRISPRi not working. The whole point is that one can get blocking or perturbation in the absence of genetic manipulation. So, for example one concluding sentence "CRISPRi is not as effective as a genetic mutation in reducing levels of either the GAL1 or HMS2 AS transcripts" should end with "… in the Xrn1 deletion."+ background, the transcript could not be detected. This is important mechanistically because often it is the act of antisense transcription that may cause the effect on neighbouring/overlapping transcription units rather than the antisense transcript itself . In We apologise that we were not clear about the reason for performing the experiments in the XRN1 delete and have now clarified this in the text. We chose to use the deletion of XRN1 to stabilise any transcripts that may be being produced in the CRISPRi strains but then rapidly degraded so that, if the experiment is done in an XRN14) In a related point, We have found it very difficult to quantify blots with very low signal levels so we apologise that the image did not appear to be representative of the accompanying quantification. We have now included a more rigorous background subtraction step for the GAL1 AS Northerns and hope that the reviewer is satisfied with the new 5) Lastly, given that CRISPRi does represses the antisense transcript in both loci tested in the study in a strand-specific manner, the title is a bit misleading.We have shown that CRISPRi only works strand-specifically at GAL1, because at HMS2, both the sense and the antisense transcripts are blocked. However, we agree that the original title perhaps didn’t reflect this fully so have now changed it to include the words ‘at all loci’: ‘CRISPRi is not strand-specific at all loci and redefines the transcriptional landscape’.In summary, despite technical issues, I nevertheless find their argument that CRISPRi can have locus-specific effects to be convincing. However, the manuscript is mostly of a technical nature. Most experiments described are control experiments that should be done anyways when using a new method. The observations that CRISPRi can result in different transcripts is good to know when designing such experiments, but there is no proposed mechanistic insight, nor new biology that is learned from this study.We agree that most of the experiments we have done should be performed as controls for any CRISPRi experiments but are aware that, especially if CRISPRi is being used for a large-scale screen, this probably won’t be done . We wish to publish this work to highlight how important it is that these controls are done or perhaps to suggest that CRIPSRi may not always be a suitable technique if the correct controls cannot be performed.We have also added mechanistic insight in the new Reviewer #3:In this manuscript Howe and collaborators in the Mellor and Brown labs explore the molecular basis of CRISPRi transcriptional interference in the context of yeast anti-sense transcription. This reviewer agrees with the authors that understanding the basis of CRISPRi in anti-sense transcription  is an important area of research because it is extremely difficult to establish direct causality with regard to these particular RNAs using genetic perturbations.In the first section of their manuscript, Howe et al., describe synthesis of sgRNA constructs using "click-chemistry" to couple sgRNA variable regions to the constant region. The author's then use knock-in yeast strains generated using these constructs to evaluate the effects of CRISPRi on anti-sense transcription at the GAL1 and HMS2 genes. The authors observe that contrary to behaving as a transcriptional road-block when targeted to the non-template strand, dCas9 CRISPRi complexes result in unpredictable changes in chromatin and transcription, resulting in pre-mature termination of sense transcripts and shifting anti-sense transcription initiation sites. Collectively, these results clearly demonstrate that CRISPRi effects are more complex than previously reported and likely depend on locus-specific chromatin context. These observations have broad implications with respect to design of CRISPRi strategies and argue a strong case for careful characterization of transcript isoforms and promoter/termination sites before and after deploying CRISPRi. However, there are some fundamental issues with controls and some of the conclusions drawn that should be addressed before publication.1) The authors observe that only a subset of sgRNAs targeting the non-template strand are capable of mediating repression, however, the reasons for this phenomenon are unclear. A simple explanation is that some sgRNAs are not capable of mediating stable dCAS9 interaction with chromatin. The author's should test this by performing Chromatin-Immunoprecipitation (ChIP) for dCas9 in their various sgRNA expressing lines to test if dCas9 chromatin occupancy correlates with repression. In my opinion this is an absolutely essential control for interpreting the authors' experiments.We are not the first to observe that, as for CRISPR, some sgRNAs are not effective for CRISPRi e.g. Lenstra et al., 2015. We do not aim to address the question of why only some sgRNAs are repressive in this paper. However, we have now performed a ChIP experiment for (d)Cas9 see  to show 2) This reviewer feels that increasing the sgRNA coverage across the sense/antisense transcripts in question would greatly strengthen the manuscript. In its current state, the authors test only 2 sgRNAs towards the GAL1-AS and SUT650 non-template strands, from which they obtain a single functional repressive sgRNA for each anti-sense transcript. As a strand specificity control they test 3 sgRNAs targeting the sense transcript HMS2 and show that none of these has an effect on sense or anti-sense. However, with a hit rate of ~1 in 2, this reviewer is not convinced by authors' claim that sgRNAs targeting the sense transcript have no effect on antisense transcription. Perhaps the coverage was simply too low to obtain a functional repressive sgRNA. The authors should perform high-density sgRNA tiling experiments (targeting both template and non-template) for either GAL1-AS or SUT650 (not necessary to do both loci). I feel this experiment will dramatically improve the manuscript and instill clarity into the context requirements for CRISPRi mediated repression.We do not claim that, if we were to find an sgRNA to block HMS2 sense, that this would not also affect the antisense transcript rather that the 3 sgRNAs we tested against HMS2 sense did not repress either the sense or antisense transcripts. We apologise if this was confused in the text and have clarified this section. We included the (negative) results from the 3 sense-targeting sgRNAs again to reiterate the fact that not all sgRNAs are effective and this is an important control to do. We included the HMS2 AS data from the sense sgRNA strains as a control for the sgRNAs also not working on the template strand.As stated above, we are not aiming to investigate what makes some sgRNAs cause effective transcript repression and others not. We do not feel that testing more sgRNAs would significantly add to the conclusions of the paper, which were that a) CRIPSRi does not always repress transcription b) CRISPRi is not always strand-specific and c) CRISPRi can induce novel transcripts that would confound any analysis. It is also relevant that the highest CRISPRi activity with unfused dCas9 was observed using PAM sites near the relevant TSSs  as we have done here. Using PAM sites that are more distant from the TSS is unlikely to add any insight into the conclusions we have made here.


In high resolution PET data with good counting statistics BFM produced estimates of rCPS comparable to SAIF, but at lower computational cost; thus the simpler, less costly method can be applied. With poorer counting statistics (lower injected radiotracer doses), BFM estimates were more biased. In data smoothed to simulate lower resolution PET, BFM produced estimates of rCPS 9–14% higher than SAIF, overestimation consistent with applying a homogeneous tissue model to kinetically heterogeneous data. Hence with lower resolution data it is necessary to account for kinetic heterogeneity in the analysis. Kinetic heterogeneity may impact analyses of other tracers and scanning protocols differently; assessments should be made on a case by case basis.Functional quantification with PET is generally based on modeling that assumes tissue regions are kinetically homogeneous. Even in regions sufficiently small to approach homogeneity, spillover due to resolution limitations of PET scanners may introduce heterogeneous kinetics into measured data. Herein we consider effects of kinetic heterogeneity at the smallest volume accessible, the single image voxel. We used L-[1- Kinetic models used in PET are necessarily simplified representations of tissue processes, and one of the simplifying assumptions frequently made is that the tissue volume being analyzed is kinetically homogeneous, i.e., rates of blood flow, delivery and efflux of tracer to/from tissue, metabolism, and incorporation into labeled products do not vary within the tissue region examined. In brain these assumptions are difficult to meet. At spatial resolutions approximately an order of magnitude higher than PET, such as achieved in autoradiographic studies, one clearly sees heterogeneity of rates of blood flow, glucose metabolism, and protein synthesis across the brain3. The rates of these processes not only differ between gray and white matter, but they can also vary considerably within gray matter structures themselves, as for example in the cortical layers5. At the relatively lower spatial resolution of PET scanning, therefore, activities measured in brain can be expected to originate from kinetically heterogeneous mixtures of tissue. Application of kinetic models designed for homogeneous tissues to heterogeneous tissues leads to errors in estimated rates of cerebral blood flow and glucose metabolism, as well as to errors in estimates of receptor binding parameters9.One of the great strengths of positron emission tomography (PET) is that it can be used to provide quantitative measurements of physiological and biochemical processes. Functional quantification with PET is generally based on kinetic modeling approaches that relate a particular biological process of interest to measurements of activity in blood and tissue following administration of a radiolabeled tracer11, the resolution limit of the PET scanner12 and the partial volume effect, a limiting characteristic of all tomographic imaging systems15.Two approaches are in current use to reduce the effects of heterogeneity on estimation of kinetic model parameters from which rates of the process of interest are determined: (1) reducing the volume of tissue in which activity is analyzed, and (2) utilizing an analysis approach that does not depend on the assumption of kinetic homogeneity. There is a limit with PET data of how small a volume of tissue can be examined, as the smallest unit of available data is the single image voxel. But even if the voxel size could be made arbitrarily small (even lower than neuroimaging standards of 1–2 mm in size) and possibly contain only kinetically homogeneous tissue, its measured activity will be affected by the resolution limit of the technique (a positron travels an average 1–3 mm before annihilation)In the present study we investigate the effect of tissue kinetic heterogeneity when PET quantification is performed at the voxel level, with the final aim of understanding whether the assumption of kinetic homogeneity within a voxel is applicable or not. In particular we are interested in (1) quantifying the degree of kinetic heterogeneity for a given PET tracer and (2) assessing the impact of PET scanner resolution and injected tracer dose on this problem.11C]leucine PET studies for quantitative measurement of rates of cerebral protein synthesis (rCPS) in human subjects19 to explore these questions. Although the specific conclusions for this tracer may differ from those of other tracers and other scanning conditions, the considerations and the techniques for examining them can be extended to all PET tracers in brain and non-brain tissues.We utilize data from L-[1-11C]leucine in brain, specifically whether the tissue is modeled as kinetically homogeneous or kinetically heterogeneous.Two approaches have been validated for quantification of rCPS at the voxel level. They depend on the assumptions used to model the behavior of L-leucine PET data without prior assumptions concerning kinetic homogeneity or heterogeneity in the data22. SAIF is a modified version of Spectral Analysis used for [18F]fluorodeoxyglucose PET studies25. SAIF implements a filtering procedure to reduce the impact of noise; this makes voxel-wise quantification feasible22. The filter employed by SAIF is a bandpass filter that separates blood components and tracer trapped in the tissue from other tissue components. Additionally, the filter limits detection of phantom components that arise from measurement noise. For irreversible tracers SAIF is optimized for estimation of the weighted average rate constant for net trapping of tracer and the weighted average influx rate constant (K1), two parameters that are fundamental for the computation of 23. In addition to the parameter estimates, SAIF returns the number of components detected in the tissue and thus provides information about the presence of kinetic inhomogeneity22. SAIF utilizes a non-negative linear least squares estimator, which is computationally faster than non-linear estimators, but slower than unconstrained linear least squares.The model for L-leucine PET studies19. For each frame of each reference time-activity curve (both homogenous and heterogeneous scenarios) 200 noise-added realizations were generated via Monte Carlo techniques. Noise was defined consistent with a stochastic process with zero mean and Gaussian distribution whose variance is described by11C, Δti is the length of Frame i, CT*(ti) is the mean decay-corrected concentration of 11C in Frame i of the tissue, and 26. The scale factor for the noise variance was set to be consistent with voxel-wise error, as detailed previously20, for both homogeneous and heterogeneous tissue simulation scenarios. Additionally, we generated noise consistent with regional time-activity curve error22 for the heterogeneous tissue simulation scenario. ROI-level noise was not used in the homogeneous tissue simulation scenario, assuming that a volume sufficient to have the lower noise level would be too large to contain only kinetically homogeneous tissue.All simulations used the arterial input functions from one subject studied previouslyβ = k2 + k3 + k4, logarithmically distributed27 in the range 0.0037–1.33 min−1, was used. For SAIF, the filter bandpass was set to  in order to be consistent with application to low as well as high signal-to-noise ratios23. Weights were set proportional to the inverse of the error variance . For each of the 19 sets of parameters in the reference data set, accuracy was assessed as the bias in K1, jth simulated reference value for K1, j=1, 2, …, 19) and ith corresponding simulation estimate. For SAIF we also investigated the capacity of the method to correctly identify the number of subregions in the simulated tissue.The performances of BFM and SAIF were evaluated in terms of robustness and accuracy. Robustness was defined as the number of failures to converge to a solution, or to find physiologically meaningful parameter estimates, divided by the total number of simulated cases19 of eight healthy, awake, male subjects (age 20–24) were reanalyzed in the current study. The subjects were chosen to have a range of injected activities of L-[1-11C]leucine. Five of our reanalyzed group were administered >0.30 mCi L-[1-11C]leucine/kg body weight, the target dose for the studies. The remaining 3 subjects had injected doses of 0.29, 0.17, and 0.16 mCi/kg. Subject inclusion criteria as well as the PET scan protocol were described in detail previously18. Briefly, all PET studies were performed on the ECAT High Resolution Research Tomograph (HRRT) , with a spatial resolution of ~2.6 mm full width at half maximum (FWHM)28. The 90-min emission scan was initiated coincident with a 2-min intravenous infusion of L-[1-11C]leucine. Images were reconstructed using the motion-compensated 3D ordinary Poisson ordered subset expectation maximization algorithm28 as 42 frames of data ; voxel size was 1.21 × 1.21 × 1.23 mm. Arterial blood was sampled over the course of the study and concentrations of unlabeled and labeled leucine in arterial plasma and total 11C and 11CO2 activities in whole blood were measured according to methods detailed previously18. All subjects underwent a T1-weighted MRI of the brain. ROIs were drawn on the MRI, and a 3D volume of each ROI mask was created. The MRI was then co-registered to the mean of the 30–60 min PET images by use of a 3D rigid body transformation and the transformation parameters were applied to the ROI mask volumes. Vinci software  was used for the co-registration.Data from previously-reported studiesK1, λ, and rCPS were determined in each voxel by means of both BFM and SAIF. The algorithm settings were maintained the same as in the simulation studies. The difference between tracer arrival time in brain and arterial sampling site was estimated by shifting the whole brain time-activity curve (TAC) with various tracer delays (0–20 sec), fitting the whole brain TAC by use of SAIF, and selecting the delay that produced the smallest weighted residual sum of squares.Whole brain and 12 regions  were evaluated in all subjects. Estimates of 23. The method exploits random resampling of normalized residuals as an alternative to random noise generation with a Gaussian model30. Heterogeneity maps were constructed by fusing the MR image, resliced to match the PET data, with a map of the spatial distribution of voxels with two or more SAIF-estimated equilibrating components.For all subjects and regions, the mean of the voxel estimates in all voxels of the ROI was used as the parameter estimate for the region. Correlation between SAIF and BFM estimates was assessed, and the relative difference between BFM and SAIF estimates was computed. In addition, we used SAIF to assess the level of heterogeneity for each region, defined as the fraction of voxels described by two or more equilibrating components. Analysis was performed by single application of SAIF and by use of a bootstrap approach (N of repetitions = 50)11C]leucine data analyses, we artificially reduced the resolution of our data by applying a 3D Gaussian filter (FWHM 7.1 mm) to all HRRT PET images in the study. The decreased-resolution data were reanalyzed with both SAIF and BFM, and the same comparisons were made as in the unsmoothed HRRT data described above.In order to examine the influence of scanner resolution on the L-leucine PET data showed good agreement; rCPS is shown in Fig. K1 (not shown) and in rCPS . Mean regional estimates of rCPS computed by BFM and SAIF, however, showed a tendency to agree better in those subjects who were administered larger doses of L-[1-11C]leucine . We produced scatter plots to further examine the relationship between injected dose of L-[1-11C]leucine and the percentage difference between rCPS determined by the two methods ; this implies we would expect higher differences in rCPS between methods in regions that are high in white matter content, since white matter has low rCPS. In the subject with the lowest injected dose . In general rCPS estimated with SAIF was higher than when estimated with BFM. In amygdala and hippocampus the discrepancy between methods was greatest: rCPS estimated with SAIF was 13% and 15% higher, respectively, than when estimated with BFM. In cerebellum and corona radiata SAIF estimates were 6% higher, and in all other regions the difference was <5%. There was good agreement between SAIF and BFM estimates in terms of variability.The foregoing data show that differences in rCPS estimated with BFM and SAIF are dissimilar in the subjects who received a low injected dose from those who received higher doses. We, therefore, looked at how the methods compare in the subset of five subjects who received a dose of L-leucine and differences in rCPS estimated with the two methods . From a single analysis of each subject, it was not possible to localize which areas in brain have higher heterogeneity fractions  for each region was computed by SAIF as the number of voxels found to have two or more subregions divided by the total number of voxels in the region. Maps of the heterogeneity fraction for one subject are shown in Fig. ons Fig. . When thent Fig. . In the ent Fig. ; the borent Fig. .Figure 8in vivo cerebral protein synthesis rates, with the final aim of understanding whether this effect is trivial or is instead a real source of bias for voxel-by-voxel analyses. We used L-[1-11C]leucine PET data acquired on a high resolution PET scanner to compare two computational methods, one based on a homogeneous tissue model (BFM) and one that accounts for tissue kinetic heterogeneity (SAIF). With high resolution data and good counting statistics, estimates of the variable of interest in our study, rCPS, were equally good with BFM and SAIF; the impact of kinetic heterogeneity on rCPS estimates was negligible. Thus the BFM analysis based on a homogenous tissue model can be used; this is not only simpler, but results in a substantially lower computational cost. When spatial resolution was reduced by smoothing the data, effects of tissue kinetic heterogeneity on estimates of rCPS were notably higher: BFM overestimated rCPS, a result consistent with applying a homogeneous tissue model to kinetically heterogeneous data. In this case, analyses need to take heterogeneity into account, even though this results in higher computational costs.In this work we explored the impact of tissue kinetic heterogeneity on PET parametric mapping of 11C]leucine studies; this violates the assumption of tissue homogeneity usually standard in PET data analyses and, in particular, in the BFM method used here. It is worth noting that the voxels classified as kinetically heterogeneous tended to be located along borders of gray and white matter tissue, where the partial volume effect would be expected to play a stronger role. The biggest effect of neglecting heterogeneity in the model for quantification of rCPS was found in voxels with the lowest values of rCPS and in regions containing a large fraction of low-rCPS voxels. In particular, neglecting heterogeneity had the largest effect on rCPS in regions that contained low-rCPS voxels interspersed with higher-rCPS voxels. The regions with highest differences between SAIF and BFM estimates of rCPS included the amygdala and hippocampus (15% and 17% respectively). Intermediate differences were found in white matter and cerebellum (~6%). For the remaining regions the two methods produced very similar results (absolute relative differences <5%). Interestingly, the injected tracer dose played a role in the differences between the SAIF and BFM estimates of rCPS, with higher injected doses associated with lower differences. This underlines the importance of good counting statistics for PET imaging studies33.In the high resolution data set, SAIF identification of kinetic heterogeneity highlights that ~45% of brain voxels should be classified as kinetically heterogeneous in L-leucine PET data acquired with the HRRT scanner. Although the applicability of these findings to different tracers, different scanner types or different experimental protocols should be assessed on a case by case basis, the methodology presented here provides a framework for approaching such analyses. Secondly, because of the lack of available L-[1-11C]leucine PET data acquired with a scanner other than the HRRT, the low resolution scenario was artificially created by smoothing the high-resolution data. These conditions do not account for the spatial interaction between nearby voxels due to the finite resolution of the HRRT PET scanner on which the data were acquired (FWHM ~2.6 mm). Thirdly, only two out of 8 subjects were injected with a substantially lower dose of tracer (~0.16 mCi/kg), limiting the inferences that can be made concerning the interaction between tracer dose and tissue kinetic heterogeneity; this topic needs further exploration.Some limitations of the study should be taken into account. First, the results reported here refer to the quite unique case of L-[1-11C]leucine method. Studies have shown that tissue kinetic heterogeneity can be a significant source of bias when PET imaging is used to measure cerebral blood flow, glucose metabolism, or neuroreceptor binding8. Compared to compartmental modelling, Spectral Analysis-based techniques have the advantage of not requiring that the number of compartments be fixed a priori; thus they become applicable to heterogeneous as well as homogeneous tissues without any additional assumptions. Interestingly, measurement of kinetic heterogeneity and its distribution across various tissues has been shown to be an informative parameter linked to the biological “complexity” and can provide useful insight when applied to pathology. In PET oncological studies, for example, tissue kinetic heterogeneity has been shown to provide information about tumor characterization and treatment response35. In acute lung injury the heterogeneity fraction has been linked to the presence of edema36. Further studies are needed to test the reliability of the measure and its biological value in different contexts.The problem of tissue kinetic heterogeneity in PET data quantification is clearly a general problem that goes beyond the measurement of protein synthesis with the L-[1-11C]leucine PET data can be affected by the problem of tissue kinetic heterogeneity. The effect on the variable of interest in our study, rCPS, is negligible when we have good counting statistics and high-resolution data, but it becomes significant with poorer spatial resolution. With high-resolution data and poorer counting statistics small biases in rCPS become apparent, but the effects of kinetic heterogeneity and high noise cannot be independently assessed. These findings may be extended to other PET tracers, to tissues other than brain, or to pathological conditions (as tumors or inflammation) that might increase the level of tissue kinetic heterogeneity, but each condition must be assessed on a case by case basis.In this study we showed how voxelwise analysis of L-[1-Supplementary Information