Critical illness from SARS-CoV-2 infection (COVID-19) is associated with a high burden of pulmonary embolism (PE) and thromboembolic events despite standard thromboprophylaxis. Available guidance is discordant, ranging from standard care to the use of therapeutic anticoagulation for enhanced thromboprophylaxis (ET). Local ET protocols have been empirically determined and are generally intermediate between standard prophylaxis and full anticoagulation. Concerns have been raised in regard to the potential risk of haemorrhage associated with therapeutic anticoagulation. This report describes the prevalence and safety of ET strategies in European Intensive Care Unit (ICUs) and their association with outcomes during the first wave of the COVID pandemic, with particular focus on haemorrhagic complications and ICU mortality.Retrospective, observational, multi-centre study including adult critically ill COVID-19 patients. Anonymised data included demographics, clinical characteristics, thromboprophylaxis and/or anticoagulation treatment. Critical haemorrhage was defined as intracranial haemorrhage or bleeding requiring red blood cells transfusion. Survival was collected at ICU discharge. A multivariable mixed effects generalised linear model analysis matched for the propensity for receiving ET was constructed for both ICU mortality and critical haemorrhage.2 and 13 (7–22) days, respectively. Thromboembolic events were reported in 146 patients (17.1%), of those 78 (9.2%) were PE. ICU mortality occurred in 335/852 (39.3%) patients. ET was used in 274 (32.1%) patients, and it was independently associated with significant reduction in ICU mortality  but not an increased risk of critical haemorrhage .A total of 852  patients were included from 28 ICUs. Median body mass index and ICU length of stay were 27.7 (25.1–30.7) Kg/mIn a cohort of critically ill patients with a high prevalence of thromboembolic events, ET was associated with reduced ICU mortality without an increased burden of haemorrhagic complications. This study suggests ET strategies are safe and associated with favourable outcomes. Whilst full anticoagulation has been questioned for prophylaxis in these patients, our results suggest that there may nevertheless be a role for enhanced / intermediate levels of prophylaxis. Clinical trials investigating causal relationship between intermediate thromboprophylaxis and clinical outcomes are urgently needed.The online version contains supplementary material available at 10.1186/s13054-021-03543-3. A growing body of observational clinical evidence indicates that coronavirus disease COVID‐19) is associated with a high incidence of thrombotic complications 9 is asso. DespiteAlthough observational clinical data suggest that the use of either prophylactic to increased doses of low molecular weight heparins (LMWH) in high-risk patients may be associated with better prognosis, the optimal thromboprophylaxis strategy in the critically ill COVID-19 patient population remains uncertain . In the The purpose of this study was therefore to describe the prevalence of ET strategies in European Intensive Care Unit (ICUs) and to assess their association with ICU mortality and safety in a large cohort of critically ill COVID-19 patients admitted to European ICUs during the first wave of the pandemic.‘standard prophylaxis’) or COVID-19 specific ‘enhanced thromboprophylaxis’ according to local protocols (group ‘ET’) and which molecule was used (presented as two subgroups ‘ET enoxaparin’ and ‘ET other’). Authors were asked to describe local protocols, including whether anti-Xa monitoring was routinely used for titration. Patients already on therapeutic anticoagulation at the time of ICU admission for established or suspected thromboembolic events were included in the group ‘therapeutic anticoagulation for indications other than prophylaxis’.This is an observational, retrospective multi-centre study, including 28 European ICUs. The study was conducted according to the Strengthening the Reporting Observational Studies in Epidemiology (STROBE) Statement guidelines . Each participating centre obtained the approval for data collection from its local Ethical Committee. The need for written informed consent was waived for retrospectively collected data. In each centre, consecutive adult patients (age ≥ 18) with a confirmed COVID-19 diagnosis requiring ICU admission in the period between 26 February and 30 May 2020 were included. Participating units were provided with a study protocol and case report form (CRF). A detailed list of all variables collected in the CRF is provided in supplementary material (section S6). For each centre a trained physician collected, curated and submitted anonymised data for analysis to the coordinating centre . Analysis was performed by AE, CR and AL. Data included patient demographics , past medical history , date of ICU admission and discharge, ICU mortality, mode of death , renal failure , thromboembolic events  and haemorrhagic events . Data regarding any antiplatelet therapy, thromboprophylaxis and therapeutic anticoagulation at ICU admission were also obtained. Participating centres were requested to indicate whether patients were treated with standard prophylaxis , D-dimers on admission, C-reactive protein, fibrinogen, platelet count, white blood cell count, intubation status and antiplatelet agent use, as fixed effects and site code as a random effect. Propensity matched datasets were then constructed using a nearest neighbour approach before fitting additional mixed effects models for ICU survival and for, with the same covariates and random effects on each of these matched datasets. The final results were then pooled.Statistical analysis was performed in R 3.6.3 . The dat2.Data completeness was good and is summarised in supplementary materials   . In non-survivors, the reported mode of death was multiorgan failure 172 (51%), cardiocirculatory collapse 87 (25.8%), respiratory failure 73 (21.7%) and massive haemorrhage in 3 (0.9%). Of the three deceased patients for whom the reported mode of death was massive haemorrhage, one patient was being treated with ET and 2 patients were on standard thromboprophylaxis regimens.enhanced thromboprophylaxis (ET) according to local protocols. The majority 236 (27.7%) received ET with enoxaparin (group ‘ET enoxaparin’) at doses reported ranging from 100 to 200 IU/Kg/day in two divided doses , with correction for renal failure and bleeding abnormalities during the course of ICU stay according to local practice.A total of 274 (32.2%) patients received ET other’) received ET with UFH (38) titrated to heparin ratio of 1.5–2.5, or fondaparinux (one patient).Only one centre (Bruxelles) reported using anti-Xa activity with a target of (0.3–0.5) systematically for dose titration. Thirty-eight (4.5%) patients (group ‘‘standard prophylaxis’). These include 19 (2.2%) patients with contraindications to anticoagulation at the time of ICU admission who received no heparin. Indications for anticoagulation for indication other than prophylaxis  were arterial embolism , deep venous thrombosis , line clotted  and pulmonary embolism .Four hundred and thirty-five (51.1%) received thromboprophylaxis as per standard protocols (group p = 0.4).Thromboembolic events after ICU admission were reported in 114 patients (13.3%), including 57 (6.6%) cases of pulmonary embolism. A crude comparison between ET and standard prophylaxis (after excluding for patients with indications for therapeutic anticoagulation other than prophylaxis at time of ICU admission) does not reveal a statistical difference in reported thromboembolic events (p = 0.0069]) but not an increased risk of critical haemorrhage . Older age and high BMI were found to be associated with a higher log odds of ICU mortality . Increased ICU admission platelet count was associated with increased log odds of ICU mortality ) as was the use of mechanical ventilation . There were no other statistically significant predictors of ICU mortality in the multivariate model.Figure p = 0.64]).Table A sensitivity analysis was performed, repeating both propensity models but excluding all patients who received full anticoagulation for non-prophylaxis indications as these might ‘enrich’ the standard group with patients with higher risk of mortality due to significant thromboembolic disease, biasing the results against standard treatment. However, the results were qualitatively the same and revealed identical statistically significant associations  rather than the coagulation cascade  . MoreoveWhilst it is not disputed that immunomodulation and inhibition of platelet activation are certainly key targets for the care of critically ill COVID-19 patients   [Furthermore, preliminary studies reported a significant reduction in thromboembolic events for critically ill COVID-19 patients treated with empirical ET strategies when compared to standard prophylaxis ( = 0.03) . These f = 0.03) .Massive pulmonary embolism may be a potentially reversible cause of death and therefore a potential therapeutic target in critically ill COVID-19 patients. A case series of post-mortem autopsies found that venous thromboembolism was present in 7 of 12 (58%) patients with COVID-19. The study concluded that pulmonary embolism had been the direct cause of death in a third of cases . This is“as half of these patients were treated with full-dose pre-emptive anticoagulation without a confirmed TE, we must be cautious about our thromboprophylaxis strategy with daily reassessment of its indication” [not associated with an increased chance of death or critical haemorrhagic events.A French single centre study on 92 critically ill COVID-19 patients reported a 40% prevalence of thromboembolic events (TE) and a 21% rate of ‘significant’ thromboembolic events, with most of such events occurring in patients being treated with full dose anticoagulation. The authors concluded: ication” . Whilst Given the high burden of thromboembolic complications associated with standard prophylaxis and the absence of major haemorrhage related mortality, the implementation of ‘enhanced’ thromboprophylaxis strategies seems justified. Whilst the ideal dosing and stratification remains to be determined by randomised clinical trials, the implementation of twice daily standard LMWH prophylaxis appears to be reasonable and has the advantage of limiting staff exposure when compared to continuous UFH infusion and aPTT monitoring. In view of the prevalence of renal impairment in this patient population, careful dose adjusting and anti-Xa and aPTT-ratio monitoring is strongly recommended. Given the high prevalence of thromboembolic events even in the absence of risk factors  and in cOur study also has several important limitations. Firstly, being a retrospective observational dataset, no definite conclusions can be taken in regard to what the ideal thromboprophylaxis strategy for critically ill COVID-19 patients should be as it is impossible to be sure that the propensity score captures the true decision making in instituting ET in an observational dataset. Secondly, as this is an observational and not interventional study, each centre relied on its own screening methods for the detection of thromboembolic complications, without a systematic screening of patients for haemorrhagic and thrombotic events. Also, we limited our observations and anticoagulant therapy at admission and in the early phases of ICU admission, thus reducing the potential effect of long-term anticoagulant strategies. Moreover, the multicentric nature of the study could potentially increase variance and data integration difficulties among different centres, which is simply not possible to correct by means of post hoc analysis. Whilst clearly not as robust in demonstrating causality as a well conducted randomised controlled trial, our propensity score method attempts to exploit ‘natural’ variations in practice within and between sites to remove bias from the ET cohort.Enhanced thromboprophylaxis strategies have been widely and empirically implemented across European ICUs during the first wave of the pandemic. Thromboembolic events remain highly prevalent. Death associated with massive haemorrhage is extremely rare, and it does not appear to be associated with ‘enhanced thromboprophylaxis’ strategies, which in this series consisted in increasing low molecular weight heparin within an intermediate range between standard prophylactic and full therapeutic dose. Within the limitations of its methodology, this study supports the continued use of enhanced intermediate levels of thromboprophylaxis for critically ill COVID-19 patients. Further well designed randomised controlled trials are urgently needed to explore the causal relationship between the dose of anticoagulation received and patients’ outcome in critically ill COVID-19 patients.Additional file 1: Supplementary statistical analysis and results.


Background. Recent studies have reported lower statistics ofupper limb (UL) weakness (48-57%) compared to widely cited values collected over2 decades ago (70-80%). Objective. To explore potential factorscontributing to the accuracy of prevalence values of UL weakness using a casestudy from a single regional centre. Methods. All patientsadmitted to the acute stroke unit with suspected diagnosis of stroke werescreened from February 2016 to August 2017. Upper limb weakness was captured (a)prospectively using the Shoulder Abduction and Finger Extension (SAFE) scoreperformed by unit physical therapists within 7 days post-stroke and (b)retrospectively via chart review using the National Institutes of Health StrokeScale (NIHSS) arm score at admission and 24 hours post-admission.Results. A total of 656 patients were admitted with afirst-ever stroke, and 621 (95%) individuals were administered the SAFE score. Atotal of 40% of individuals had UL weakness using the SAFE score (SAFE ≤8) at amean time of 1.9 (SD 1.5) days post-stroke. In the same sample, 57% and 49% hadUL weakness using the admission and 24-hour post-admission NIHSS arm score,respectively. Conclusions. The accuracy of population-level ULweakness prevalence values can be affected by weakness measure and scorecut-off, time post-stroke weakness is captured, sample characteristics and useof single or multiple sites. Researchers using prevalence values for clinicaltrial planning should consider these attributes when using prevalence data forestimating recruitment rates and resource needs. Forinstance, one large unselected study of first-ever stroke (n = 642) collected in2009/10 in Sweden estimated only 48% of their sample had UL motor impairment.5 Screening data collected in 2017/2018 from an ongoing longitudinal study inSwitzerland reported only 57% of consecutive patients with ischaemic stroke (n =845) experienced UL weakness.6 It is interesting to ponder whether these more recent studies reflect a truechange in UL weakness/motor impairment prevalence, differences in sample and/orstudy methods and/or the increasing use of reperfusion therapies. Capturing a trueprevalence value is challenging. Selected arm weakness/impairment measure, thresholdscores adopted, time of weakness assessment and number of sites data are collectedfrom have the potential to make a large impact on the accuracy of prevalenceestimation aiming to capture the true effect of stroke on the UL. Adding to thecomplexity is that rehabilitation researchers hoping to use prevalence values toinform clinical trial planning may be interested in prevalence studies that alignwith their population of interest, planned time post-stroke at recruitment and typeof institution where their research will take place. The purpose of this study is toexplore potential factors contributing to the accuracy of prevalence values of ULweakness using a case study from a single regional centre. Specifically, we usedscreening data for a prospective longitudinal cohort study to estimate UL weaknessprevalence values using two different weakness measures at three different timepoints post-stroke to highlight the challenges of obtaining true prevalence valuesand/or values that may inform clinical trial planning.The most frequently cited statistic for the prevalence of upper limb (UL) weakness inacute stroke is approximately 70%, provided by data from the Copenhagen Stroke Study(n = 421) collected in 1991/92.This case study used prospective screening data collected from a consecutive samplelongitudinal cohort study examining UL recovery post-stroke.7 While the vast majority of stroke patients are treated locally,approximately 14% of patients8 are transferred from other sites in Metro Vancouver which has apopulation of approximately 2.8 million.7 Another 10% of patients are transferred from health authorities outsideof Metro Vancouver and would have required air ambulance. Data were collectedfrom only those individuals with a diagnosis of ischaemic stroke or primaryintracerebral haemorrhage confirmed by CT or MRI. Individuals with subarachnoidhaemorrhage, cerebral venous thrombosis and those assessed greater than 7 daysafter their stroke (N = 35) or receiving palliative care (N = 11) were excluded.First-ever stroke admission was the primary unit of analysis in this study.Prior stroke was determined through physician history notes. Individuals whosechart was inaccessible were excluded as information regarding recurrent strokewas missing (N = 24).All individuals with a suspected diagnosis of stroke admitted to the stroke unitof a regional hospital  between February 28, 2016and August 31, 2017 were screened for study inclusion. This regional hospitalhas a comprehensive tertiary stroke centre, serving the Vancouver citypopulation of approximately 675 000. It is one of only three comprehensivecentres in the province (population 4.6 million).9 Physical therapists completed SAFE scoring unless circumstances preventedadministration (ie. discharge and unwell). Upper limb weakness was defined as aSAFE score of 8 or less. This cut-off was selected based on the original PREPalgorithm which states that a SAFE score of 8 or above is predictive of a fullrecovery, which is defined as the potential to return to normal or near-normalhand and arm function within 12 weeks.10 We chose a more conservative cut-off score of 8 for our longitudinalstudy because a SAFE score of 8 could represent the combination of normalstrength (ie. 5/5) in one component and more significant weakness in the othercomponent (ie. 3/5-antigravity movement without resistance). To reduce burden,therapists were allowed to indicate ‘no weakness’ on the SAFE forms ifparticipants had a score greater than 8. Thus, SAFE scores of 9 or 10 were notalways indicated on the form. Weakness categories were further defined as severe(0-4) and mild/moderate (5-8) and little to none (>8). In general, severe ULweakness refers to no active movement through to movement when gravity iseliminated, and mild/moderate weakness refers to movement against gravity withstrength reduced.Upper limb weakness was captured using the Shoulder Abduction and FingerExtension (SAFE) score. The SAFE score is the sum of the Medical ResearchCouncil (MRC) strength gradings (0-5) for shoulder abduction and fingerextension  and it is shown to be highly predictive of ULfunctional recovery after stroke.Upper limb weakness was also captured retrospectively via chart review using theNational Institutes of Health Stroke Scale (NIHSS) arm score indexed atadmission and 24 hours post-admission. The NIHSS arm score captures weakness ona 5-point scale after asking patients to keep their arm raised against gravityfor 10 seconds (90° in seated position and 45° in supine position). Scores rangefrom 0 (no drift) to 4 (no movement). Weakness categories were further definedusing the NIHSS arm score as severe (3-4) (no movement or effort againstgravity); mild/moderate (1-2) (weak antigravity movement) and little to none(0). Demographic and clinical information (including length of stay anddischarge disposition) were collected from patient medical records. Ethicsapproval was obtained from the local university and hospital review boards.Informed consent was not required as data were collected in the process ofscreening for a consecutive sample observation study.Descriptive statistics were used to characterize the sample. All admission SAFEforms which indicated ‘no arm weakness’ were given a numerical score of 9.Half-ratings (ie. ‘+’or ‘−’) were ignored unless total numerical score was 8.One or more ‘+’half-rating with a score of 8 was considered ‘no arm weakness’.Distribution of weakness scores for the initial NIHSS arm scale was determinedfor individuals with SAFE and initial NIHSS arm scores. Distribution of weaknessscores for the 24-hour post-admission NIHSS arm score was determined forindividuals with completed SAFE, initial NIHSS and 24-hour post-admission armscores. This was done to compare weakness category proportions in a consistentsample. All available baseline and 24-hour NIHSS arm scores were also analysedto determine the proportion of those with UL weakness using the full NIHSS armscore datasets.A total of 1087 admissions were screened, and 808 admissions met the inclusioncriteria . AtotalImportantly, 22.3% (N = 122) of individuals with initial NIHSS arm and SAFE scoreshad weakness according to the initial NIHSS arm score (NIHSS arm score >1) butwere classified as having little to no weakness according to the SAFE (SAFE score>8). In addition, 5.7% (N = 31) of individuals with initial NIHSS arm and SAFEscores had no weakness according to the initial NIHSS arm score (NIHSS arm score =0) but were classified as having weakness according to the SAFE (SAFE≤8).Finally, 14 except for the one 2009/10published statistic of 48%.5 The 2017/2018 statistic of 57%6 which used the NIHSS arm score was very similar to our prevalence value usingthe full dataset of admission NIHSS arm scores (59%). These data may suggest thatfewer people may experience UL weakness early after stroke compared to the 1990s.However, one should be aware of the limitations of these metrics. Our data show thepotential effects that metrics and score cut-offs for characterizing weakness, timeof weakness measurement and differences in sample characteristics can have on theaccuracy of prevalence values. Indeed, variation in these above factors couldpotentially explain observed disparities in weakness prevalence across studies.This case study, which used screening data for a prospective longitudinal cohortstudy examining UL recovery, found that 40% of the 621 individuals administered theSAFE at a mean of 2 days post-stroke experienced UL weakness as defined by a SAFEscore ≤8. Importantly, weakness data collected retrospectively using the admissionNIHSS arm scores in our cohort resulted in a prevalence value almost 20% higher thanthat captured by the SAFE approximately 2 days later. Despite the differences inthese values, both these proportions are less than the 69-77% from earlierstudies911 The SAFE score used inour case study and the arm and hand component of the Scandinavian Stroke Scale(SSS) used in the Copenhagen Stroke Study only capture weakness in twocomponents (ie. shoulder and hand). It is possible that critical motorimpairment could exist in other movements captured by a multicomponent motorimpairment measure such as the Motor Assessment Scale (MAS) or Fugl-Meyer.Interestingly, the SAFE, MAS and SSS arm and hand components are all stronglycorrelated to the Fugl-Meyer however and thus are likely picking up a similarconstruct of motor impairment.12 In addition, the authorsof the PREP2 algorithm found that the SAFE and Fugl-Meyer had the same accuracyfor predicting functional recovery at 3 months.9Selection of UL weakness measure is an important decision in the design of a ULweakness prevalence study. Feasibility of measurement has to be weighed againstthe potential effect on sensitivity. The SAFE score was selected as the ULweakness screening measure for our longitudinal study due to its quickadministration with our large cohort and its ability to predict functionalrecovery at various time points.6 (57%) could therefore be underestimating the true prevalence of ULweakness at the time of hospital admission. Future studies comparing thecorrelation and predictive ability of the NIHSS arm scale and othermulticomponent measures of weakness are thus warranted. Interestingly, three ofthe four early studies, apart from the Copenhagen Stroke Study, did not specifythe weakness measure used. They were all accessed via the medical chart orthrough a medical exam.24 It is thusunknown whether they captured weakness at a single or multiple joints andwhether they captured strength against resistance. Despite this lack ofinformation concerning the precise UL measure used in the majority of theearlier studies, UL weakness prevalence from all four earlier studies fellwithin a range of 8% (ie. 69-77%).The NIHSS arm score is a potentially convenient measure of UL weakness to captureweakness prevalence due to its widespread use. However, the NIHSS only measuresweakness in one joint and only captures antigravity movement without resistance(MRC strength score 3). Prevalence as captured by the NIHSS arm scale used inour study (59%) and the Swiss study13 may be interested in prevalence values that more closely reflect theirpopulation of interest.The selection in weakness/motor impairment score cut-off is another importantfactor when considering the sensitivity of UL weakness prevalence values forcapturing the full spectrum of weakness. All studies that specified the ULmeasure used, apart from our study, defined weakness/motor impairment as 1 pointless than the measure’s maximum score. The low UL weakness prevalence found inour study could have been influenced by our weakness definition (ie. SAFE score<9 vs SAFE score <10). This selected cut-off was based on the PREPalgorithm, which predicts that individuals with a SAFE score of 8 or more at3 days post-stroke will achieve a full recovery at 3 months post-stroke.However, exclusion of this group of people has implications on the sensitivityof our prevalence value obtained. While individuals with a SAFE score of 9 maybe able to produce mild resistance against gravity in either finger extension orshoulder abduction, this group still experiences weakness that may benefit fromexisting rehabilitation interventions. Importantly, our prevalence value wouldlikely be substantively higher than 40% if our threshold of weakness includedindividuals with SAFE scores of 9. Unfortunately, we were not able to determinethe number of individuals who had SAFE scores of 9 vs 10 because we allowedtherapists to indicate ‘no weakness’ on the score forms if patients had scoresabove 8. Furthermore, it should be noted that a maximum score on the NIHSS armscore does also not necessarily equate to full strength as it does not measurestrength against resistance. Thus, prevalence values based on a weaknessdefinition of NIHSS arm score> 0 may not be capturing the full spectrum ofweakness. Nonetheless, researchers planning future rehabilitation clinicaltrials that follow the recommendations by the international Stroke Recovery andRehabilitation Roundtable to target individuals with more moderate to severe UL impairment14 This study compiled data from six studies1520 (N = 373) exploringprediction of motor recovery captured by the Fugl-Meyer. The authors highlightedthat the majority of initial Fugl-Meyer scores (captured <2 weekspost-stroke) in their compiled dataset was less than 12 points out of 66, whichcoincides with severe UL impairment.21 Furthermore, in our sample, we found that 62% of people within the severecategory had a SAFE score of 0. This is concerning given the dearth ofevidence-based rehabilitation treatments for people with severe UL impairmentand our finding that these individuals are least likely to go home.22 Importantly, interventions are needed for this population to help movethem towards a more moderate level of impairment so that they too canparticipate in and benefit from intensive therapy programmes that involve highdoses of active movement repetitions. Furthermore, future studies may determinewhether the distribution of UL weakness  is related tofactors such as corticospinal tract damage or psychometrics of the measurementscale (eg. floor/ceiling effects).Understanding the severity profile of UL weakness is useful for researchersplanning future clinical trials that target specific severity groups. Oursingle-site study found that approximately one-third of people with UL weaknessexperienced mild/moderate weakness compared to approximately 50% in theCopenhagen Stroke Study. The SAFE score cut-off selected could have impacted theapparent increase in proportion of severe weakness observed in our studycompared to the Copenhagen Stroke Study. The addition of the individuals withSAFE scores of 9 would increase the tally of individuals with mild weakness andmay even up the proportion of those with severe/mild-moderate weakness. Inaddition, the proportion of individuals with severe stroke appeared to greatlyincrease when weakness was captured by the SAFE score compared to when capturedby the 24-hour post-admission NIHSS arm scores. This could reflect any of thefollowing: worsening of UL weakness, biased make-up of missing data in the24-hour post-admission NIHSS arm scores (27% missing) or differences in howweakness is captured and categorized across the SAFE and NIHSS arm scales. Thisfurther highlights the complexity of comparing prevalence rates across differenttime points using different weakness measures. Our finding that a greaterproportion of those with weakness experience severe weakness (as captured by theSAFE score) is consistent with a 2019 opinion article written by Hawe et al.Finally, discharge data from our single centre may highlight potential challengesfor researchers conducting UL rehabilitation trials in other comprehensivestroke centres trying to recruit individuals with severe weakness. Approximately44% of individuals with severe weakness in our case study were eithertransferred to another hospital or died before discharge making recruitment ofthis population into rehabilitation trials very challenging. Multiple sites maybe necessary to complete acute rehabilitation clinical trials for individualswith severe impairment in a timely manner.4 Nakayama et al,1 Rathore et al4 and Held et al6 captured weakness at admission to the hospital, and thus, the majority ofcases are presumed to have been assessed within 24 hours. Importantly, latertime points in which weakness/motor impairment was assessed in more recentstudies were likely a significant factor for observed differences in weaknessprevalence values between earlier and more recent studies. Persson et al5 and our study captured weakness/motor impairment at a slightly later timepoint of 1.5 days post-admission and 1.9 days post-stroke, respectively, whichreflects weakness after acute reperfusion therapies have been administered. Thepotential time difference in weakness assessment of a day or two is not atrivial one. For instance, the prevalence value of 57% found in the Held et al6 and captured at admission to the hospital decreased to 46% captured at ascreening evaluation where 70% of evaluations took place within 48 hours ofstroke. In addition, we found a prevalence rate of 57% using the initial NIHSSarm score, but this value decreased to 40% using the SAFE when captured at amean time of 1.9 days post-stroke in the same sample. Moreover, the 153individuals classified differently according to the initial NIHSS arm task vsSAFE could represent true recovery or deterioration across the time in betweenthese 2 measures’ administration or could represent differences in how weaknessis measured and defined. This point highlights the importance of including atime qualifier when citing UL prevalence values. Indeed, the inclusion of a timequalifier allows consumers of prevalence data to gauge which information is mosthelpful for them. For instance, researchers hoping to conduct clinical trials inthe acute phase may want to know rates of weakness closer to the time they aretrying to recruit participants. Furthermore, future studies that use the sameweakness measure in a non-selected sample and capture weakness at very fineintervals within the first week post-stroke are needed to examine the timecourse of UL weakness prevalence early after stroke.There was variation across studies in the time weakness/motor impairment wasassessed. Two of the earlier studies captured weakness at the time of maximumdeficit and therefore would likely obtain higher prevalence values in theirsample than if they captured weakness at a fixed time point.1; Lawrence et al3 reported N = 39 individuals unable to assess at the time of maximumimpairment and Kotila et al2 reported excluding 101 individuals who died within the study period. Thisissue further highlights the difficultly of including people with severe strokein research studies.Differences in sample characteristics could also contribute to the accuracy ofarm weakness/motor impairment prevalence and differences in observed statisticsacross studies. Exclusion of individuals based on the inability to access them(ie. due to early discharge) or difficulty assessing  can impact the sensitivity of prevalence estimates.Indeed, our case study excluded individuals who were assessed using the SAFEgreater than 7 days post-stroke (N = 36) and those whose SAFE score could not beobtained due to early death, being unwell or having severe aphasia (N = 18). Itis possible that these individuals had severe UL stroke which could result inthe underestimation of the true prevalence of UL weakness as captured by theSAFE assessment in our study. Importantly, this appears to be a challenge notunique to our case study. For instance, the Copenhagen Stroke Study reported N =44 individuals unable to assess at admission6 only included ischaemic stroke. One might expect weakness prevalence instudies that excluded haemorrhagic stroke to be lower than studies that includedthem.Upon visual inspection, the variation in the mean age does not appear to form apattern in those studies finding high or lower prevalence values. The inclusionor exclusion of individuals with haemorrhagic stroke could potentially explainsome of the differences in prevalence across studies. Six of the seven reportedstatistics included individuals with haemorrhagic stroke whereas the 2017/2018Swiss data6 Both of these studies found similar UL weakness prevalence captured bythe NIHSS arm scale at admission to their respective hospitals (ie. 59% and57%). The median stroke severity in the study of Persson et al5 was lower. However, their reported value should be treated with cautiondue to the large percentage of missing total NIHSS scores (46%). It is possiblethat greater public awareness of stroke symptoms and the improved recognition ofminor stroke/TIA as a neurological emergency has led to greater numbers ofpeople with mild stroke being admitted to the hospital in recent years.23 Interestingly, we did not find a large difference in proportion of mildstrokes between our study and the Copenhagen Stroke Study, which is the onlyearlier study to report overall stroke severity of the sample. In the latter,40% had a mild stroke as defined by the SSS, while we found 35% of our cohorthad a mild stroke after converting NIHSS initial scores to SSS scores (N = 482).24 While the expert care of the comprehensive stroke centre from our studymight potentially contribute to lower prevalence of UL weakness, the nature ofcomprehensive stroke centres with 24/7 access to stroke neurologist specialistsoften results in a more complex stroke patient population. Moreover, asdifficult cases are transferred from surrounding areas, the additional transfertime would contribute to worse outcomes.The distribution of overall stroke severity could also impact observed ULweakness prevalence. The level of stroke severity in our study, as captured bythe NIHSS, was similar to that of Held et al.25 Thus, any potential effect of reperfusion rates on our observed ULweakness prevalence may not generalize to non-comprehensive stroke centres.The rapid administration of reperfusion therapies for individuals with ischaemicstroke has greatly advanced stroke care. It is difficult to determine the effectof reperfusion therapies on UL weakness in the absence of randomized controltrials that include specific UL weakness measures. The apparent decrease in ULweakness found in more recent studies could be largely due to the later timepoint at which weakness was captured or could be due to other differences instudy and sample characteristics. Nonetheless, it is reasonable to expect thatrates of weakness/motor impairment might be lower after receiving thesetherapies compared to rates at admission due to the fact that reperfusiontherapies have become standard of care due to their reported effects ondisability. The prevalence of UL weakness/motor impairment after any reperfusiontherapies have been administered is arguably the most relevant to researchersand healthcare administrators as rehabilitation services and clinical trialsconducted in the acute phase will most certainly be starting after reperfusiontherapies have been administered. Nonetheless, more studies are needed tocapture prevalence both at admission and within a few days post-stroke todetermine how or if UL weakness prevalence is changing. In addition,thrombolysis rates in comprehensive stroke centres have previously been shown tobe much higher than those observed in primary stroke centres.1 and the Held et al6 study may not generalize to the greater population of individuals withstroke. The increasing collection of multi-centred stroke databases (eg GreaterCincinnati/Northern Kentucky Stroke Study) may provide more accurate estimatesin the future.Finally, more accurate estimations of UL weakness prevalence on the populationlevel are obtained from multi-site data. Estimates obtained from single sitessuch as those from our study, the Copenhagen Stroke StudyThis case study observed a lower rate of UL weakness (40% and 59%) than expected. Avariety of factors may have contributed to our lower values such as exclusion ofindividuals with mild weakness, exclusion of some individuals with severe stroke,weakness captured at a later time post-stroke and high reperfusion rates andhighlights the challenges of obtaining accurate population-level estimates of ULweakness prevalence early after stroke. Researchers using UL prevalence data toinform clinical trial planning should consider differences between their plannedtime of recruitment and UL impairment inclusion criteria to those used in prevalencestudies. They should also consider the added challenges of recruiting individualswith severe stroke and consider multiple sites to support the importantinvestigation of new interventions aimed at individuals with severe UL weakness.


Gut microbiome studies in multiple sclerosis (MS) patients are unravelling some consistent but modest patterns of gut dysbiosis. Among these, a significant decrease of Clostridia cluster IV and XIVa has been reported. In the present study, we investigated the therapeutic effect of a previously selected mixture of human gut-derived 17 Clostridia strains, which belong to Clostridia clusters IV, XIVa, and XVIII, on the clinical outcome of experimental autoimmune encephalomyelitis (EAE). The observed clinical improvement was related to lower demyelination and astrocyte reactivity as well as a tendency to lower microglia reactivity/infiltrating macrophages and axonal damage in the central nervous system (CNS), and to an enhanced immunoregulatory response of regulatory T cells in the periphery. Transcriptome studies also highlighted increased antiinflammatory responses related to interferon beta in the periphery and lower immune responses in the CNS. Since Clostridia-treated mice were found to present higher levels of the immunomodulatory short-chain fatty acid (SCFA) butyrate in the serum, we studied if this clinical effect could be reproduced by butyrate administration alone. Further EAE experiments proved its preventive but slight therapeutic impact on CNS autoimmunity. Thus, this smaller therapeutic effect highlighted that the Clostridia-induced clinical effect was not exclusively related to the SCFA and could not be reproduced by butyrate administration alone. Although it is still unknown if these Clostridia strains will have the same effect on MS patients, gut dysbiosis in MS patients could be partially rebalanced by these commensal bacteria and their immunoregulatory properties could have a beneficial effect on MS clinical course.The online version contains supplementary material available at 10.1007/s13311-021-01016-7. Multiple sclerosis (MS) is an autoimmune, degenerative, chronic, and demyelinating disease that affects the central nervous system (CNS) . As a coin vivo studies have shown that a cocktail of 17 Clostridia strains isolated from a human faecal sample, which partially belong to previously-mentioned clusters, promoted both the number and function of regulatory T (Treg) cells in the colon and successfully suppressed experimental colitis in mice . At 0 and 2 days postimmunisation (dpi), mice were intravenously injected with 250 ng of pertussis toxin . Mice were weighed and examined daily for neurological signs in a blinded manner and corrective measures and endpoint criteria to ensure EAE-incident animals welfare were included as previously detailed -thymidine  were added to each well. Splenocyte cultures were maintained under the same conditions for an additional 18 h and incorporated radioactivity was measured in a beta-scintillation counter . Five replicates per experimental condition  were assessed per mouse. Stimulation indices were calculated by dividing the mean counts per minute (cpm) of MOG35-55 or PHA-L condition by the mean cpm of the control condition. The results are shown as the stimulation index.Splenocyte suspensions were prepared by grinding spleens through a 70-μm nylon cell strainer at the end of the experiment (28 or 35 dpi). Splenocytes were seeded at 2 × 10detailed . Splenoc35-55-stimulated splenocytes by using a ProcartaPlex Multiplex Immunoassay , according to the manufacturer’s instructions. Samples were acquired in a Magpix instrument  and data were analysed with a ProcartaPlex Analyst software . The results are shown as the concentration of each cytokine (pg/ml).Cytokine secretion pattern  was assessed in the supernatants of MOGex vivo stimulation of splenocytes was performed as previously detailed  . Regarding the biological effect of Clostridia treatment in the periphery, Clostridia-treated mice showed an increased defence response to other organisms that was partially related to the innate immune response, the IL-1 production, and the response to IFN-β (adjusted p < 0·1 and NES > 2) .Clostridia strains reduced the activation of the immune response in general and T cells in particular, the lymphocyte differentiation, and the mononuclear cell proliferation  in the CNS of EAE mice [·3] Fig. a. Beside 2) Fig. b. This dp = 0·047) compared to that in vehicle-treated mice  .Since the administered Clostridia strains are highly known for their capacity of SCFA production, we studied whether the levels of several SCFA such as acetate, propionate, and butyrate, were altered as a result of the oral administration of these bacteria. The concentration of the SCFA butyrate was increased in the serum of Clostridia-treated mice  compared to vehicle treatment  as a preventive approach  compared to vehicle treatment . As expected, butyrate-treated mice also reflected their clinical improvement in other clinical variables such as the accumulated weight loss  and motor coordination skills  compared to vehicle group  , from seven days before mice immunisation to the end of the experiment (35 dpi), significantly reduced the clinical signs of EAE mice    Fig. e and CNS34) Fig. f were di35–55) and polyclonal (PHA-L) immune responses were not altered by the preventive administration of the SCFA butyrate  compared with those in the vehicle group  (data not shown). Finally, no between-group differences in the cytokine secretion pattern  were observed for antigen-specific stimulatory condition (data not shown).On the other hand, antigen-specific (MOGp = 0·069) compared to that in vehicle-treated mice    was estimated in the preventive approach and it was identified as an effective dose in the EAE model, 100 mg of this SCFA was selected as the suitable butyrate quantity for the therapeutic approaches. Mice were treated once daily with butyrate or vehicle after attaining a clinical score equal to or greater than 1 and being randomised into clinically equivalent experimental groups, from 13–15 dpi to the end of the experiment (28 dpi). A slight clinical effect was observed in the butyrate group , T cell inflammatory infiltrate density , and astroglia reactivity  were slightly reduced in butyrate-treated mice compared to vehicle treatment   and the vehicle group   than in the control group   or polyclonal immune responses  compared to the vehicle group as a therapeutic approach (data not shown).Within the spinal cord white matter, demyelination  is consistent with these gene expression results. A few immune pathways were also downregulated in the CNS of Clostridia-treated mice. The reduction in the cell surface interactions at the vascular level may be related to the downregulation of genes related to the immune cell proliferation in the CNS as well as to the upregulation of genes related to the response to IFN-β in the periphery. Since the IFN-β limits immune cell trafficking to the CNS by downregulating T cell adhesion molecules that interacts to BBB adhesion molecules , the lowin vitro activation of DCs and subsequent promotion of Treg cells as well as in vivo generation of Treg cells in peripheral compartments [The SCFA butyrate and, to a lesser extent, propionate, have been demonstrated to foster artments . Moreoveartments . Therefoex vivo. Additionally, the SCFA butyrate seems to behave similar to propionate treatment since only preventive but not therapeutic oral administration after the EAE induction phase had a significant beneficial effect on EAE clinical course [Since our results, together with previous studies, connected the SCFA butyrate with immunoregulatory responses, we decided to test the oral administration of butyrate in the chronic EAE model. In our experimental model, preventive administration of butyrate performed a beneficial effect on CNS autoimmunity by halting both demyelination and CNS inflammation. These results are in accordance with previous studies that reported a significant amelioration of CNS demyelination in a cuprizone model as a preventive approach . Considel course . Neverthl course . Oral prl course . Thus, tin vivo assays with the SCFA butyrate proved its preventive effect on CNS autoimmunity but a slight therapeutic clinical impact on EAE clinical course. Our results suggest that the therapeutic effect performed by this commensal bacteria population was not exclusively related to the SCFA and could not be reproduced by butyrate administration alone. Although it is still unknown whether these 17 Clostridia strains will have the same effect on MS patients, previously-defined gut dysbiosis regarding Clostridia cluster IV and XIVa in MS patients could be rebalanced by Clostridia strains administration and its immunoregulatory properties could have a beneficial effect on MS clinical course.Our data show that the human gut-derived Clostridia strains improved the clinical outcome of established EAE as a therapeutic approach. The observed clinical improvement was related to lower histopathological features in the CNS and was associated to an enhanced immunoregulatory response of Treg cells in the periphery. Likewise, transcriptome studies highlighted increased antiinflammatory responses related to IFN-β in the periphery and lower activation, differentiation, and proliferation of immune cells in the CNS. Further ESM 1(PDF 1225 KB)ESM 2(PDF 1225 KB)ESM 3(PDF 1225 KB)ESM 4(PDF 1225 KB)ESM 5(PDF 1225 KB)ESM 6(PDF 1546 KB)ESM 7(PDF 1225 KB)ESM 8(PDF 1225 KB)ESM 9(PDF 1225 KB)ESM 10(PDF 1225 KB)ESM 11(PDF 1225 KB)ESM 12(PDF 1225 KB)ESM 13(PDF 1226 KB)in vitro, supernatants were harvested to further assess cytokine secretion pattern. Clostridia strains do not alter the secretion pattern of disease-related cytokines in the supernatants of autoreactive splenocytes. The graphs show the results of a representative experiment . The data are presented as the means ± standard deviations. Abbreviations: GM-CSF: granulocyte–macrophage colony-stimulating factor, IFN: interferon, IL: interleukin, TNF-α: tumour necrosis factor alpha.Supplementary file14 (TIF 24543 KB) Supp. Fig.1 Clostridia strains do not alter key disease-related cytokine secretion pattern in the supernatants of autoreactive splenocytes. Splenocyte cultures were prepared at the end of the experiment (28 dpi) and stimulated with 5 μg/ml MOG35-55. After 54 h Below is the link to the electronic supplementary material.


STimulator of INterferon Genes (STING) is an adaptor for cytoplasmic DNA sensing by cGAMP/cGAS that helps trigger innate immune responses (IIRs). Although STING is mostly localized in the ER, we find a separate inner nuclear membrane pool of STING that increases mobility and redistributes to the outer nuclear membrane upon IIR stimulation by transfected dsDNA or dsRNA mimic poly(I:C). Immunoprecipitation of STING from isolated nuclear envelopes coupled with mass spectrometry revealed a distinct nuclear envelope-STING proteome consisting of known nuclear membrane proteins and enriched in DNA- and RNA-binding proteins. Seventeen of these nuclear envelope STING partners are known to bind direct interactors of IRF3/7 transcription factors, and testing a subset of these revealed STING partners SYNCRIP, MEN1, DDX5, snRNP70, RPS27a, and AATF as novel modulators of dsDNA-triggered IIRs. Moreover, we find that SYNCRIP is a novel antagonist of the RNA virus, influenza A, potentially shedding light on reports of STING inhibition of RNA viruses.  •A nuclear envelope pool of STING redistributes upon innate immune response activation•STING has a unique set of nuclear partners that contribute to innate immune responses•RNA- and DNA-binding STING partners may promote IRF3 transcription factor activation•STING nuclear partners may explain its protection against RNA viruses Molecular physiology; Immunology; Virology; Cell biology STING (STimulator of INterferon Genes), also called MITA, ERIS, MPYS, NET23, and TMEM173, is an important player in the innate immune response (IIR), the first line of defense against pathogens , yet sevThus far, all these roles have been presumed to occur in the cytoplasm, but the recent finding that STING partner cGAS is also present in the nucleus  raises tThe finding of cGAS, STING’s upstream partner in cytoplasmic dsDNA sensing, in the nucleus  is extreHere we confirm INM residence for endogenous STING and show that INM STING-GFP redistributes from the nucleus to the ER upon treatment with dsDNA or, surprisingly, the dsRNA mimetic poly(I:C). We further show that STING mobility in the NE increases with both DNA- and RNA-triggered immune responses. Moreover, we identify partners of NE localized STING, which are enriched for RNA- and DNA-binding proteins, and testing several of these partners indicates that they can contribute to IIR activation. Importantly, one of the partners identified, SYNCRIP, can protect against infection with the RNA virus, influenza A.An earlier attempt to determine if the NE pool of STING was in the outer nuclear membreane (ONM) or INM was inconclusive . TherefoAs the super-resolution approach used STING fused to GFP, we also tested for an endogenous STING NE pool by immunogold electron microscopy (EM) A. To tesFinally, we confirmed the INM pool of STING by a third approach, the ability of C-terminally tagged STING-RFP to accept photons from lamin A-GFP through Förster resonance energy transfer by fluorescence lifetime imaging (FRET-FLIM). The lifetime of the activated lamin A-GFP fluorescence was reduced when cells also expressed STING-RFP as a photon acceptor E. On aveOne possible explanation for STING-GFP being in the INM of some cells but not in others B is thatWe used fluorescence recovery after photobleaching (FRAP) to test if IIR activation promotes STING shuttling between the nucleus and cytoplasm. For a control that would similarly avoid unintentional IIR induction due to transfected plasmid DNA, we generated a matched NET55-GFP-expressing HT1080 cell line. Induction of IIRs by infection with herpes simplex virus type 1 (HSV-1) visibly increased the speed of fluorescence recovery A, reduci2/s in the poly(I:C)-treated cells and to 0.49 μm2/s in dsDNA-treated cells  microscopy, which enables the tracking of individual NETs as they diffuse along the INM and ONM of the NE . The nuced cells G and 2H.Having established that STING is present in the INM, we sought to investigate its role in the nucleus by identifying NE-specific partners that may have been missed in earlier studies because STING’s association with the intermediate filament lamin polymer renders this pool highly insoluble . To mainThe proteins that co-IP'd in the STING-cross-linked NEs were weighted for likely abundance based on spectral counts and plotted based on GO biological process terms. This revealed enrichment in proteins with GO terms for chromatin/chromosome organization and RNA/DNA binding compared with their representation among all proteins encoded by the genome E. PlottiDownstream of STING ER/Golgi functions, IRF3/7 transcription factors induce IFN and other IIR genes in the nucleus. Therefore, we wondered if some of these STING NE co-IP partners have known interactions with IRF3/7 and may modulate immune-signaling cascades. Accordingly, we searched the HPRD interactome database  using CyTo test whether putative partner proteins identified in the NE STING co-IP experiment are involved in dsDNA-triggered IIRs, we used a dual-luciferase reporter system in combination with siRNA-mediated knockdown of 7 partner proteins with links to IRF3/7 A to testTo further confirm the role of these STING NE co-IP partners in IIRs, independent of the luciferase assay system, we measured transcripts of IFNβ with the various knockdowns in HT1080 cells A ± initiWe wondered if the STING redistribution observed in Given the potential links between NE STING partners and IRF3/7 and the effects on IFNβ induction, we decided to look at whether their knockdown affected IRF3 activation as measured by IRF3 phosphorylation. IRF3 phosphorylation upon treatment with plasmid DNA was reduced when STING or its NE co-IP partners were knocked down compared with cells treated with control siRNA F. In conAlthough none of the STING co-IP partners tested were found to have negative effects on poly(I:C)-stimulated IFN expression E, we dectrans phosphorylates itself and neighboring STING dimers  increases STING mobility while promoting its redistribution away from the INM suggests a possible mechanism for STING protection against RNA viruses. This function seems to use a noncanonical pathway because STING does not redistribute from the ER to Golgi perinuclear foci with poly(I:C) treatment. Because STING is reported to not directly bind RNA or poly(I:C) , these pThat the INM STING pool can mobilize to translocate to the ONM through the peripheral channels of the NPC may reflect a backup mechanism to signal IIRs using the peripheral NPC channels when viruses inhibit the central channel transport. Viruses often target the central channel of the NPCs to either block transport or usurp it so that virus transcripts are preferentially transported over host-directed transport , but theThe increasing complexity of STING interactions and its multiple pathways for activating IIRs make confirmation of STING's involvement in the IIR contributions of these STING partners difficult. Nonetheless, they clearly can contribute to IIRs from the measures shown here, and several reports in the literature support this when reevalutated in light of our results. MEN1 binds and represses the activity of the AP1 transcription factor JunD , and theThe STING-NE co-IP partners that bind RNA also have several previously reported functions that would be consistent with their ability to support IIRs indicated here. For example, the DDX5 targeting by the N(pro) protease of pestivirus presumably counters host antiviral defenses , 2014b. Other links to viral infection for these newly identified STING partners include the hepatitis B virus HBx protein that alters the intracellular distribution of RPS27a . AdditioIdentifying partners of proteins in the INM is inherently tricky. This is because most such proteins bind to both chromatin and the intermediate filament lamin polymer while also being embedded in the membrane where they could also have luminal partners. Chromatin is inherently insoluble as is the lamin polymer that only becomes soluble in 7 M urea, and the solubilization conditions required to extract these proteins from the membrane and break INM proteins away from these structures are likely to break other partner protein interactions. While our reversible cross-linking approach is the best way we can find to get around these problems, it also has the limitation that there is a slightly higher possibility that partners identified could be indirect as part of larger complexes.


BAP1 have been associated with poorer prognosis in rhabdoid meningioma and meningioma with rhabdoid features, and germline mutations have been linked to a hereditary tumor predisposition syndrome (TPDS) predisposing patients primarily to melanoma and mesothelioma. We present the first report of a familial BAP1 inactivating mutation identified after multiple generations of a family presented with meningiomas with rhabdoid features instead of with previously described BAP1 loss-associated malignancies. A 24-year-old female presented with a Grade II meningioma with rhabdoid and papillary features treated with subtotal resection, adjuvant external beam radiation therapy, and salvage gamma knife radiosurgery six years later. Around that time, her mother presented with a meningioma with rhabdoid and papillary features managed with resection and adjuvant radiation therapy. Germline testing was positive for a pathogenic BAP1 mutation in both patients. Sequencing of both tumors demonstrated biallelic BAP1 inactivation via the combination of germline BAP1 mutation and either loss of heterozygosity or somatic mutation. No additional mutations implicated in oncogenesis were noted from either patient’s germline or tumor sequencing, suggesting that the inactivation of BAP1 was responsible for pathogenesis. These cases demonstrate the importance of routine BAP1 tumor testing in meningioma with rhabdoid features regardless of grade, germline testing for patients with BAP1 inactivated tumors, and tailored cancer screening in this population.Meningioma is the most common primary brain tumor, and recurrence risk increases with increasing WHO Grade from I to III. Rhabdoid meningiomas are a subset of WHO Grade III tumors with rhabdoid cells, a high proliferation index, and other malignant features that follow an aggressive clinical course. Some meningiomas with rhabdoid features either only focally or without other malignant features are classified as lower grade yet still recur early. Recently, inactivating mutations in the tumor suppressor gene  Meningioma is the most common primary brain tumor, and recurrence risk increases with increasing WHO Grade from I to III . RhabdoiBAP1 that codes for the breast cancer (BRCA)1-associated protein, an important member of many vital pathways including DNA damage signaling and repair (BAP1 display a hereditary tumor predisposition syndrome (TPDS) leading to early onset malignancy – most frequently melanoma, mesothelioma, and renal cell carcinoma  presented with decreased left facial sensation, blurry vision, left-sided frontal headaches, and pain with mastication of 3 months’ duration refractory to symptom-directed medical management. Physical exam revealed left masticator weakness, diminished sensation in the left V1-V3 distribution, and a decreased left-sided corneal reflex but was otherwise unremarkable. Because of the subtotal resection of Grade II disease, adjuvant radiation therapy was recommended to 5940 cGy in 33 daily fractions. The patient was simulated in the supine position with an aquaplast mask for head and neck immobilization. To delineate treatment volumes, preoperative and postoperative T1-weighted axial postcontrast MRI imaging was fused to the computed tomography (CT) simulation scan. A preoperative gross tumor volume (GTV) was created to ensure that the clinical target volume (CTV) consisting of the residual contrast enhancing disease and postoperative bed appropriately covered the extent of the macroscopic and potential microscopic disease. An anisotropic expansion of 0-8 mm from the CTV was used to create a planning target volume (PTV) accounting for daily setup uncertainty while respecting dose constraints to the optic structures and brainstem   of Patient A presented with a 1-year history of left-sided, progressive hearing loss and pulsatile tinnitus refractory to symptom-directed medical management. On physical exam, she displayed left-sided conducting hearing loss without other abnormalities. via a simultaneous integrated boost was created using a 0.2 cm expansion from the GTV that was then cropped off the left cranial nerves VII and VIII. The brainstem was constrained to a max dose of less than 5400 cGy, and the left cochlea and fifth, seventh, and eighth cranial nerves were to receive a mean dose of less than 5000 cGy. Treatment was delivered via VMAT with 3 six MV arcs  with no other cancer susceptibility loci found mutated  . The premature termination resulting from this variant would remove the BRCA1-binding domain (residues 594-657)  , 13. Her594-657) . A previ594-657) .BAP1 (p.Gln593*) mutation in both tumors. The second inactivating somatic mutation in BAP1 in Patient A’s tumor occurred through an 11-base pair frameshift mutation NM_004656.4:c.1092_1102del (p.His364GlnfsTer30) consistent with a loss of function BAP1 variant ]. A family history of kidney cancer at age of 52 in Patient B’s sister was discovered as well as lung cancer in patient B’s father 1-associated protein which is integral to many cellular pathways including DNA damage signaling and repair, and inactivating mutations of this tumor suppressor gene are oncogenic , in a patient presenting with uveal melanoma leading to biallelic inactivation of BAP1 with associated loss of function in this patient’s tumor as well as in a lung adenocarcinoma and meningioma in 2 additional relatives (BAP1 mutation (c.1948T>A (p.Tyr646*); two of the family members developed meningiomas in addition to their mesotheliomas (BAP1 (p.Asp404*) germline truncating mutation, and one case of papillary meningioma was identified by autopsy  (BAP1 inactivation via an inherited germline BAP1 (p.Gln593*) mutation and biallelic inactivation by different somatic mechanisms. Neither tumor contained mutations in additional genes associated with meningioma (such as NF2 and AKT1)  , 6. Charo 12.89) . Tumors o 12.89) , an analo 12.89) , 6. DespAP1 p.Gln93* mutatfeatures . HoweverBAP1 inactivation TPDS identified after multiple generations of a family presented with meningioma with rhabdoid features instead of presenting with malignancies. Inactivating BAP1 mutations have been associated with inferior outcomes in rhabdoid meningioma and meningioma with rhabdoid features, and routine tumor testing for this mutation may aid prognostication in lower grade tumors with rhabdoid features. Because germline mutations produce a TPDS, genetic testing should be offered to patients found to have BAP1 inactivated tumors. Despite a lack prospective evidence to inform guidelines for screening for malignancy in this population, surveillance for ocular and cutaneous melanoma for patients carrying this germline mutation should be considered based upon elevated rates of disease in retrospective series. Given the paucity of available data to guide management of this rare condition, additional work is needed to determine the optimal diagnostic and management strategy for BAP1 mutated meningioma with rhabdoid features and develop consensus guidelines for screening patients with inactivating germline mutations for malignancy.Besides the relatively limited follow up for Patient B, additional limitations of this case series include that germline genetic testing for asymptomatic family members was not available, and surgical pathology was not accessible to pursue tumor sequencing for family members previously deceased secondary to malignancy. Regardless, we present the first report of a familial The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Except, patient written consent obtained. The patients/participants provided their written informed consent to participate in this study.RP – first author. UG – second author. AY, DM, DK, DT, and EM – contributed equally. JP – senior authorship. All authors contributed to the article and approved the submitted version.JP discloses Honoraria from Huron Consulting group and Novocure, research support from Varian Medical Systems and Kroger outside the submitted work.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Drosophila melanogaster. RNA sequencing data from human PFC and fly heads were analyzed to determine conserved transcriptome signatures of age. Our analysis revealed that expression of 50 conserved genes can accurately determine age in Drosophila (R2 = 0.85) and humans (R2 = 0.46). These transcriptome signatures were also able to classify Drosophila into three age groups with a mean accuracy of 88% and classify human samples with a mean accuracy of 69%. Overall, this work identifies 50 highly conserved aging-associated genetic changes in the brain that can be further studied in model organisms and demonstrates a novel approach to uncovering genetic changes conserved across species from multi-study public databases.Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging problem. Aging affects the prefrontal cortex (PFC), which governs executive function, learning, and memory. Previous sequencing studies have demonstrated that aging alters gene expression in the PFC, however the extent to which these changes are conserved across species and are meaningful in neurodegeneration is unknown. Identifying conserved, age-related genetic and morphological changes in the brain allows application of the wealth of tools available to study underlying mechanisms in model organisms such as  Employing a comparative approach to study conserved aging phenotypes across multiple species provides a deeper insight into molecular aging signatures than studying aging within a single organism. Since aging is the strongest risk factor for developing neurodegenerative diseases, identification of conserved molecular signatures in the brain will allow for more effective study of mechanisms that may underlie neurodegeneration as well as facilitate the development of novel therapeutic strategies to slow cognitive aging .Drosophila, study of neurodegenerative diseases frequently utilizes both the brain, containing the mushroom body which is necessary for olfactory learning and memory [Drosophila [Age-related cognitive decline varies across individuals. Neuroimaging studies indicate that cognitive decline is related to changes in the structure of the prefrontal cortex (PFC)  and alted memory , and theosophila . IdentifDrosophila to increase statistical power through combining samples across multiple laboratories. This facilitates the identification of conserved aging genes in the human PFC/fly head in a unique approach that circumvents the prohibitive cost of collecting hundreds of cross-species samples in a single laboratory. In this work, we examine transcriptome changes across species to identify conserved genes that are highly correlated with chronological age in both human and fly. Furthermore, we were able to predict genetic network interactions of these genes as well as pathways affected by the subset of conserved genes. From a comparative biology perspective, such conserved genes should play an important role in age-related physiological changes in the PFC and comprise a promising target set for future mechanistic analysis in Drosophila, a model organism with a short lifespan and well-established genetic tools. We identify the target gene set by cross-referencing genes highly correlated with age in humans and Drosophila. Machine learning algorithms validate the association of these genes with age. Similar techniques could be used to identify target orthologs in other model organisms and tissues in future analyses.Here, we demonstrate the utility of previously published, publicly available RNA sequencing datasets from humans and Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool  [Drosophila. Publicly available data downloaded from the NIH SRA included multiple Bioprojects outlined in Table 1. Files were downloaded from the SRA using Aspera Connect and reads that were downloaded in SRA format were converted using fastq-dump from the NIH SRA-Toolkit [Drosophila, Middle-aged was between 30–60 years in humans and between 10–29 days in Drosophila, Old was classified as above 60 years in humans and above 30 days in Drosophila. Additional sequencing parameters such as read lengths or study metadata can be found for each study contained within this analysis in the NIH Sequence Read Archive (SRA) according to the Bioproject identifiers within Table 1.Available sequencing data was identified using the terms “aging”, “RNA-seq”, “brain transcriptome”, “cortex”, and “heads” in articles on PubMed and in the NIH Sequence Read Archive (SRA) database. All eligible studies were published online before August 2019. The following inclusion criteria were used to select data for the analysis: (1) tissue ; (3) availability of age data; (2) lack of disease  or treatment; and (4) Illumina format raw next-generation sequencing files. The following exclusion criteria were also used to select data for this study: (1) lack of neurologically normal samples; (3) incomplete age information or incubation temperature data for fly samples; (2) presence of a treatment such as injections, traumatic brain injury or sham-surgery procedures; and (4) studies including only microarray data. When overlapping data of the same study population was encountered in more than one publication across multiple SRA submissions, only the most complete study was used for our analysis leaving a single occurrence for each biological sample. To compare orthologous genes across species, the g/diopt) , was useAll reads were analyzed with FastQC for quality control. Reads with low quality scores  were discarded. Adapter sequences were trimmed using BBDUK . Reads thttps://github.com/akbee/Aging-Brain-Transcriptome.Reference fasta genome files and genome annotation gtf files were downloaded from the Ensemble genome browser and Flybase.org browser. Human reads were aligned to the GRCh38 release 94 of the Ensemble Human Genome, and fly reads were aligned to the DMEL release 25 Flybase genome using the STAR v2.5.2 aligner . TranscrBased on previously published work , 29, 13 R2, and median absolute error scores in Scikit-learn. All analyses were conducted in batches of 1000 random sampling tests with replacement to estimate the mean for each model performance metric and a 95% confidence interval for R2. In-depth descriptions of these algorithms can be found in Hastie, et al. [To identify correlation with biological age, all data were split into a training set (75%) and a testing set (25%) by randomly selecting samples using the train_test_split module in Scikit-learn. Age association models were fit using Scikit-learn v0.23.2  in Jupyt, et al. .S1 Fig).We examined Trimmed Mean of M values (TMM) , and theS1 Table. The top 1000 human aging correlated genes were converted into fly homologs using Diopt version 8.0. Human fly homologs were prioritized according to highest to lowest score within both human ensembl ID and flybase ID and the highest ranked homolog for each gene was kept. This left a one to one ‘best match’ between human ensembl ID and flybase ID. This list was cross-referenced to the top 1000 fly aging correlated genes to find matches between the two lists.All genes within species were ranked according to correlation coefficient according to within-species age shown in Expression data of human prefrontal cortex at young, middle, and old was input to the Next-Generation Clustered Heat map (NG-CHM) version 2.16.0 .Cytoscape version 3.8.0 was used to generate a genetic interactome of 50 human ensembl IDs with a medium confidence score . STRING Drosophila genes. URL: www.cyverse.org [Cyverse DNA Subway was utilized to map all 50 conserved human genes to 49 erse.org . Proteinhttp://www.pantherdb.org). Gene Ontology (GO) Analysis was used to group genes according to cellular location, function and biological processes.Panther  Classification System (version 15) was used to categorize molecular and cellular classification of genes of interest   . All AscAll studies contained within this manuscript received ethics approval from their respective IRB ethics committees prior to study initiation. See citations in table one for additional information on each published study. Iowa State University Institutional Review Board does not require approval for the secondary data analysis of anonymous publicly available data used in this work.Drosophila head, we explored methods and constraints for combining data from the National Institutes of Health Sequence Read Archive. We identified key inclusion and exclusion requirements for the combination of data from this publicly available repository. These factors are described in detail in the methods section. The studies meeting these criteria and used for further analysis are described in Table 1. In total, 289 raw fastq files were downloaded from NCBI and processed. Ninety-four percent of the Drosophila reads and ninety-six percent of the human reads were mapped to their respective reference sequences. In our analysis of the combined data sets, 9,650 genes in Drosophila and 16,879 genes in humans had read values of greater than 10 in at least 50% of the samples.To obtain a higher analytical power than typically feasible from a single experiment in human prefrontal cortex and For regression analysis, we compared 13 distinct algorithms in the Python Scikit-learn library for predicting human chronological age as shown in R2 of 0.62 and a median absolute error of 8.28 years on held out data. The standard gradient boosting regressor through Scikit-learn performed similarly with a mean R2 of 0.61 and a median absolute error of 8.72 years . Within Drosophila, XGBoost performed among the best algorithms, with a mean R2 of 0.89. Linear regression, which has been shown previously to predict age from the transcriptome in peripheral blood [. Additional details of human and Drosophila regression results using XGBoost can be found in S2 Table. Given its superior predictive power on this specific data set, XGBoost was selected for downstream analysis.XGBoost performed best with a mean al blood , performDrosophila resulted in several algorithms with similar predictive abilities. Four algorithms had a mean R2 value of 0.89 or greater. Of these, XGBoost had the lowest median absolute error. Due to its strong predictive ability in both human and Drosophila in comparison to our other algorithms, XGBoost was selected for downstream analysis.Comparison of algorithms within S3 Table). Overall, classification of both human and Drosophila samples was possible with a high degree of accuracy .Algorithms were also compared for classifying samples. This approach is valuable in data sets where exact age is unknown. In a previous report examining machine learning to classify human age groups, an ensemble classifier using linear discriminant analysis (LDA) most accurately classified human chronological age . In our Fig 1. First, we established that gene expression data in the human PFC and fly head were predictive of chronological age within species. Fig 2A shows a sample regression result for human data. These results were obtained using 75% of the samples to train and the remaining 25% for testing. To eliminate the chance of a “lucky draw,” we repeated the 75/25 split 1000 times to assess average performance. Fig 2B depicts the resulting histogram of R2 values. The mean R2 was 0.61, and the median absolute error was 8.07 years. Fig 2C and 2D shows results for fly data, where a mean R2 of 0.93 and a median absolute error of 0.46 days were obtained. The average accuracy of classifying samples as young, middle-aged, or old was 80% for humans and 93% for flies (S3 Table). We repeated this procedure using the 1000 genes most correlated with aging in humans and the 1000 genes most correlated with aging in flies. Using this technique to reduce the number of features in our models resulted in a slight boost in R2 on the testing data for both species, illustrating the potential for error due to overfitting in machine learning models .A workflow depicting our approach for conserved gene identification can be found in Drosophila through Diopt, we identified genes shared among both lists. This narrowed our window of conserved genes to 50 which were highly predictive of aging in both humans and flies. Fig 4 illustrates the ability to identify human and Drosophila age based on expression levels of these 50 genes. Fig 4A and 4B depicts the human results, where these 50 genes identified chronological age with a mean R2 of 0.46 and median absolute error of 10.79 years. Fig 4C and 4D depicts the results for Drosophila, where a mean R2 = 0.85 and median absolute error of 0.99 days was obtained. In both cases, we pay a small penalty in regression accuracy for reducing the number of genes from 1000 to 50, however both predictions remain strong (see S2 Table). Similar results hold for classification accuracy (see S3 Table). For example, reducing the number of genes used in the prediction by 95% results in only a 15% drop in human age classification accuracy and a 10% drop in fly classification accuracy.After determining that aging correlated genes retain predictive ability within species, we wanted to identify a smaller set of genes that could be validated in future animal studies examining their role in aging. Cross referencing the 1000 genes most important in aging within both human and Fig 5A). Higher expression is indicated in red with lower expression indicated in blue.A heatmap of mean human expression data from the prefrontal cortex vs. age in the 50 highly conserved genes indicates an overall increase in expression from young to old age groups in the 50 highly correlated conserved genes  and MAPK (6 genes). The enrichment of these signaling pathways was found to be significant and survived FDR multiple testing correction (p ≤ 0.005). We observed a high amount of overlap with the genes represented in these two pathways and other significantly enriched pathways such as cancer and focal adhesion as is indicated by the highly connected group of genes which are found in each of these pathways .Genetic interactions and enrichment of genes in pathways previously known to play an important role in brain aging are revealed using STRING analysis of the 50 highly conserved correlated genes. Fig 5B).As age is one of the greatest predictors of neurodegeneration, our list of 50 genes identified from PFC and fly head were referenced to a STRING disease database. 1000 Genes with a reported association with either Parkinson’s or Alzheimer’s disease were cross-referenced to the list of 50 conserved genes. Within the 1000 genes associated with Alzheimer’s disease and Parkinson’s disease, a total of 7 genes were also identified in our conserved list. The genes associated with Alzheimer’s disease were Erb-B2 receptor tyrosine kinase 2 (ERBB2), Cystatin B (CSTB), and Caspase 6 (CASP6). Those associated with Parkinson’s disease were DNA damage inducible transcript 4(DDIT4), Bone morphogenic protein 4 (BMP4), Coiled-coil-helix-coiled-coil-helix domain containing 10 (CHCHD10), Dilochyl-phosphate mannosphyltransferase 3 (DPM3) and caspase 6 (CASP6)  with 10 genes represented. Reducing our input to the 50 conserved human genes, 4 remained in the Gonadotropin pathway . Although the Gonadotrophin pathway is emerging as a possible therapeutic target in aging and neurodegeneration in Humans [Drosophila.We utilized the Panther Gene Ontology database to reveal evolutionarily relevant functions of our aging correlated genes. Panther Gene Ontology utilizes phylogenetic information to infer evolutionary function of genes that have yet to be well characterized. Genes highly correlated with aging were mapped to Panther pathways as shown in n Humans , The gonDrosophila, we constructed a phylogenetic tree using our 50 conserved genes of Human and Drosophila. Protein sequences of our conserved genes were input into Cyverse–DNA Subway to generate a full phylogenetic tree. S3C Fig depicts a sub-cluster of genes from our phylogenetic tree. Interestingly, 2 of our genes from the Gonadotropin-releasing hormone pathway appeared to share sequence similarity, including ENSG00000123999 (INHA) and ENSG00000125378 (BMP4), along with its homolog FBgn0000490 (dpp).To elucidate roles of the conserved Gonadotropin-hormone genes in Drosophila and humans to reveal aging-associated genes with greater statistical power than can typically be obtained from a single laboratory. Our transcriptome profiling across species uncovered 50 genes whose expression is strongly correlated with aging in both Drosophila and humans. Twenty-two of these genes had not been previously associated with aging .In this work, we developed a method for combining datasets across multiple studies and organisms giving us the ability to harness the power of a larger dataset than is typically feasible to obtain in a single lab. We then examined several machine learning algorithms to identify the best model for predicting age via highly conserved transcriptome signatures. Using this method, we analyzed publicly available RNA sequencing data from aging C. elegans with humans, they demonstrate that neural excitation increases as a function of age and inhibiting the excitation of neurons increases longevity. Other key mechanistic relationships such as NADH dehydrogenase expression and longevity [Previous work supports a cross-species approach to the discovery of gene function in aging. Zullo et al.  demonstrongevity  as well ongevity  have beeDrosophila age in days to human age in years [Drosophila is a common model of aging as denoted by De Nobrega and colleagues [Important consideration must be taken when examining equivalence of invertebrate age to human age. There are several published reports comparing both mouse  and rat in years , Drosophlleagues . For ourlleagues .Our analysis identified an enrichment of two established key aging-related pathways. The PI3K-Akt signaling pathway was heavily represented in our list of 50 genes with 10 being known players in this signaling pathway. This pathway has previously been shown to indirectly promote mTOR complex 1 and mTOR complex 2 kinases , 53 as wSix of our 50 genes were found to be involved in the MAPK signaling pathway. The MAPK signaling cascade has known involvement in neurodegenerative diseases. Activation of MAPK has been shown to increase oxidative stress, which is a key risk factor in both Alzheimer’s and Parkinson’s diseases . Our datGiven the strong correlation between aging and neurodegeneration, we were interested in identifying genes in our list of 50 highly conserved aging genes found in the human PFC that were known to be associated with neurodegenerative diseases. We referenced our list of 50 highly predictive aging genes with 1000 genes associated with Alzheimer’s and Parkinson’s diseases according to the STRING disease database. Given these genes reported associated in neuronal disease alongside conserved expression changes within aging we would propose further mechanistic studies of these genes as risk factors for neurodegenerative diseases associated with aging. These include Caspase-6 (CASP6) whose proteolytically processed form has been shown to increase in correlation with amyloid beta pathologies in Alzheimer’s disease patients . We alsoDrosophila head due to the morphological changes in the PFC that often accompany neurological decline. However, comparison of tissue between human PFC and the Drosophila head should be exercised with caution. Numerous tissues outside of the brain exist within the Drosophila head [In the current study, we focused on the human prefrontal cortex (PFC) and the ila head .Drosophila. Additionally, we identified 22 genes that do not have a previously characterized role in aging. This provides exciting insight into potential targets to follow-up utilizing the Drosophila model. The gonadotropin-hormone pathway is not well studied in Drosophila, yet our data suggests that a small number of conserved age related genes appear to be involved in this pathway in humans. Further investigation into these genes could provide more understanding for how the gonadotropin pathway and other pathways which have not been well studied in aging can be elucidated in the Drosophila model.Our data analysis validated 28 genes previously associated with aging that are conserved between human and Table 2). This may be due to the use of heterogeneous tissue samples from the human PFC containing several cell types  in the current study compared to tissue culture of a single cell type. Similarly, linear regression has been used to predict age from gene expression in peripheral blood samples [Drosophila RNA sequencing data among 13 popular machine learning algorithms, as shown in Table 2. We selected the XGBoost algorithm because it performed well in age prediction with human and fly data; however, several other machine learning algorithms could generate models with very high accuracy and low error, especially in fly.Other research groups have applied machine learning techniques to predict age from gene expression and biomarker data . In huma samples  but perf samples , where sS2 Fig indicates that misclassification rates were similar in the three age groups.Due to the nature of data collection in animal experiments, samples are normally collected at a few specific ages, such as 10 days or 25 days. The variability in gene expression data from a single time point is likely much less than the variability across a range of ages . As a result, models trained on data from a few time points might generalize poorly to new samples from time points never seen by the algorithm. This may explain some of the performance gap between aging via fluorometrically measured pteridine 6-biopterin  and our Reproducibility and transparency are important current issues in the field of machine learning , where mSeveral problems are inherent to combining data from publicly available repositories. Performing meta-analysis across multiple RNA sequencing datasets is limited by the lack of normalization standards  and limiA key strength of this study was that we achieved a large sample size by combining RNA seq samples across studies to improve the overall statistical power. Additionally, we were able to overcome challenges of using gene expression data with different underlying read count distributions due to differences in sample depth across studies through normalizing read counts using the TMM method. Differences among individual datasets still exist, illustrating the necessity for new statistical approaches to combine RNA sequencing data more effectively across multiple studies generated by different sequencers, RNA extraction protocols, library preparation kits, and experimental design of sequencing.S5 Table). Further study of the novel 22 aging-associated genes may shed light on previously unassociated pathways that are altered during aging processes in the PFC. These results position scientists to delve deeper into the underlying conserved mechanisms of these identified aging genes in the PFC. Manipulating the expression of these conserved aging genes could potentially extend lifespan or enable development of novel anti-aging therapeutic compounds. Our dataset combining publicly available transcriptome data across multiple studies and two species demonstrated successful identification of chronological age using neurological transcriptome signatures. Using machine learning to model aging, we identified novel similarities in aging signatures across humans and Drosophila emphasizing the necessity for additional comparative aging research studies.In this study, we uncovered 50 genes conserved between humans and flies capable of accurately predicting age in both species. Roughly half of these genes have been previously associated with aging. We demonstrate that of our 50 genes, 22 novel genes have not yet been published with respect to aging (see S1 TableThis table depicts the aging correlated genes for humans and flies sorted according to their correlation coefficient.(XLSX)Click here for additional data file.S2 Table2, mean square error, median absolute error and R2 95% confidence interval across 1000 iterations of training/testing predictions. Each row represents a different way to select genetic features for age prediction, where each column represents the metric used for evaluating the effectiveness in predicting aging.This table depicts the average R(XLSX)Click here for additional data file.S3 TableThis table depicts the average accuracy, F1, Precision, and Recall scores for classifying samples into their age groups. The data contained in these tables represents average scores across 1000 iterations of training/testing predictions. Each row represents a different way to select genetic features for age prediction, where each column represents the metric used for evaluating the effectiveness in age group classification.(XLSX)Click here for additional data file.S4 TableThis table describes whether previous reports exist linking these genes to aging or neurodegeneration phenotypes in Human or another model organism.(XLSX)Click here for additional data file.S5 TableThis table describes previous literature of listed genes, along with references.(XLSX)Click here for additional data file.S1 Fig2 of read counts by study indicate improved distribution overlap following normalization. A) Results without normalization applied. B) Results after applying Relative Log Expression (RLE) normalization. C) Results after applying Trimmed Mean of M values (TMM) normalization. Each color corresponds to a different study.Histograms of log(TIF)Click here for additional data file.S2 FigConfusion matrices of average biological age prediction. A) Average age group classification results for human samples using all available data. B) Average age group classification results for Drosophila samples using all available data. C) Average age group classification results for 1000 genes in human most correlated with aging. D) Average age group classification results for 1000 genes more correlated with aging in Drosophila. E) Average age group classification results for 50 conserved and correlated genes applied to predict age in humans. F) Average age group classification results for 50 conserved and correlated genes applied to predict age in Drosophila. All confusion matrixes depict the average of 1000 trials of age prediction.(TIF)Click here for additional data file.S3 FigPanther pathway analysis of genes implicated in aging. A) Predicted pathways using 1000 genes most associated with aging in human data. Threshold set at 5 genes involved in a pathway. B) Pathway analysis of 50 human genes conserved in fly. C) Phylogenetic tree branch depicting gene sequence homology. Genes included are found in the Gonadotropin-releasing hormone pathway (P06664). Numbers throughout the branches indicate bootstrapped scores out of 100 trails testing for sequence similarity. Higher numbers indicate stronger prediction of phylogenetic relationship.(TIF)Click here for additional data file.