Although inflammation in the central nervous system is responsible for multiple neurological diseases, the lack of appropriate biomarkers makes it difficult to evaluate inflammatory activities in these diseases. Therefore, a new biomarker reflecting neuroinflammation is required for accurate diagnosis, appropriate therapy, and comprehension of pathogenesis of these neurological disorders. We previously reported that the cerebrospinal fluid (CSF) concentration of lateral olfactory tract usher substance (LOTUS), which promotes axonal growth as a Nogo receptor 1 antagonist, negatively correlates with disease activity in multiple sclerosis, suggesting that variation in LOTUS reflects the inflammatory activities and is a useful biomarker to evaluate the disease activity. To extend this observation, we analyzed the variation of LOTUS in the CSF of patients with bacterial and viral meningitis, which are the most common neuroinflammatory diseases.n = 40), who were followed up by CSF study at least twice, and from healthy controls (n = 27). Patients were divided into bacterial (n = 14) and viral meningitis (n = 18) after exclusion of eight patients according to the criteria of this study. LOTUS concentrations, total protein levels, and CSF cell counts in the acute and recovery phases were analyzed chronologically. We also used lipopolysaccharide-injected mice as a model of neuroinflammation to evaluate LOTUS mRNA and protein expression in the brain.CSF samples were retrospectively obtained from patients with meningitis (Regardless of whether meningitis was viral or bacterial, LOTUS concentrations in the CSF of patients in acute phase were lower than those of healthy controls. As the patients recovered from meningitis, LOTUS levels in the CSF returned to the normal range. Lipopolysaccharide-injected mice also exhibited reduced LOTUS mRNA and protein expression in the brain.CSF levels of LOTUS correlated inversely with disease activity in both bacterial and viral meningitis, as well as in multiple sclerosis, because neuroinflammation downregulated LOTUS expression. Our data strongly suggest that variation of CSF LOTUS is associated with neuroinflammation and is useful as a biomarker for a broader range of neuroinflammatory diseases. These new markers now play an important role in evaluation of the activities of neuroinflammatory diseases, including MS [Inflammation in the central nervous system (CNS) is associated with a wide range of neurological diseases. In particular, infection and autoimmunity cause acute or chronic inflammation in the CNS . Howeveruding MS , 5. HoweRecently, we showed that lateral olfactory tract usher substance  is critical for axonal growth. LOTUS acts as a Nogo receptor 1 (NgR1) antagonist, preventing Nogo from binding to NgR1 –8. FurthIn this study, to investigate the relationship between LOTUS and neuroinflammation, we analyzed the variation of LOTUS concentrations in CSF of patients with bacterial and viral meningitis. These patients’ clinical courses were objectively verified using established CSF markers such as total protein concentration, cell counts, and glucose, in addition to clinical findings, and we also performed comparisons between the acute and recovery phases in the same patients. Furthermore, we investigated the mechanisms underlying the observations in human patients using lipopolysaccharide-injected mice as a disease model of neuroinflammation.n = 20), bacterial (n = 15), or tuberculous (n = 5) meningitis were classified as follows [n = 20). Patients in the aseptic group with infectious sign (n = 18) were defined as viral meningitis, and two patients were ruled out due to lack of infectious sign. Patients with reduced CSF glucose level (< 40% of the plasma glucose level), neutrophil-predominant CSF pleocytosis, or positive microbiological culture were classified into the bacterial group (n = 14), and one patient was excluded due to complications of hydrocephalus. Patients with Mycobacterium tuberculosis gene-positive CSF, as confirmed by polymerase chain reaction, were classified into the tuberculous meningitis group (n = 5). In these patients, prolonged recovery period [All patients enrolled in this study had underwent neurological evaluation at our university hospital and been diagnosed as meningitis. Between January 2008 and September 2017, CSF samples were obtained retrospectively from 40 patients who had been followed up for CSF study at least twice, after initial sampling in the acute phase, and 27 healthy controls. Patients with aseptic . A neuroinflammation response was induced by intraperitoneal injection of 40 mg/kg lipopolysaccharide ; phosphate-buffered saline (PBS) was administered as a negative control. Mice were analyzed 24 or 48 h after treatment.Lotus) and hypoxanthine phosphoribosyltransferase 1 (Hprt1) were evaluated by quantitative PCR (qPCR) using Power SYBR Green PCR Master Mix  on a LightCycler® 96 SW 1.1. . Relative expression levels were determined using the ΔΔCT method; the levels of the mRNAs of interest were normalized against the geometric mean level of Hprt1 mRNA The following specific primer sets were used:Total RNA was extracted from the brains of LPS- or PBS-injected mice using the miRNeasy Mini Kit  and reverse-transcribed using the SuperScript III First-Strand Synthesis SuperMix for RT-PCR . Expression levels of the genes encoding LOTUS . The detailed methodology of this assay is described elsewhere . Experimt test as appropriate, using GraphPad Prism version 6.0 . P < 0.05 was considered statistically significant.Statistical analysis was performed with Wilcoxon matched-pairs signed-rank test or Student’s p = 0.31 and 0.53, respectively).The characteristics of the patients enrolled in this study are summarized in Table First, we examined conventional CSF biomarkers of meningitis, such as total protein and cell counts, in the acute and recovery phases in the same patients. Objective improvements in these markers and clinical recovery were confirmed in all patients with meningitis Figs.  and 2.FiWe next assessed the CSF LOTUS concentration in the acute and recovery phases by immunoblot analysis Fig. a. The stLotus mRNA expression in the brain, using LPS-injected mice as a model. Lotus mRNA expression was downregulated in LPS-inflamed brains, followed by a decrease in LOTUS protein levels , and oligodendrocyte myelin glycoprotein (OMgp) are representative myelin components . These mCurrently, total protein concentration and cell counts in the CSF are widely used as biomarkers to evaluate disease activity, although they are not necessarily appropriate for monitoring neuroinflammation in most CNS diseases. For evaluation of meningitis, these conventional markers are well established; therefore, we first confirmed improvement of meningitis in reference to these CSF biomarkers, in addition to the clinical neurological findings. Then, we chronologically compared the CSF LOTUS concentration between acute and recovery phases. We observed that the LOTUS level was reduced in acute phase, followed by recovery to the healthy control level, accompanied by clinical improvement. These data demonstrated that the CSF LOTUS concentration is inversely correlated with the disease activity of meningitis, as we previously reported in MS.Lotus mRNA expression by neuroinflammation, and (2) enhanced degradation of LOTUS protein. Accordingly, we investigated whether Lotus mRNA levels decreased with the severity of neuroinflammation in LPS-injected mice, which is widely used as an animal model for neuroinflammation [Lotus mRNA, along with a reduced level of LOTUS protein, in the brain. These results indicated that the mechanism underlying reduced CSF LOTUS concentration is downregulation of Lotus mRNA expression in response to neuroinflammation.We then considered two possible mechanisms for a decrease in LOTUS: (1) downregulation of ammation –25. As ePrevious proteomic studies reported LOTUS in CSF from normal subjects , and anoInterleukin-6 (IL-6) concentration in the CSF is often used as a biomarker of neuroinflammation in bacterial meningitis and progressive neuro-Behçet’s disease –31. HoweIn contrast to IL-6, LOTUS may reflect disease activity of wider range of CNS diseases such as viral and bacterial meningitis, CNS sarcoidosis, and MS. Thus, the CSF LOTUS level is potentially of great diagnostic value for estimating neuroinflammation in a variety of CNS diseases, and we hypothesize that LOTUS might be critical to the pathogenesis of these diseases.CSF levels of LOTUS correlated inversely with disease activity both in bacterial and viral meningitis, as in multiple sclerosis. Therefore, variation of CSF LOTUS is associated with neuroinflammation and is useful as a biomarker, leading accurate diagnosis, appropriate therapy, and comprehension of pathogenesis for a broader range of neuroinflammatory diseases.


Purpose: Surfactant proteins (SPs) are involved in the regulation of rheological properties of body fluids. Concentrations of SPs are altered in the cerebrospinal fluid (CSF) of hydrocephalus patients. The common hallmark of hydrocephalus is enlargement of the brain ventricles. The relationship of both phenomena has not yet been investigated. The aim of this study was to evaluate the association between SP concentrations in the CSF and enlargement of the brain ventricles.Procedures: Ninty-six individuals  were included in this retrospective analysis. CSF specimens were analyzed for SP-A, SP-B, SP-C and SP-D concentrations by use of enzyme linked immunosorbent assays (ELISA). Ventricular enlargement was quantified in T2 weighted (T2w) magnetic resonance imaging (MRI) sections using an uni-dimensional (Evans’ Index) and a two-dimensional approach .Results: CSF-SP concentrations (mean ± standard deviation in ng/ml) were as follows: SP-A 0.71 ± 0.58, SP-B 0.18 ± 0.43, SP-C 0.89 ± 0.77 and SP-D 7.4 ± 5.4. Calculated values of Evans’ Index were 0.37 ± 0.11, a calculation of LVAI resulted in 0.18 ± 0.15 (each mean ± standard deviation). Significant correlations were identified for Evans’ Index with SP-A  and SP-C , LVAI with SP-A , SP-C  and SP-D . Furthermore, SP-C showed a clear inverse correlation with age .Conclusion: The present study confirmed significant correlations between SPs A, C and D in the CSF with enlargement of the inner CSF spaces. In conclusion, SPs clearly play an important role for CSF rheology. CSF rheology is profoundly altered in hydrocephalic diseases, however, diagnosis and therapy of hydrocephalic conditions are still almost exclusively based on ventricular enlargement. Until now it was unclear, whether the stage of the disease, as represented by the extent of ventricular dilatation, is somehow related to the changes of SP levels in the CSF. Our study is the first to provide evidence that increasing ventriculomegaly is accompanied by enhanced changes of rheologically active compounds in the CSF and therefore introduces completely new aspects for hydrocephalus testing and conservative therapeutic approaches. Surfactant proteins (SPs) are a heterogeneous group of proteins being present at various sites in the body  being abundant at the sites of the blood-brain barrier (BBB) and blood-CSF barrier -derived measurements  approved study with retrospective design.Patients were included in the hydrocephalus group if the diagnostic criteria for aqueductal stenosis (AQS), acute hydrocephalus (AH) or normal pressure hydrocephalus (NPH) were met. Patients were included in the control group if evidence of neurological disorders  was absent. To be included in either group, MRI and CSF samples had to be available in addition to the clinical data.If signs of infection or other inflammatory or autoimmune diseases were present, the patient was not included in the study. Also, evidence for blood brain barrier breakdown with leakage of serum proteins into the CSF was an exclusion criterium for both groups.A total of 96 individuals was examined. All patients or caregivers gave their informed consent for the scientific use of CSF-samples, clinical and radiological data. Informed consent was given in writing. CSF specimens of 41 subjects without evidence of neurological pathologies were used as a control group. Those specimens were obtained during a diagnostic workup that necessitated CSF examination after lumbar puncture . Furthermore, CSF samples were obtained from 55 patients finally diagnosed with hydrocephalus in the context of their diagnostic workup.Hydrocephalus patients were categorized into the following subgroups after the review of patient records and brain imaging: AQS, AH (excluding AQS) and NPH. Twenty-one patients with narrowed or obstructed mesencephalic aqueduct and corresponding enlargement of the lateral and III. ventricles in cranial imaging were classified as AQS  according to the manufacturer’s manual. Commercially available ELISA kits  were used for quantification of SP-A , SP-B , SP-C  and SP-D  in CSF samples. The analysis was performed using a microplate spectrophotometer (ELISA-reader) at a wavelength of 450 nm and a reference wavelength of 405 nm for absorbance measuring. Surfactant protein concentrations in ng/ml CSF were calculated by comparison between standard series and the determined values of antigen concentration (protein concentration) according to the manufacturer’s manual.In all patients cMRI was performed by using either a 1.5 T device  or 3 T device . The imaging protocols differed between the investigated individuals depending on the initially suspected diagnosis. All imaging protocols included an axial and sagittal T2 weighted (T2w) turbo spin echo sequence (TSE) as minimal imaging requirements, being necessary for evaluation of Evans’ Index/LVAI and identification of AQS or AH.Image analysis was performed by a neuroradiologist (UQ) with 15 years of radiological experience.The Evans’ Index was determined using OsiriX lite software as follows: maximal width of the ventricular frontal horns divided by the maximal transverse width of the cranium Evans, .The LVAI was estimated as follows: the axial section showing the maximal area of the lateral ventricles  was identified. The area of both frontal and occipital horns—the maximal lateral ventricular area—was quantified within this section using the area measurement function in OsiriX lite. The total intracranial  area in the same axial section was calculated, respectively. Finally, the index was calculated as follows: maximal lateral ventricular area divided by the corresponding intradural area. Figures T-test as a post hoc algorithm. For Dunnett’s test the control group served as reference group for the hydrocephalic subsets. p-values < 0.05 were taken to indicate statistical significance in all instances. Spearman’s correlation coefficient was used to analyze the association between Evans’ Index, LVAI, age and CSF-SP concentrations.For statistical analysis the SPSS statistical software package was used . Collected data was evaluated by means of descriptive statistics . Comparison of CSF-SP concentrations, Evans’ Index and LVAI between hydrocephalic patients and controls as well as between the hydrocephalic subsets  and NPH) and controls were performed by means of ANOVA and—for the latter—Dunnett’s The acquired data was analyzed in three subsequent steps: (i) CSF-SP concentrations and indices of ventricular dilatation (Evans’ Index and LVAI) were determined for the whole collective in order to elucidate whether there is a general association of SP concentration and ventricular enlargement. (ii) Calculations were performed once more after separation into two subgroups—all hydrocephalic patients and healthy controls—to corroborate the gathered findings by comparing changes in SP concentrations between individuals with and without reliably proven ventricular enlargement. (iii) The hydrocephalic subgroup was further devided into etiological subsets—acute hydrocephalic conditions (AQS plus AH) vs. NPH—in order to challenge the previously reported exceptions of the rather chronic hydrocephalic disease NPH regarding the differences of SP concentration in the CSF.The calculated CSF-SP concentrations (mean ± standard deviation) over all investigated subjects were as follows: SP-A = 0.71 ± 0.58 ng/ml, SP-B = 0.18 ± 0.43 ng/ml, SP-C = 0.89 ± 0.77 ng/ml and SP-D = 7.4 ± 5.4 ng/ml Table . Analysir = 0.388, p < 0.001, SP-A + LVAI: r = 0.352; SP-C + Evans’ Index: r = 0.392, p < 0.001 SP-C + LVAI: r = 0.471, p < 0.001). However, correlation of SP-C was stronger with LVAI compared to Evans’ Index, as shown by the higher correlation coefficient (r). Interestingly, SP-D showed a statistically significant correlation with LVAI  was performed. As shown in Table p < 0.001) and SP-C (p < 0.001) between hydrocephalic patients and control subjects. The findings are graphically summarized in Figures p = 0.087).CSF-SP concentrations as well as the two dilatation indices are being compared. ANOVA revealed statistically significant differences regarding CSF-SP concentrations of SP-A (p < 0.001), Figures Evans’ Index and LVAI showed statistically significant differences between hydrocephalic patients and control subjects by means of ANOVA . Figures ANOVA, comparing the acute hydrocephalic conditions subset, the NPH subset and the control group as three equal groups regarding CSF-SP concentrations and measures of ventricular enlargement revealed the following results: SP-A, SP-C, SP-D, Evans’ Index and LVAI, but not SP-B were significantly different between the groups (SP-A: p < 0.001), SP-C (p < 0.001), SP-D (p = 0.040), Evans’ Index (p < 0.001) and LVAI (p < 0.001) were different in the acute hydrocephalic conditions subset compared to the control group. Regarding the NPH subset, Dunnett’s test showed the following results: significant differences in comparison to the control group were identified for Evans’ Index (p < 0.001) and LVAI (p < 0.001). SP-A showed a clear trend, but the difference in SP-A levels between NPH subset and control group did not reach statistical significance (p = 0.052).Dunnett’s test gave the following results: values of SP-A  was performed including only both hydrocephalic subsets.p = 0.046, SP-C: p = 0.008). Figures As expected, measures of ventricular dilatation did not reveal statistically significant differences between the acute hydrocephalic conditions subset and the NPH subset. In contrary, CSF-SP concentrations were significantly different in the NPH subset compared to the acute hydrocephalic conditions subset . Spearman correlation analysis yielded the following results: in both subsets there was a highly significant correlation between SP-A and SP-C concentrations with both dilatation indices. The acute subset even showed a significant correlation of SP-D with both Evans’ Index and LVAI, which was not detectable in the NPH subset.r = 0.458, p < 0.001) and LVAI , SP-C with Evans’ Index  and LVAI  and SP-D with Evans’ Index  and LVAI .In the acute hydrocephalic conditions subset values were as follows: SP-A with Evans’ Index  and LVAI  and SP-C with Evans’ Index  and LVAI .In the NPH subset values were as follows: SP-A with Evans’ Index  in the overall population, the association between both parameters is graphically demonstrated in Figure r = −0.357, p = 0.011. Analyzing the data exclusively from the control group the correlation was as follows: r = −0.327, p = 0.039.Finally, the relation of both dilatation indices was examined more closely. Evans’ Index correlated well with LVAI  frontal occipital horn ratio (O’Hayon et al., Evans’ Index and LVAI revealed an excellent correlation in our study, indicating that both reflect ventricular enlargement. This is further underlined by the results of ANOVA, showing that Evans’ Index and LVAI were significantly different in hydrocephalus patients compared to the healthy control group.In the overall collective, SP-A and SP-C revealed clear correlations with Evans’ Index, which corresponds well to the findings of our previously published study (Schob et al., As mentioned above, one of the major findings of our previous study (Schob et al., Surprisingly, SP-C revealed a clear inverse correlation with age in our present study. This association was identifiable when calculating the Spearman correlation coefficient including hydrocephalus patients only, including the control group only and including all investigated individuals. This might indicate that SP-C generally decreases in the CSF with growing age.The present study confirmed a clear correlation between SP-A and SP-C in the CSF with enlargement of the inner CSF spaces. Furthermore, a moderate correlation between SP-D in CSF with ventricular enlargement was identified by using a more accurate, area measurement based approach compared to the simple, linear approach. This possibly indicates that SP-D is also differently regulated in hydrocephalic conditions, but in a more subtle manner than SP-A and SP-C. In conclusion, SPs clearly play an important role for CSF rheology. CSF rheology is profoundly altered in hydrocephalic diseases, however, diagnosis and therapy of hydrocephalic conditions are still almost exclusively based on ventricular enlargement. Until now it was unclear, whether the stage of the disease, as represented by the extent of ventricular dilatation, is somehow related to the changes of SP levels in the CSF. Our study is the first to provide evidence that aggravated ventriculomegaly is accompanied by enhanced changes of rheologically active compounds in the CSF and therefore introduces completely new aspects for hydrocephalus testing and conservative therapeutic approaches.SS performed the experiments and wrote the article. AW performed experiments. JD, CR, MPirlich wrote the article and performed statistical analysis. PV, AS and K-TH wrote the article. UQ and MPreuß designed the experimental setting and wrote the article. UQ and MPreuß contributed equally to the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease caused by the expansion of a CAG trinucleotide repeat encoding an abnormally long polyglutamine tract (PolyQ) in the huntingtin (Htt) protein. In HD, striking neuropathological changes occur in the striatum, including loss of medium spiny neurons and parvalbumin-expressing interneurons accompanied by neurodegeneration of the striosome and matrix compartments, leading to progressive impairment of reasoning, walking and speaking abilities. The precise cause of striatal pathology in HD is still unknown; however, accumulating clinical and experimental evidence suggests multiple plausible pathophysiological mechanisms underlying striatal neurodegeneration in HD. Here, we review and discuss the characteristic neurodegenerative patterns observed in the striatum of HD patients and consider the role of various huntingtin-related and striatum-enriched proteins in neurotoxicity and neuroprotection. Huntington’s disease (HD) is an autosomal dominantly inherited neurodegenerative disorder characterized by the late onset of gradually worsening motor, cognitive, and psychiatric disturbances . At presIn HD, the striatum has been identified as the primarily affected structure, which undergoes severe degeneration. As a core structure of basal ganglia circuits, the striatum integrates midbrain dopaminergic inputs and neocortical and thalamic glutamatergic inputs, and then sends GABAergic outputs to its target nuclei, such as the globus pallidus and substantia nigra. The striatum plays a critical role in processing information related to motor function and reward- and goal-oriented behavior. To facilitate the understanding of HD neuropathology, we briefly review normal striatal anatomy .1/D5 receptor (D1R/D5R) activation and influence the induction of long-term depression (LTD) via the cyclic guanosine monophosphate (cGMP) pathway  IMA107 demonstrated a 25%–33% reduction in striatal PDE10A 25 years earlier than predicted symptomatic onset [PET imaging studies provide important insights about HD pathogenesis. PET can detect various molecular changes in HD gene expansion carriers before disease manifestation ,129. Curic onset ,136. Decic onset , and lowic onset . The PDEic onset . In HD aic onset ,139. PDEic onset . Moreoveic onset .2Rs, brain metabolism, and cortical gray and white matter volume at approximately 10 years before disease manifestation [1Rs and D2Rs are significantly reduced by 25%–50% [1Rs and 4%–6.3% for D2Rs [Increased microglial activation begins at approximately 15–20 years before symptom manifestation ,142,143.estation ,129. In  25%–50% ,145. At for D2Rs ,146.2Rs were selectively ablated [Along the sagittal axis of the brain, the caudal caudate nucleus (CN) and putamen show greater neuronal loss than the rostral CN and putamen. Moreover, along the coronal axis of the brain, the dorsal and medial striatum are more degenerated than the ventral and lateral striatum . Magneti ablated .Wild-type Htt and mHtt are expressed in the brain without significant inter-regional variation, which suggests that the pathogenic process is not a direct effect of mHtt toxicity ,151. IncIn patients with HD, striatal vulnerability may be associated with reduced corticostriatal input due to impairment of BDNF, an important neuromodulator and trophic factor. Wild-type Htt positively regulates BDNF, whereas mHtt fails to regulate BDNF . BDNF isRhes is a guanine nucleotide-binding protein that may affect small ubiquitin-like modifier (SUMO) modification and preferentially interact with mHtt . Rhes isIn HD, the most striking neuropathology is the primary and progressive degeneration of MSNs and parvalbumin interneurons and relative sparing of cholinergic and NOS-containing interneurons in the striatum ,170,171.N-methyl-d-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in the CN and cerebral cortex of symptomatic patients [In HD, glutamate excitotoxicity within the striatum has been proposed as a possible cause of MSN vulnerability ,186,187.patients ,190 and patients ,192 withpatients ,194. In patients ,196,197.NMDA glutamate receptors form heteromeric dimers of NR1 and NR2 subunits . The NR2In post-mortem studies of HD brain tissue, decreased activity in mitochondrial respiratory chain complexes II, III, and IV was found . By main1R decrease observed in striatal PET studies, the annual striosomal degeneration rate is estimated to exceed that of D2Rs at the premanifest stage by 2–3-fold. We hypothesize that individuals with pronounced matrix neurodegeneration had prior striosomal neurodegeneration to some extent, and rapid matrix neuron loss may produce the matrix-predominant neurodegeneration pattern. Since the striosome and matrix compartments are responsible for motor focusing and motor scaling [1Rs are required for the maintenance of exaggerated movements, and neurodegeneration of matrix D2Rs reinforces this enlargement in movements as well. Rapid matrix neurodegeneration may be associated with mHtt-related neurotoxicity. Since autopsied brains are usually acquired at the end stage of the disease, it may be useful to investigate the striatal neuropathology of animal models. In a study of a transgenic rodent model of HD, preferential loss of striosomal neurons was reported [Differential neurodegeneration of the striatal compartments has been implicated in HD ,11,218.  scaling , striosoreported . Moreovereported . These creported .1Rs and D2Rs were decreased, a relative increase in dopamine transmission could occur at the network level. Dopamine overflow in the striatum also results in a relative increase in striatal glutamatergic inputs.Preferential striosomal neurodegeneration may disinhibit dopaminergic neurons in the SNc via the striosomal pathway . It is h1R overstimulation exerts a neurotoxic effect, whereas D2R stimulation can be neuroprotective [1R and G-protein α subunit (Gαolf) are preferentially expressed in the striosome compartment, which may be associated with striosomal vulnerability [2R/Gαi–mediated adenylyl cyclase inhibition and does not directly interact with D1R [2AR/cAMP/PKA activity selectively under the conditions of dopamine/adenosine-related drug challenge in A2AR/D2R-expressing MSNs [2AR is evenly distributed in the striatum [2AR/cAMP/PKA signaling in A2AR/D2R-expressing MSNs [Preferential striosomal MSN degeneration may disrupt motor focusing via the acetylcholine-mediated transcompartmental pathway and increase motor scaling via increased striosomal pathway-mediated dopaminergic input . In HD, otective . The D1Rrability ,235. Rherability ,237 or irability ,239,240.striatum , hence ping MSNs .In HD, early-stage preferential striosomal neurodegeneration is followed by late-stage neuron loss in the surrounding matrix ,79,188. Enhanced dopaminergic and glutamatergic inputs to the striatum may cause neurotoxicity due to impaired calcium-buffering capacity and subsequent neurodegeneration of the matrix compartment ,245,246.1R activities via interaction with PSD-95 [1 signaling at post-synaptic transmission sites. These PSD-95 activities may also exert protective effects against excessive glutamatergic and dopaminergic inputs in the matrix compartment [Wild-type Htt is a negative regulator of glutamate receptor and Dh PSD-95 . This inpartment . PSD-95 partment . By phospartment  and may partment .1R and Gαolf and deficient in calbindin-D28K, PSD-95, NPY, and CDK5-pY15. Consequently, striosomal MSNs may be more susceptible to D1R- and glutamate-mediated excitotoxicity than matrix MSNs. A better understanding of the pathogenic mechanisms by which neurodegeneration primarily and progressively occurs in the striatum in HD patients can be achieved with further in vivo and in vitro studies on striatal cell type- and compartment-specific vulnerability to neurotoxicity caused by mHtt.When examining pathophysiological changes in patients with Huntington’s disease, several factors should be taken into consideration. For several reasons, in early-stage HD, preferential striatal MSN neurodegeneration and predominant striosomal neuron loss occur due to susceptibility, not specificity. This implies that it is important to examine the differences between MSNs and non-MSN neurons or between the striosomal and matrix compartments of the striatum. Structure-specific protein expression may be a key contributor to the neurodegenerative phenomena of the HD brain . These p


Introduction: Amyloid beta 1-43 (Aβ43), with its additional C-terminal threonine residue, is hypothesized to play a role in early Alzheimer’s disease pathology possibly different from that of amyloid beta 1-42 (Aβ42). Cerebrospinal fluid (CSF) Aβ43 has been suggested as a potential novel biomarker for predicting conversion from mild cognitive impairment (MCI) to dementia in Alzheimer’s disease. However, the relationship between CSF Aβ43 and established imaging biomarkers of Alzheimer’s disease has never been assessed.Materials and Methods: In this observational study, CSF Aβ43 was measured with ELISA in 89 subjects; 34 with subjective cognitive decline (SCD), 51 with MCI, and four with resolution of previous cognitive complaints. All subjects underwent structural MRI; 40 subjects on a 3T and 50 on a 1.5T scanner. Forty subjects, including 24 with SCD and 12 with MCI, underwent 18F-Flutemetamol PET. Seventy-eight subjects were assessed with 18F-fluorodeoxyglucose PET (21 SCD/7 MCI and 11 SCD/39 MCI on two different scanners). Ten subjects with SCD and 39 with MCI also underwent diffusion tensor imaging.Results: Cerebrospinal fluid Aβ43 was both alone and together with p-tau a significant predictor of the distinction between SCD and MCI. There was a marked difference in CSF Aβ43 between subjects with 18F-Flutemetamol PET scans visually interpreted as negative  and positive , p < 0.001. Both CSF Aβ43 and Aβ42 were negatively correlated with standardized uptake value ratios for all analyzed regions; CSF Aβ43 average rho -0.73, Aβ42 -0.74. Both CSF Aβ peptides correlated significantly with hippocampal volume, inferior parietal and frontal cortical thickness and axial diffusivity in the corticospinal tract. There was a trend toward CSF Aβ42 being better correlated with cortical glucose metabolism. None of the studied correlations between CSF Aβ43/42 and imaging biomarkers were significantly different for the two Aβ peptides when controlling for multiple testing.Conclusion: Cerebrospinal fluid Aβ43 appears to be strongly correlated with cerebral amyloid deposits in the same way as Aβ42, even in non-demented patients with only subjective cognitive complaints. Regarding imaging biomarkers, there is no evidence from the present study that CSF Aβ43 performs better than the classical CSF biomarker Aβ42 for distinguishing SCD and MCI. Treatment of this devastating disease will depend on biomarkers that can reliably identify individuals who will develop dementia due to AD in the future. A previous study following patients with mild cognitive impairment (MCI) for 2 years, found that the baseline cerebrospinal fluid (CSF) levels of amyloid-beta 1-43 (Aβ43) could distinguish patients that converted to AD dementia from those that did not, suggesting that CSF Aβ43 could be a useful addition to the more well-studied CSF biomarkers amyloid beta 1-42 (Aβ42), total tau (t-tau), and tau phosphorylated on position 181 (p-tau) . Aβ43 di species , others  species . Whether species . Cerebra species  as a com species . Some PS of Aβ43 . In a trormation . Aβ43 haormation .Information is sparse regarding CSF Aβ43 as a potential clinical biomarker, especially in early stages of cognitive impairment. Previously, it has been shown that CSF Aβ43 levels are decreased in MCI and AD dementia as compared to controls, with a strong correlation between CSF levels of Aβ43 and Aβ42 . At the in vivo. Uptake of amyloid-binding PET tracers, like 18F-Flutemetamol (18F-FLUT), correlates inversely with CSF Aβ42 levels (18F-fluorodeoxyglucose (18F-FDG) PET imaging  imaging allows visualization of cerebral Aβ aggregates 2 levels , and pos2 levels . It rema2 levels , and mic2 levels . Several2 levels . Neurode imaging . In AD d2 levels  and 18F-e in MCI .18F-FLUT PET, and secondly whether CSF Aβ43 correlates with MRI and 18F-FDG PET imaging findings of neurodegeneration in non-demented patients with cognitive complaints.The objectives of this study were to explore firstly whether CSF Aβ43 reflects cerebral amyloid deposits as visualized by 18F-FLUT PET either at the time of inclusion (n = 22) or at a second assessment 2 years after first inclusion in the project (n = 18). Clinical assessment, lumbar puncture, and MRI were done within 3.5 months of 18F-FLUT PET. Thirty-one of the 40 subjects also underwent 18F-FDG PET. Patients were interviewed and examined by a physician trained in diagnosing cognitive disorders. A clinical report form was used to collect information about current cognitive symptoms both from the participant and a knowledgeable informant. Standardized cognitive testing, physical examination, and blood screening were completed. MCI (n = 12) was defined based on the core criteria in the recommendation from the National Institute on Aging-Alzheimer’s Association  was defined according to the recommendations by the Subjective Cognitive Decline Initiative Working Group .Forty subjects were included in the Dementia Disease Initiation (DDI) project at Akershus University Hospital between March 2013 and March 2016. They were referred by their general practitioner to the hospital’s memory clinic or recruited through newspaper advertisements. Inclusion criteria were complaints of decline in cognitive capacity compared with a previously normal state, age 40–79 and Scandinavian first language. Exclusion criteria were established dementia, neurodevelopmental disorders, known brain injury including recognized previous stroke, as well as any serious somatic or psychiatric disorder or drug use that could significantly influence cognitive capacity. All subjects were assessed with  NIA/AA; . Documen NIA/AA; , Trail M NIA/AA; , Control NIA/AA;  and the  NIA/AA;  or a sco NIA/AA; . All sub NIA/AA; . Subjectng Group , with non = 89). Subjects in Cohort 2 were defined as having MCI (n = 39) if objective cognitive impairment was evident on at least one of the following screening tests; MMSE score below 28, score equivalent to mild impairment on one or more of the items of the Cognistat (n = 11).Fifty subjects were included in the MCI project at Akershus University Hospital between January 2007 and February 2013 after having been referred to the hospital’s memory clinic by their general practitioner. Inclusion criteria were cognitive complaints for at least 6 months and age 40–79. Exclusion criteria included established dementia, major psychiatric disorder, drug abuse, significant solvent exposure, and anoxic brain damage. All subjects underwent lumbar puncture and MRI at inclusion. The subjects were assessed with clinical interview, routine physical examination, blood screening, and a battery of cognitive tests. One subject was found to have been included in Cohort 1 and was therefore excluded from Cohort 2 when data from both cohorts were analyzed together .g for 10 min within 4 h of collection. The 1.5 ml CSF was stored at -80°C prior to analysis of the traditional CSF biomarkers Aβ42, t-tau and p-tau. In Cohort 1, the 4.5 ml CSF was aliquoted into 450 μl polypropylene tubes before storage at -80°C, while in Cohort 2 the 4.5 ml CSF was stored at -80°C, before later being thawed and aliquoted, with further storage at -80°C prior to determination of Aβ43. Consequently, the samples underwent one freeze-thaw cycle before determination of Aβ43 in Cohort 1 and two in Cohort 2, with the exception of two samples in Cohort 2 where due to lack of CSF in the biobank, remaining CSF after analysis of the traditional CSF biomarkers was used also for analysis of Aβ43, resulting in three freeze-thaw cycles.Lumbar puncture was performed generally between 8 a.m. and noon, at the L3/L4, L4/L5, or L5/S1 interspace and without any serious adverse events. The first 4 ml CSF was used for routine clinical investigations. The next 1.5 and 4.5 ml CSF were collected in two polypropylene tubes and centrifuged at 2000 × ® β-amyloid 1–42 (® hTau Ag (® phosphoTau (181P)   (Fujirebu (181P) ; t-tau >® β-amyloid 1–42 have 50x less affinity for Aβ43 compared to Aβ42. Samples of CSF were analyzed undiluted and in duplicate. The measurement range for the kit was reported to be 2.34–150 pg/ml. All samples analyzed in the study (7.51–66.49 pg/ml) fell within this range. Intra- and inter-assay variations have been reported previously  run according to the instructions given by the manufacturer. The antibodies included in the kit were anti-human Aβ (38–43) rabbit IgG affinity purity and anti-human Aβ (82E1) mouse IgG MoAb Fab’ affinity purity. According to the manufacturers of the kits the cross-reactivity for Aβ42 in the Aβ43 ELISA is <1% and the antibodies in the Innotesteviously . As thisAPOE) genotyping was performed on EDTA blood samples from all subjects at the Gene Technology Division, Department of Interdisciplinary Laboratory Medicine and Medical Biochemistry, Akershus University Hospital according to the laboratory’s routine protocol using real-time PCR combined with a TaqMan assay .Apolipoprotein E , in-plane resolution = 1.2 × 1.2 mm2, bandwidth = 840 Hz/pixel. Cortical reconstruction and volumetric segmentation was performed with the FreeSurfer image analysis suite version 5.3.01. This includes segmentation of the subcortical white matter and deep gray matter volumetric structures . The fractional anisotropy, mean, radial, and axial diffusivities were assessed in the following four tracts, selected based on previous reports of DTI changes in AD and MCI  tracer and after resting were positioned head-first supine in the scanner. A low-dose CT scan was acquired first for attenuation correction. Subjects fasted at least 6 h in advance and blood glucose was measured routinely before 18F-FDG injection . PET scanning in 3D-mode commenced 45 min after injection of 18F-FDG and 90 min after 18F-FLUT. PET data were acquired for 10 min for 18F-FDG and for 20 min (four frames of 5 min) for 18F-FLUT. Acquired data were corrected for random events, dead time, attenuation, scatter, and decay. PET volumes were reconstructed with an iterative algorithm  and smoothed with a post-reconstruction 3D Gaussian filter of 3 mm full-width at half maximum. Image format for 18F-FDG was 256 × 256 (pixel size 1 mm × 1 mm), for 18F-FLUT 192 × 192 (pixel size 1.3 mm × 1.3 mm), with slice thickness 3.75 mm. In Cohort 2 18F-FDG PET/CT-scans were acquired as previously described  toolbox. Due to missing dynamic images one subject had to be excluded from the automated quantitative analyses (n = 39). Five cortical ROIs known to harbor substantial amyloid plaques in AD were selected for analysis of 18F-FLUT uptake: the precuneus and posterior cingulate combined, anterior cingulate, prefrontal, inferior parietal and lateral temporal cortex. The average 18F-FLUT uptake in each of these ROIs was calculated incorporating values from both hemispheres. The average uptake in the cerebellar cortex, which is usually devoid of amyloid pathology in early AD, was used as the reference region after eroding voxels at the segmentation boundaries to avoid influence due to inaccurate segmentation or co-registration. Regional standardized uptake value ratios (SUVRs) for 18F-FLUT were created by dividing the average uptake in each ROI by the average uptake in the cerebellar cortex.Visual interpretation of the 18F-FDG activity. Uptake in the cerebellar white matter was used as the reference region after first eroding the cerebellar white matter mask. SUVRs were calculated by dividing the average uptake of 18F-FDG per voxel in each ROI to the average uptake in the cerebellar white matter.The same ROIs that were used for the structural MRI analyses were selected for study of p-values below 0.05 were considered significant. Distribution of the variables and whether normal distribution could be assumed were assessed by histograms and a Shapiro–Wilk test. Levene’s statistics were calculated to assess the homogeneity of variance in each variable for parametric tests. For comparisons of CSF biomarker levels, demographical data and neuropsychological test results between SCD and MCI, a χ2 test was used for categorical variables, an independent sample t-test for continuous variables with normal distribution, and a Mann–Whitney U test for continuous variables with non-normal distribution. Binary logistic regression models were created with the SCD/MCI distinction as the dependent variable and with either CSF tau, p-tau or one of the imaging biomarkers as a covariate. In significant models, CSF Aβ43 and Aβ42 were then in turn added as a second covariate. Bivariate correlation and partial correlation controlling for age were assessed between CSF biomarkers, MRI, 18F-FLUT and 18F-FDG variables. Spearman’s rank coefficients (rho) of the correlations between an imaging variable and Aβ43, and the imaging variable and Aβ42, were compared with an asymptotic z test using software available from http://quantpsy.org  curves for the prediction of a positive 18F-FLUT scan were plotted for both Aβ peptides, and area under the curve (AUC) was calculated. Cut-off values for Aβ43 and Aβ42 yielding the best combination of sensitivity and specificity, were determined by maximal Youden’s index. Differences in AUC were assessed using MedCalc statistical software .The statistical analyses were performed using IBM SPSS version 23  unless otherwise stated. All tests were two-sided and tpsy.org . To deteTable 1. The frequency of the APOEε4 genotype was similar in both SCD and MCI. The pattern of relative levels of the two CSF Aβ peptides was similar with respect to APOE genotype, with significantly lower mean peptide levels in the group with APOEε4/ε4 . No significant difference for the correlations between APOE allele and Aβ43 and Aβ42 levels was found. There was a strong positive correlation between the CSF measurements of Aβ43 and Aβ42  with no significant difference between the groups (SCD rho 0.81 and MCI rho 0.86). In the MCI group both Aβ43 and Aβ42 correlated inversely with t-tau and p-tau without any significant difference between the two amyloid peptides . In the SCD group, however, there were no significant correlations between Aβ43/Aβ42 and t-tau and p-tau. For all subjects (n = 89) the overall correlation coefficients for Aβ43:t-tau was rho -0.28, Aβ42:t-tau rho -0.25, Aβ43:p-tau rho -0.29, and for Aβ42:p-tau rho -0.37, without any significant differences between Aβ43 and Aβ42. Adjustment for age did not significantly change the correlations between the CSF biomarkers. In binary logistic regression models for the distinction between SCD and MCI, both Aβ43, Aβ42, and p-tau were statistically significant predictors when entered into the model as the only CSF biomarker. In a multivariate model with p-tau, inclusion of Aβ43 added significantly to the prediction, while Aβ42 did not (Table 2).Demographical characteristics, cognitive scores and CSF biomarker levels in the SCD and MCI groups are reported in 18F-FLUT PET scans were deemed to be positive , two borderline positive (one CN and one MCI), two borderline negative (one SCD and one MCI), and 27 negative . The mean CSF Aβ43 in these four groups were 15, 22, 32, and 37 pg/ml, respectively, and the mean CSF Aβ42 547, 657, 1074, and 1042 pg/ml. The mean difference [95% CI] in CSF concentration between subjects with positive and negative scans was 22 pg/ml  for Aβ43 and 495 pg/ml  for Aβ42, p < 0.001 for both. ROC curves for prediction of a positive scan gave AUC 0.97 for both Aβ43 and Aβ42. The best cut-off value was ≤24 pg/ml for Aβ43 with sensitivity 100% and specificity 93% for a positive scan, and ≤679 pg/ml for Aβ42 with sensitivity 100% and specificity 89%. In the group with negative scans, 16/27 (59%) had APOE genotype APOEε3/ε3 and 9/27 (33%) APOEε3/ε4, the same in the group with positive scans was 2/9 (22%) and 5/9 (56%), but the differences were not statistically significant. 18F-FLUT SUVR based on the automated quantitative assessment of the scans was not a significant predictor of the SCD/MCI distinction in binary logistic regression models with and without age as a covariate. Mean overall 18F-FLUT SUVR for the five ROIs was 1.29, 95% CI [1.17–1.40] in the SCD group and 1.40, 95% CI [1.23–1.58] in the MCI group (p = 0.26). There were highly significant inverse correlations between CSF Aβ43 and 18F-FLUT SUVR in all the examined ROIs and the correlations became stronger with adjustment for the effect of age (Table 3). The same was true for Aβ42, and there were no significant differences between the correlation coefficients for the two Aβ peptides. The correlations with overall 18F-FLUT SUVR remained strong also when analyzing only subjects diagnosed with SCD . When excluding subjects with visually interpreted definitely positive scans from the analysis (n = 31), there were still significant correlations with overall SUVR; for Aβ43 unadjusted rho -0.37, p = 0.04, adjusted for age rho -0.52, p = 0.004, for Aβ42 unadjusted rho -0.39, p = 0.03, adjusted for age rho -0.51, p = 0.004. CSF t-tau and p-tau were not significantly correlated with overall 18F-FLUT SUVR .Based on visual interpretation nine of the 40 Table 4). In the SCD group CSF Aβ43 correlated significantly with hippocampal volume and CSF Aβ42 with thickness of the posterior cingulate cortex. In the MCI group there were no significant correlations after correction for age. None of the correlations were significantly different between CSF Aβ43 and Aβ42, there was only a trend toward the correlation coefficient for hippocampal volume being stronger with CSF Aβ43 than Aβ42  in the SCD group. Both CSF Aβ43 and Aβ42 were significant predictors of hippocampal volume in linear regression both with and without age in the model. T-tau was a significant predictor when modeled alone and with age, but not when either of the CSF Aβ peptides were entered into the same model.Magnetic resonance imaging data from the two cohorts were analyzed separately due to the use of different scanners for the imaging acquisition. There were no significant differences in mean hippocampal volume or thickness in the six cortical ROIs between SCD and MCI in either of the cohorts (Supplementary Table) and none of the ROIs were significant predictors of the SCD/MCI distinction in binary logistic regression models even after adjustment for age. In Cohort 1 there were no significant correlations between Aβ43/42 CSF levels and the structural MRI measurements. In Cohort 2 there were unadjusted moderate positive correlations for both CSF Aβ43 and Aβ42 with total hippocampal volume, thickness of the middle temporal, inferior parietal and inferior frontal cortices with statistical significance after correction for multiple testing in seven ROIs. CSF Aβ43 correlated significantly also with thickness of the ERC and CSF Aβ42 with posterior cingulate cortical thickness. After adjustment for effects of age, however, only the correlations with hippocampal volume, inferior parietal and inferior frontal cortical thickness were nominally significant (Table 5).Diffusor tensor imaging was only available for Cohort 2. There were no statistically significant differences in the DTI metrics of the selected tracts between the SCD and MCI groups and in logistic regression models none of the DTI metrics were significant covariates for the SCD/MCI distinction even after adjustment for age. Both CSF Aβ43 and Aβ42 were inversely correlated with axial diffusivity in the corticospinal tract and this was the only correlation that maintained significance after correction for multiple testing. Both Aβ peptides showed a nominal significant negative correlation with mean diffusivity in the cingulum bundles and corticospinal tract. There was also a nominal significant negative correlation for CSF Aβ42 and radial diffusivity, but a positive correlation with fractional anisotropy in the cingulum bundles. Comparing the two Aβ peptides, the positive correlation with fractional anisotropy in the cingulum bundles was slightly stronger for CSF Aβ42 compared to CSF Aβ43, though it did not reach significance after strict Bonferroni correction for multiple testing . In the larger Cohort 2 (n = 50) both CSF Aβ43 and Aβ42 appeared to be correlated with glucose metabolism in the hippocampus and several of the cortical ROIs, but this changed after correction for the effect of age when only the correlation between CSF Aβ42 and 18F-FDG uptake in the entorhinal cortex was significant after correction for multiple testing. Looking at the SCD and MCI subjects separately, the correlation between CSF Aβ43 and 18F-FDG uptake in the posterior cingulate cortex was nominally significant after correction for age, while the only significant correlation after correction for multiple testing was that between CSF Aβ42 and 18F-FDG uptake in the posterior cingulate cortex in the MCI group (Table 6). By direct comparison none of the differences in correlation coefficients between CSF Aβ42 and Aβ43 were statistically significant. Unadjusted there was an inverse relation between p-tau and overall 18F-FDG uptake (average of the seven ROIs), but after correction for age there were no significant correlations with either p-tau or t-tau.As the 18F-FLUT only binds Aβ when it has formed extensive β-sheet formations in insoluble fibrils, and does not bind to the soluble Aβ oligomers that are suggested more likely to be the main neurotoxic culprit in AD (The interest in CSF Aβ43 as a biomarker first arose from experimental data suggesting that this peptide could be more prone to aggregation than Aβ42, and thus potentially have importance for amyloidogenesis in AD . Our resit in AD . Whetherit in AD . Future Measurements of Aβ43 in CSF have not previously been described in relation to cerebral imaging findings, while CSF Aβ42 has been extensively studied. CSF Aβ42 has been shown previously to correlate with hippocampal volume in several cross-sectional studies . Some lo18F-FLUT PET have been reported to be superior to the core CSF biomarkers in predicting the conversion from MCI to dementia (White matter changes are also known to be related to CSF biomarkers . CSF Aβ4dementia . Therefo18F-FLUT PET, nor could we evaluate the impact of biomarkers on the distinction between controls and SCD. This ought to be assessed in future studies.The current study has several limitations. The use of different MRI and PET-scanners in the two cohorts made direct comparisons between cohorts challenging. Cortical thickness was measured to be higher in Cohort 2 than in Cohort 1 for several of the ROIs even though p-tau levels were on average higher in Cohort 2. Correcting for age resulted in only a slight reduction in the between cohort differences in these ROIs. It is known that differences in scanner field strength and possibly also scanner settings like pulse sequence, can impact on regional cortical thickness measurements , which mIn this first description of CSF Aβ43 in relation to imaging biomarkers, we found that CSF levels of Aβ43 are inversely correlated with fibrillary Aβ accumulation in the brain and more weakly positively correlated with biomarkers of neurodegeneration including hippocampal volume. However, none of the studied correlations between CSF Aβ and imaging measurements were significantly different between the two Aβ peptides when controlling for multiple testing. We conclude that in respect to imaging, CSF Aβ43 does not appear to contribute any added value over the well-established CSF biomarker Aβ42 in distinguishing individuals with SCD from those with MCI.18F-FDG PET images and performed clinical examinations of participants. CC processed the 18F-FLUT PET images. BG visually interpreted 18F-FDG and 18F-FLUT PET scans. MW carried out laboratory work and administered the CSF biobank. RG did neuropsychological assessments of participants. AB supervised imaging acquisition. LW, SS, GB, and TF supervised the project. All authors critically revised and approved the manuscript.IA planned the study, recruited and clinically examined study participants, performed the statistical analyses and wrote the manuscript. CL planned the study and performed ELISA of CSF Aβ43 together with IM. PS and LK processed the MRI and The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer IB and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.


Amblyseius swirskii, which was previously well able to learn prey during early life, enhancing foraging later in life. We investigated whether, and, if so, how quickly, a transgenerational diet switch to live prey restores the early learning ability of foraging predatory mites. The first experiment shows that the early learning ability was restored after switching the diet of the pollen-fed predator line to live spider mites for two generations before conducting the behavioral assay. The second experiment reveals that offspring regained their learning ability if the diet of their mothers was switched from pollen to spider mites for 3 or 10 days before offspring production. Both experiments in concert suggest transgenerational, pollen-induced operational loss of learning ability in the predatory mite A. swirskii. Maternally-transmitted nutrient deficiency and/or maternally-induced epigenetic changes are the most plausible explanations for the pollen diet-induced loss of learning ability. Our study represents a key example for maternal diet-induced variation in learning ability.The ability to learn is ubiquitous in animals but highly variable within and between species, populations and individuals. Diet-related circumstances, such as diet quantity and quality can influence both long-term constitutive  and short-term operational  learning performance. Here, we scrutinized the causes of loss of learning ability, following multi-generational feeding on pollen, in a line of the predatory mite  Drosophila melanogaster. Regarding constitutive variability, Mery and Kawecki   if and how quickly the predators regain their learning ability after a maternal diet switch to live prey. We designed two experiments, which in concert should allow discriminating between the two major pathways possibly resulting in loss of learning ability, long-term selection (constitutive) or short-term  trans-generational nutritional deficiency and/or epigenetic changes.The goals of our research were to scrutinize (1) if Amblyseius swirskii used in the experiments derived from a population originating from Koppert Biological Systems . Upon receipt in October 2013, the population was split into two lines: one line was reared on ‘Nutrimite’ , a diet exclusively consisting of cattail pollen T. angustifolia , and the other was reared on a diet of two-spotted spider mites Tetranychus urticae Koch (Tetranychidae) . Both lines were reared on separate artificial arenas until conducting the experiments in spring 2015. The artificial arenas consisted of acrylic tiles (15 × 15 × 0.2 cm), resting on water-saturated foam cubes in plastic boxes (20 × 20 × 6 cm). Water-saturated tissue paper wrapped around the edges of the tiles confined the arenas. The arenas were equipped with small cotton tufts under cover slips to provide shelter and oviposition sites for the predators , using a mite brushing machine , onto glass plates and from there onto the rearing arena. Tetranychus urticae was reared on whole bean plants P. vulgaris grown at room temperature 23 ± 2 °C and 16:8 h L:D photoperiod.Frankliniella occidentalis Pergande (Thripidae). Dead thrips were offered to ease feeding on thrips by the juvenile predators—live thrips are difficult to grasp and kill—to enable reinforcement and thus associative learning. Thrips were reared on detached bean leaves of P. vulgaris (ca. 11 × 13 cm) placed upside down on a 1% agar solution in a closed petri dish (14 cm diameter). For ventilation, a circular opening (1 cm diameter) was cut into the lid and covered with gauze. To obtain first instars, adult thrips were randomly withdrawn from the stock population, reared on whole green bean pods inside glass jars, and transferred to a fresh bean leaf for 24 h for oviposition. After removing adult thrips, the petri-dish was stored in a climate chamber at 25 ± 1 °C, 65 ± 5% relative humidity (RH) and 16:8 h L:D photoperiod for 3.5 days. At that time most larvae had hatched, which were then either directly withdrawn and used in the experiment as live prey or placed into acrylic cages, using a fine brush, killed by deep-freezing at −18 °C for at least 2 h and used in the experiment as dead prey.Prey used in the experiments were live and dead (killed by deep-freezing) first instars of Western flower thrips All predator rearing arenas, thrips rearing units and experimental cages were kept in climate chambers at 25 ± 1 °C, 65 ± 5% RH and 16:8 h L:D photoperiod.A. swirskii from the PO–PO line can regain their learning ability by a multi-generational diet switch. To this end, a third line of A. swirskii, which derived from the PO–PO line, but the diet of which was switched to spider mites, was established . Twenty gravid females from the PO–PO line were placed on a new leaf arena (as described above), marked with small water color dots on their dorsal shields and reared on spider mites until the gravid females of the F1 generation (having no color dots) appeared on the arena, that is, approximately 10 days after establishing the PO–SM line. Offspring of the F1 generation were used in the experiment.The first experiment aimed at determining whether To obtain even-aged eggs giving rise to the experimental individuals, ten gravid females were randomly taken from each line, PO–PO, SM–SM and PO–SM, and placed on new, separate small leaf arenas (50 × 50 mm) for oviposition and fed with both spider mites and pollen. After 24 h, eggs were collected, using a fine moistened brush, and placed singly into acrylic cages  previously loaded with either pollen (to generate thrips–naïve predators) or two dead plus one live thrips larvae (to generate thrips-experienced predators). The cages were closed at the bottom with fine gauze and on the upper side with a microscope slide  or 10–11 days (PO–PO–10 SM). Eggs produced by the predator females on the 3rd or 4th day (PO–PO–3 SM) or 10th or 11th day (PO–PO–10 SM) after transfer to the spider mite arena were used for the experiment. The eggs were singly placed into acrylic cages and the emerging juvenile mites subjected to the same pollen and thrips experience and consolidation phases, and mated females then subjected to the same bioassay, as described for the first experiment. Two lines and two types of early diet experience resulted in four treatments: PO–PO–3 SM + pollen, PO–PO–3 SM + thrips, PO–PO–10 SM + pollen and PO–PO–10 SM + thrips. Each treatment was replicated 19 or 20 times.The second experiment aimed at determining whether Statistical analysis was carried out using SPSS 21 . In the first experiment, we used separate generalized linear models (GLM) to analyze the influence of predator line  and early thrips experience (yes/no) on (1) the attack latency of adult predator females , (2) the number of individual feeding events on thrips , and (3) the number of eggs laid by each female , followed by pairwise LSD or Sidak tests, if needed, to separate predator lines. Attack latency was defined as the time elapsed until the predators successfully attacked and killed the first thrips larva  and predator line (PO–PO–3 SM or PO–PO–10 SM) on (1) the attack latency of adult predator females , (2) the number of individual feeding events on thrips , and (3) the number of eggs laid per female . Likewise, separate one-sided GLMs were used to assess whether early thrips experience shortened the attack latency of adult predator females within each predator line (PO–PO–3 SM or PO–PO–10 SM) . GEE was used to compare cumulative consumption of thrips over time  as affected by thrips experience and line identity (PO–PO–3 SM and PO–PO–10 SM).12 = 5.118, P = 0.02), in the larval and early protonymphal stage, as well as predator line  affected the attack latency of adult A. swirskii females on first-instar thrips . Predators from the PO–SM and SM–SM lines attacked thrips more quickly than predators from the PO–PO line  and PO–SM  lines but not in the PO–PO line . Thrips-experienced predators from the SM–SM and PO–SM lines were faster in attacking thrips than thrips–naïve predators, which was not the case for predators from the PO–PO line. The mean number of individual feeding events on thrips by A. swirskii across time ranged from 0.50 ± 0.15 (SE) to 1.00 ± 0.22 and was neither affected by thrips experience early in life , nor by predator line  or their interaction . Cumulative consumption of thrips by A. swirskii over time was affected by thrips experience early in life  and predator line  but not the interaction   significantly higher thrips consumption by thrips-experienced than thrips–naïve predators in the PO–SM  and SM–SM  lines but not PO–PO  line  nor predator line  had a main effect on the number of eggs laid by A. swirskii, but the interaction was significant  , whereas in the SM–SM  and PO–PO lines , thrips–naïve and thrips-experienced predators laid similar numbers of eggs , in the larval and early protonymphal stage, as well as predator line  affected the attack latency of adult A. swirskii females on first-instar thrips . Thrips–naïve predators from both lines attacked thrips later than did thrips-experienced predators. Predators from the PO–PO–3 SM line attacked thrips later than did predators from the PO–PO–10 SM line, regardless if they had experienced thrips in early life or not. Within each of the two lines, thrips experience in early life significantly shortened the attack latency of A. swirskii on first-instar thrips . The number of individual feeding events on thrips by A. swirskii across time was affected by predator line , but not by thrips experience early in life , and not by the interaction . No matter if the predators experienced thrips early in life or not, predators from the PO–PO–10 SM line fed significantly more often on thrips than predators from the PO–PO–3 SM line . Cumulative consumption of thrips by A. swirskii was affected by thrips experience in early life  and predator line  but not by the interaction   nor predator line  had an effect on the number of eggs laid by A. swirskii .Thrips experience early in life (GLM; Wald χA. swirskii females, which derived from a line that had been reared on pollen for >50 generations and had lost its foraging learning ability, regained their learning ability after multi-generational and maternal diet switches from pollen to spider mites. Full restoration of the learning ability of offspring occurred already after a 3 days-long maternal diet switch from pollen to spider mites. The time elapsed since maternal diet switch enhanced the overall predation performance of the offspring.Our study shows that offspring of N. californicus (Schausberger et al. A. swirskii (Christiansen et al. The first experiment shows restoration of the learning ability of the predatory mites after multi-generational diet switch from pollen to spider mites. Restored learning ability was evident in shorter attack latencies on first-instar thrips by adult predators after learning thrips in early life. The ability to learn prey in early life, resulting in faster recognition times and shorter attack latencies was shown before for A. swirskii can regain their learning ability by short-term maternal diet switches before offspring production, this experiment also suggests maternal effects on the overall vigor of the predators. The vigor of the predators apparently increased with the time elapsed since maternal diet switch, as indicated by the generally shorter attack latencies on the difficult-to-grasp prey thrips, and higher consumption rates of offspring from mothers switched to spider mite prey for 10 days than those switched for only 3 days. Other maternal effects described for predatory mites relate to foraging and predation risk contexts. For example, P. persimilis females, which are food-limited during internal egg formation, produce smaller-sized offspring (Walzer and Schausberger The second experiment more precisely determined the time period needed for restoration of the predators’ learning ability. Thrips-experienced predators produced by mothers, whose diet had been switched to live prey for 3 or 10 days before offspring production, attacked thrips more quickly, and consumed more thrips, than naïve predators, which is similar to the results of the first experiment. Revealing that T. angustifolia, as well as other plant-derived substances, might lack certain nutrients, which are necessary for learning, for example to strengthen existing, or establish new, neuronal connections. The nutritional value of pollen may differ from plant to plant and its digestibility for the animal as well (Roulston and Cane A. swirskii fed on the tomato russet mite Aculops lycopersici laid more eggs and developed more quickly than A. swirskii reared on cattail pollen (Park et al. T. angustifolia contains 5–14% starch and up to 17% protein (Schmidt, Buchmann and Glaum N. californicus, Neoseiulus cucumeris, Amblyseius andersoni, and Amblydromalus limonicus reared on T. urticae, T. angustifolia or on a liquid artificial diet consisting of honey, sucrose, tryptone, yeast extract and egg yolk, and showed that especially broad diet generalists, like N. cucumeris, A. andersoni, and A. limonicus, performed better on the artificial diet. Clearly, suboptimal nutritional diets, e.g. when A. swirskii feeds on pollen, may not only negatively affect life history traits but also cognition (for review: Gomez-Pinilla T. angustifolia pollen and possibly being responsible for the loss of learning ability requires further scrutiny.Early restoration, following the switch to live prey, of the learning ability of predators from the PO–SM lines in both experiments excludes laboratory selection as cause of loss of learning ability in the PO–PO line. Since laboratory selection can be excluded, restoration of the predators’ learning ability must respresent a nutritional (Xia et al. Apis mellifera, bees from different castes with different, distinct phenotypes share the same genotype (Herb et al. Metaseiulus occidentalis, was sequenced (Hoy et al. Assuming epigenetic mechanisms, genes, which need to be active for the process of learning prey, might be inactive in offspring from purely pollen-fed mothers. One of the first suggestions that DNA methylation affects gene expression and therefore regulates the on-and-off switching of genes during development was made by Holliday and Pugh . For exaThe results of our study are of importance for the use of predatory mites in biological control. Predatory mites are commonly reared on factitious food or other prey than the target pest (Fidgett and Stinson


Efforts to identify fluid biomarkers of Parkinson’s disease (PD) have intensified in the last decade. As the role of inflammation in PD pathophysiology becomes increasingly recognized, investigators aim to define inflammatory signatures to help elucidate underlying mechanisms of disease pathogenesis and aid in identification of patients with inflammatory endophenotypes that could benefit from immunomodulatory interventions. However, discordant results in the literature and a lack of information regarding the stability of inflammatory factors over a 24-h period have hampered progress.n = 12) and age-matched healthy control (HC) subjects (n = 6) at 11 time points across 24 h to (1) identify potential diurnal variation, (2) reveal differences in PD vs HC, and (3) to correlate with CSF levels of amyloid β (Aβ) and α-synuclein in an effort to generate data-driven hypotheses regarding candidate biomarkers of PD.Here, we measured inflammatory proteins in serum and CSF of a small cohort of PD  were stable across 24 h and different between HC and PD. Regression analysis revealed that C-reactive protein (CRP) was the only factor with a strong linear relationship between CSF and serum. PD and HC subjects showed significantly different relationships between CSF Aβ proteins and α-synuclein and specific inflammatory factors, and CSF IFNγ and serum IL-8 positively correlated with clinical measures of PD. Finally, linear discriminant analysis revealed that serum TNF and CSF α-synuclein discriminated between PD and HC with a minimum of 82% sensitivity and 83% specificity.Our findings identify a panel of inflammatory factors in serum and CSF that can be reliably measured, distinguish between PD and HC, and monitor inflammation as disease progresses or in response to interventional therapies. This panel may aid in generating hypotheses and feasible experimental designs towards identifying biomarkers of neurodegenerative disease by focusing on analytes that remain stable regardless of time of sample collection.The online version of this article (doi:10.1186/s12974-017-0935-1) contains supplementary material, which is available to authorized users. A growing body of literature supports a role for peripheral and central immune cells and inflammation in the pathogenesis and progression of neurodegenerative diseases including Parkinson’s disease (PD) –5. Yet tAlthough there is no consensus on the role of inflammation as a primary, causative, or early factor in PD or a secondary byproduct of disease, and the role of inflammation in PD is still a hypothesis, there is a great interest in establishing which inflammatory markers can help stage disease and identify disease endophenotypes, to inform immunomodulatory interventions, as has been effectively demonstrated in multiple sclerosis , for exa40), and 1–42 (Aβ42), and to define an ideal set of inflammatory factors in serum and CSF to be used in conjunction with levels of CSF α-synuclein and Aβ proteins to distinguish PD vs HC subjects with sensitivity and specificity.Herein, we describe CSF and serum inflammation at 11 time points across a 24-h period (spanning in effect 26 h) in PD patients and age-matched HC subjects in an effort to identify a subset of readily detectable and stable inflammatory factors. The first part of this study evaluated the stability of key CSF biomarkers, alpha-synuclein, DJ-1, and Abeta1-42 in young healthy volunteers in a two period study using the same sampling schedule as outlined here. A minimum of two weeks separated the repeat sampling for blood and CSF . Given tA total of 18 PD and 8 HC subjects were screened; of these, 12 subjects with PD (as diagnosed by a movement disorder-trained physician based on the widely employed criteria ) and 6 aSubjects were admitted to the clinic the day prior to sample collection for baseline assessments, as described above. Lumbar and venous catheters were inserted, and CSF and blood were collected concurrently over 26 h . Vital signs were taken regularly, and subjects remained in the clinic for at least 24 h following sample collections for monitoring, and a final neurological examination before discharge. Study procedures were safe and well tolerated with no serious adverse events related to the procedure .https://www.michaeljfox.org/page.html?id=193&navid=data-biospecimens). CSF samples were collected via intrathecal catheter connected to a peristaltic roller pump. Sample collection (6 ml) started approximately 12 min before each identified sampling time and was concluded approximately 6 min after. The catheter was cleared of any residual CSF before sample collections to ensure fresh sampling. CSF samples were centrifuged at × 1600g for 15 min at 4 °C, supernatants were transferred into six 0.5 mL-aliquots and three 1.0 mL-aliquots in polypropylene tubes, and stored at − 80 °C or on dry ice within 1 h of collection. CSF samples contaminated with blood were discarded. The quality of the CSF samples was visually colorless, and red blood cell (RBC) analysis from the 2-ml discard sample was conducted at each time point. The analysis was conducted within 1 h of sample collection. Whole blood (10 ml) was collected in red top vacutainers, centrifuged at × 1350g for 15 min at 4 °C, serum was transferred into six 0.5 mL-aliquots and three 1.0 mL-aliquots, stored in polypropylene cryotubes, and stored at − 80 °C until shipment on dry ice. All subjects had CSF assessment for blood contamination (hemoglobin ELISA) as part of the safety assessments, typically twice during each catheterization period. In general, if not noted in the clinical study report, this would mean a clinically insignificant finding for RBCs in ~  98% of the samples.The samples were accessed through the 24 h Biofluids bank, a subset of a large Michael J. Fox Foundation biospecimen bank available to the community , Aβ1–40 was detected using the Covance Sandwich ELISA , total protein was determined using a BCA protein assay (ThermoFisher/Pierce Bio), and hemoglobin was measured by sandwich ELISA (Bethyl Laboratories) according to manufacturer’s instructions.α-Synuclein concentration was determined using an ELISA with a luminescent readout  electrochemiluminescent (ECL) immunoassay V-PLEX human pro-inflammatory panel  and a V-PLEX human NGAL  and CRP  panel on the MSD SECTOR Imager 2400-A  according to manufacturer’s instructions. CSF and serum samples were run in duplicate, and serum samples were diluted by a factor of 1. Data were analyzed by laboratory personnel blinded to condition using MSD integrated data analysis software that converts ECL signal to pg/mL values based on standard curves of calibrator proteins. All % coefficient of variation (CV) values for samples analyzed were below 5%.U rank-sum nonparametric test was used to compare levels of each analyte between HC and PD subjects at time 0  in the serum and CSF of PD and age-matched HC subjects. IL-1β, IL-2, IL-4, IL-10, IL-12, and IL-13 were not reliably above the lower limit of detection for the MSD assay and therefore were excluded from analyses. We also examined the relationship between the consistently detectable inflammatory factors and CSF levels of more established neurodegenerative disease-specific biomarkers, specifically α-synuclein, AβClinical heterogeneity and inter-individual variability are significant challenges to interpreting clinical data and critical determinants of statistical power. Thus, we first determined individual variability in inflammatory factors across 24 h. Regression analyses revealed marked variability across time in multiple inflammatory factors in both HC and PD subjects  = 10; p = 0.01) and higher levels of serum NGAL  = 10; p = 0.01) at baseline than age-matched HC subjects  = 208.58; p < 0.0001, and F = 8.49; p = 0.009, respectively), and serum NGAL  = 25.98; p < 0.0001). All three analytes showed minimal variability across time  = 0.11; p = 1.00, F = 0.38; p = 0.95, and F = 0.73; p = 0.69, respectively). CSF α-synuclein, Aβ40, and Aβ42 were significantly lower in PD patients as compared with HC subjects  = 8.35; p = 0.01, F = 14.69; p = 0.001, and F = 4.61; p = 0.05, respectively). Depending on time of day, these CSF markers were different in PD versus HC subject groups. There were no differences between PD and HC across time in any other analytes.Despite finding significant within group variability and a relatively small sample size, we found significant differences between serum TNF and serum NGAL levels in PD versus HC at baseline , CRP (p = 0.03), IL-8 (p = 0.02), and Aβ42 (p = 0.006) levels across the day were best fit by a parabolic (not linear) relationship in PD subjects, but not in HCs , indicating that the levels of these CNS proteins rise and fall across the day more in PD subjects versus HC. CSF IL-6 levels rise and fall across in the day in HC subjects (p = 0.01), but not in PD (p = 0.12), and CSF Aβ40 levels across the day were best fit by a quadratic trend in both PD (p = 0.02) and HC (p = 0.004) subjects.We next determined whether inflammatory analytes, α-synuclein, and/or Aβme Table . The patNext, we investigated the extent to which levels of an inflammatory marker in serum correlated with its levels in the CSF  = 14.0; p = 0.02) and in PD  = 243.8; p < 0.0001), but the slopes did not differ between HC and PD subjects (t(16) = 1.28; p = 0.22) (a). Serum and CSF NGAL (b), TNF (c), IL-6 (d), IL-8 (e), and IFNγ (f) did not correlate at baseline, indicating that serum and CSF levels of these factors do not reflect one another . We found no significant correlation between serum and CSF levels of any factor except CRP , while HC subjects did not. HC subjects demonstrated a relationship between CSF IFNγ and CSF α-synuclein (c), CSF CRP and CSF Aβ40 (d), and serum CRP and CSF Aβ40 (e), while PD subjects did not . At time 0, CSF NGAL, CSF IFNγ, CSF CRP, and serum CRP co-varied with α-synuclein, Aβner Fig. : PD subjner Fig. a as well40, and Aβ42; data not shown).Next, we analyzed relationships between serum and CSF inflammation and clinical measures of disease state as determined by the UPDRS, its components , and disease duration  to determine if any particular analyte or a particular set of analytes allowed for correct assignment of group membership . LDA revealed that inflammation and a neurodegenerative disease marker correctly assign individuals to HC or PD groups. Indeed, serum TNF alone misclassified only 17% of PD subjects into the HC group and only 17% of HC subjects Fig. e. Serum Serum and CSF IL-6 alone incorrectly assign 100% of HC individuals into the PD group  and age-matched HC subjects (n = 6). The key findings are summarized in Table Several studies have investigated the extent to which Parkinson's disease (PD) pathophysiology is associated with increased inflammatory status. However, due to conflicting results , there iWe stress that the sample size of this study is very small and designed to generate new hypotheses and inform a larger study. While age and BMI were controlled for , indicating that multiple sampling points may be required. However, repeated sampling protocols increase intra-subject variability of CSF Aβ protein levels , highligIdentification of TNF and IFNγ as potential candidate biomarkers is noteworthy given that both of these cytokines have been strongly implicated in degeneration of nigral dopaminergic neurons and basal ganglia pathologies in pre-clinical and post mortem studies –63. LeveTogether, our results strongly suggest that serum TNF and serum NGAL are the most promising candidate inflammatory biomarkers because they remain relatively invariant throughout a 24-h period in the majority of subjects and because their levels are significantly different between PD and HC groups both at time 0 and across the day. To our knowledge, this is the first study to investigate NGAL protein levels in blood or CSF of PD subjects. NGAL  is an acute phase protein  involved42 levels fit a quadratic equation indicating a parabola shape when graphed across the day  in PD subjects but not HCs. CSF IL-6 levels rise and fall across in the day in HC subjects, but not PD, and CSF Aβ40 levels across the day were best fit by a quadratic trend in both PD and HC subjects. CSF TNF has the clearest pattern of change across the day, increasing from below 1 pg/mL at 5:30 AM to around 2 pg/mL at around 11:30 AM and finally back down to below 1 pg/mL at 5:30 AM the next day. However, there was no difference in CSF TNF levels between PD and HC subjects at any time point, and the lack of significant quadratic trend in HC CSF TNF is likely due to a small sample size. Although few studies have sampled biofluids across the day to determine diurnal inflammatory patterns, one study in adult insomnia patients and age-matched HCs (~ 27–31 years old) found disturbed rhythms in plasma IL-6 and increased plasma TNF  in insomniacs, when measured every 30 min across 24 h [40 and Aβ42 across the day [We next demonstrated that CSF TNF, CRP, IL-8, and Aβoss 24 h . Althougoss 24 h  bears stoss 24 h . Additio the day . Togethe the day . One rea the day .Though serum and CSF inflammatory factors have been suggested to reflect one another, there are conflicting reports . Therefo40), and Aβ42 are proteins currently under intense investigation as potential biomarkers of neurodegenerative disease. Levels of inflammatory factors in the brain are likely to be changing prior to frank neuronal death because microglia, the innate immune cells in the brain produce cytokines when activated in response to aggregated proteins [40, and Aβ42) in a disease-dependent manner. These exciting and novel data suggest that serum and CSF inflammation may be associated with abnormalities in CSF levels of α-synuclein, Aβ40, and Aβ42. Interestingly, all significant relationships between CSF and serum inflammation and CSF toxic peptide levels at time 0 are positive such that as inflammation in CSF or periphery increases, so do CSF α-synuclein and Aβ peptides, providing additional evidence that inflammation and toxic oligomer species may be part of a feed-forward cycle of protein aggregation-neuroinflammation.Alpha (α)-synuclein, Amyloid-beta-40  is comprised of several components including the mentation, behavior, and mood score, the activities of daily living score (ADL), the motor evaluation score, and the complications of therapy score. Our data indicate that serum IL-8 levels were significantly and positively associated with the ADL scores component of the UPDRS. As serum IL-8 was stable across the day in the majority of PD individuals, this factor may be of interest in future longitudinal studies to determine whether it is associated with disease severity irrespective of disease duration or time of day. Consistent with this idea, the association of IL-8 with clinical severity was also reported in a recent analysis of human serum in a multi-center cohort of 142 subjects with familial PD arising from leucine rich repeat kinase 2 (LRRK2) mutations where high levels of IL-8, MCP-1, and CCL4 were associated with the presence of a specific clinical subtype that is characterized by a broad and more severely affected spectrum of motor and non-motor symptoms . With reGiven that PD is an extremely heterogeneous disease with differing rates of progression , 92, ourWe used linear discriminant analysis (LDA) to determine if any particular analyte or set of analytes allowed for correct assignment of any one sample to PD or HC group membership across time. To our surprise, there was a relatively minimal misclassification when only serum TNF levels were considered; serum TNF alone misclassified, at most, only 25% of PD subjects (3 of 12) into the HC group, and 33% of HC (2 of 6) subjects into the PD group across all time points. When serum TNF and CSF α-synuclein levels are considered together, ≥ 82% sensitivity and 83% specificity were achieved in both HC and PD groups across all time points; CSF tau and Aβ demonstrate comparable sensitivity (but less specificity) as diagnostic tools for Alzheimer’s disease . TogetheIn summary, our data indicate that serum and CSF NGAL, TNF, IFNγ, and CRP, and serum IFNγ are promising candidate biomarkers of inflammation. Importantly, we emphasize that the sample size in our study was very small and confirmatory studies will be needed in a larger cohort of subjects to validate these findings using current biorepositories with banked serum and CSF, details regarding the employed collection protocol, and good clinical history data for each subject Additional file 2:Serum TNF and NGAL are significantly different between PD and HC at time 0. (PDF 561 kb)Additional file 3:Serum TNF, IFNγ, and NGAL are different between PD and HC irrespective of time. (PDF 1693 kb)Additional file 4:PD and HC subjects have different relationships between serum and CSF inflammation. (PDF 801 kb)Additional file 5:Summary of relationships between inflammation and Aβs and α-synuclein in PD and HC. (PDF 2346 kb)Additional file 6:F = 14.0; p = 0.02) and in PD  = 243.8; p < 0.0001), but the slopes do not differ between HC and PD (t(16) = 1.28; p = 0.22). Serum and CSF NGAL  = 0.80; p = 0.42 and PD; slope = 3.44 ± 18.28, R2 = 0.003, F = 0.04; p = 0.85) (b), TNF  = 0.68; p = 0.46 and PD; slope = 2.15 ± 2.13, R2 = 0.09, F = 1.02; p = 0.34) (c), IL-6  = 0.32; p = 0.60 and PD; slope = −0.03 ± 0.11, R2 = 0.009, F = 0.09; p = 0.77) (d), IL-8  = 1.31; p = 0.32 and PD; slope = 0.06 ± 0.22, R2 = 0.01, F = 0.08; p = 0.79) (e), and IFNγ  = 1.59; p = 0.28 and PD; slope = 9.42 ± 12.77, R2 = 0.05, F = 0.54; p = 0.48) (f) do not correlate at baseline. (PDF 74 kb)Serum and CSF CRP levels are positively correlated in HC and PD at baseline. The relationship between serum and CSF inflammatory factors was analyzed using samples from the first collection period . There was no significant correlation between serum and CSF levels of any factor except CRP (a). Serum and CSF CRP significantly correlate in HC Additional file 8:CSF IFNγ and serum IL-8 positively correlated with UPDRS components at baseline in PD subjects. (PDF 563 kb)Additional file 9:x axis is the percentage of correct categorization into a group, and the red line is set at the lowest percentage of correct categorizations for the factor or factors considered. The y axis is the collection hour. (PDF 239 kb)Linear discriminant analysis reveals sensitivity and selectivity of analytes in discriminating between HC and PD. Serum and CSF levels of IL-6 (a), serum IL-6 (b). Serum and CSF IL-8, TNF, NGAL, and IFNγ, and CSF α-synuclein, Aβ40 and Aβ42 (d), serum IL-8, TNF, NGAL, IFNγ, and CSF α-synuclein, Aβ40, and Aβ42 (e), serum and CSF IL-8, TNF, NGAL, IFNγ, and CSF Aβ40 (f), serum and CSF IL-8, TNF, NGAL, IFNγ, and CSF Aβ42 (g), serum and CSF IL-8, TNF, NGAL, IFNγ, and CSF α-synuclein (h), serum and CSF IL-8, TNF, NGAL, IFNγ (i), serum IL-8, TNF, NGAL, IFNγ, and CSF Aβ42 (j), serum IL-8, TNF, NGAL, IFNγ, and CSF α-synuclein (k), serum IL-8, TNF, NGAL, IFNγ, and CSF Aβ40 (l), serum and CSF TNF, NGAL, IFNγ, and CSF α-synuclein, Aβ40, and Aβ42 (m), serum TNF, NGAL, IFNγ, and CSF α-synuclein, and Aβ42 (n), serum TNF, NGAL, and IFNγ (o), serum and CSF TNF, NGAL, and IFNγ (p), serum TNF, NGAL, and IFNγ and CSF α-synuclein, Aβ40, and Aβ42 (q), serum TNF and CSF α-synuclein, and Aβ42 (r), serum TNF and CSF Aβ42 (s), serum IL-8, TNF, NGAL, and IFNγ (t). The Additional file 10:Subject demographics. (PDF 411 kb)Additional file 11:Inclusion and exclusion criteria summary. (PDF 1759 kb)


We investigated the influence of geographical predisposition on the spatial distribution and composition of coronary plaques.Thirty coronary arteries were evaluated. A total of 1441 cross-sections were collected from intravascular ultrasound (IVUS) and radio-frequency signal-based virtual histology (VH-IVUS) imaging. To exclude complex geographical effects of side branches and to localise the plaque distribution, we analysed only eccentric plaques in non-branching regions. The spatial distribution of eccentric plaques in the coronary artery was classified into myocardial, lateral, and epicardial regions. The composition of eccentric plaques was analysed using VH-IVUS.p = 0.003). No significant difference was observed between the myocardial and lateral sides (46.7 ± 7.5% vs. 20.8 ± 5.0%) or between the lateral and epicardial sides. Eccentric thin-capped fibroatheromas were more frequently distributed towards the myocardial side than towards the lateral side (p = 0.024) or epicardial side (p = 0.005).The plaque was concentric in 723 sections (50.2%) and eccentric in 718 (49.9%). Eccentric plaques were more frequently distributed towards the myocardial side than towards the epicardial side (46.7 ± 7.5% vs. 12.5 ± 4.2%, Geographical predisposition is associated with distribution, tissue characterisation, and vulnerability of plaques in non-branching coronary arteries. The central mechanism of atherosclerosis is chronic inflammation in the presence of damaged vascular endothelium and lipid-laden foamy macrophages derived from infiltration of monocytes into the arterial wall. This mechanism can lead to coronary stenosis and thrombotic obstruction after disruption of the resulting atherosclerotic plaque . AccumulThis cross-sectional observational study was carried out in a single centre. We studied 30 consecutive patients who underwent elective PCI under the diagnosis of stable effort angina pectoris and from whom satisfactory grey-scale and VH-IVUS images were obtained. This study was approved by the Nippon Medical School institutional review board, and informed consent was obtained from all patients.According to our standard protocol and previous report , all patGrey-scale and VH-IVUS images were acquired using a phased array 20 MHz, 3.2 Fr IVUS catheter  with an automated pullback of 0.5 mm/s. The IVUS catheter was tracked over a 0.014-inch guide wire up to a position distal to the diseased segment. The VH-IVUS data were recorded onto the imaging system’s hard disk, and analyses were performed independently by experienced analysts. The analysts were unaware of the angiographic findings and the patients’ baseline clinical and lesion characteristics. All measurements were derived automatically using Volcano imaging system pcVH 2.1 software. The VH-IVUS data analysis was based on grey-scale border contour calculation, and the tissue maps were provided by the software . All cross-sections located near a side branch (within twice the vessel diameter) were excluded from analysis to minimise confounding by flow turbulence. The plaque eccentricity index was the ratio of maximum to minimum plaque thicknesses calculated as previously suggested . An eccePerivascular IVUS landmarks—coronary veins, pericardium, myocardium, and side branches—were used for vessel orientation, as previously described . Based oVH-thin-cap fibroatheroma (VH-TCFA) was defined according to a previous study, in which VH-TCFA was a plaque burden  exceeding 40% over three consecutive frames, with a confluent necrotic core whose arc was in contact with the lumen for 36° along the lumen circumference . We counAll IVUS images were analysed by two experienced investigators who were blinded to the angiographic data and clinical presentations. When discordance occurred between the observers, a consensus reading was obtained from another investigator.p < 0.05.All data were analysed using SPSS version 21.0 . All data were analysed by the Shapiro-Wilk test for distribution of normality, and the test showed that the data were not distributed normally. The data were analysed by a non-parametric statistical method, the Kruskal-Wallis test. The post-hoc multiple comparison was performed automatically in SPSS. Data are presented as mean ± SEM. Statistical significance was considered as The baseline characteristics of the patient population are presented in Table p = 0.003). The minimum vessel diameter was significantly smaller at sites where lateral side plaque was observed, compared with sites with epicardial side plaque. The maximum vessel diameter was significantly smaller at sites with lateral side plaque than at sites with myocardial side plaque. The plaque area of lateral side plaques was significantly smaller than that of myocardial or epicardial plaques.The results of the grey-scale IVUS data are summarised in Table We analysed the composition of eccentric plaque by using VH-IVUS and classified it into four categories: Fibrous, fibro-fatty, necrotic core, and calcification. The results are summarised in Table p = 0.024) or in epicardial side plaques   than in lateral side plaques  Fig. .Fig. 3DiThe major findings of this study are as follows. (1) Eccentric plaques were more frequently distributed towards the myocardial side than towards the lateral and epicardial sides of the coronary artery. (2) A significant difference was observed in the diameters of the eccentric plaque vessels between the distributed sides. (3) The difference in the plaque component between the distributed sides was also significant. TCFAs were more frequently observed in myocardial side plaques than in lateral or epicardial side plaques.Coronary arteries are continually subjected to mechanical force, such as tensile or compressive stress and shear stress generated by the heartbeat and pulsatile blood flow during each cardiac cycle , 12. Amop = 0.108), and numerically larger on the epicardial side than on the lateral side . The average plaque area was approximately 40% (44.83 ± 0.75%), and the compensatory vascular remodelling was associated with geographical predisposition.Vascular adaptation by shear stress allows the arterial tree to deviate from a straight-tube geometry to another morphology. This phenomenon permits the shear stress to remain unchanged, which provides the predilection site for eccentric plaque development . Human aUsing VH-IVUS imaging, a spatial relationship between low shear stress and the necrotic core was observed in early plaques (plaque burden <40%) , while iIn a previous study using integrated backscatter IVUS , Sato etLongitudinal studies in porcine models have shown that TCFAs, which develop more frequently in the coronary regions, are exposed to low shear stress throughout their evolution , 25. AutAlthough the precise mechanisms that promote the focal formation of rupture-prone coronary plaques in vivo remain to be elucidated, we found that eccentric TCFAs were clustered towards the myocardial side. We only analysed eccentric plaques, which may be predisposing to future coronary events . The relThe limitations of this study are as follows. First, the sample size was small; only 30 coronary arteries in 30 patients were analysed. Second, all the patients were in stable condition, and their plaque phenotype may have been different from that of unstable patients. Third, we included right arteries, in which atherosclerotic change may differ from that in left coronary arteries . Fourth,Eccentric coronary plaques are more often observed on the inner side of the coronary arteries. The geographical predisposition of myocardial distribution in the human coronary artery was associated with a larger lipid burden, a thinner fibrous cap, and a higher prevalence of TCFA. The geographical classification of coronary plaques using IVUS is applicable in clinical settings to elucidate the complex profile of atherosclerotic plaque.


Orchitis is common in adult male spinal cord injury (SCI) patients and, therefore, both health professionals and SCI patients themselves tend to attribute testicular swelling to orchitis, with a consequent potential delay in the diagnosis of testicular tumours. A 37-year-old man with paraplegia developed swelling of the right testis. With a presumptive diagnosis of acute bacterial orchitis, he was prescribed ciprofloxacin while awaiting an ultrasound scan. Ultrasound examination of the testis 4 weeks later showed a moderate hydrocele, enlargement and altered echogenicity of both the epididymis and testis, and features of mass-like lesions within the substance of the testis. As these changes might merely have represented a partly treated infection, a follow-up scan was carried out 2 weeks later, which revealed a lobulated mass of mixed echogenicity within the testis and a focal area of increased echogenicity indicative of calcification. A radical orchidectomy performed 19 days later revealed a seminoma. To prevent delay in the diagnosis of testicular tumours in SCI patients, we propose the following measures: (1) patients who develop swelling of the testis should consult a physician as soon as possible for clinical examination; blind antibiotic therapy should be avoided if possible; (2) if clinical examination reveals a hard swelling of the testis and the typical features of acute urinary infection are absent, an ultrasound scan of the scrotum should be performed as soon as possible; (3) in patients with equivocal ultrasound findings, ultrasound-guided, fine-needle aspiration cytology may allow an early diagnosis of testicular malignancy; (4) education of SCI patients and their caregivers is needed to implement these recommendations.