Introduction of the so-called biocompatible peritoneal dialysis (PD) fluids was based on a large body of experimental evidence and various clinical trials suggesting important clinical benefits. Of these, until now, only preservation of residual renal function—likely due to lower glucose degradation product load and, in case of icodextrin, improved fluid and blood pressure control—have consistently been proven, whereas the impact on important clinical endpoints such as infectious complications, preservation of PD membrane transport function, and patient outcome, are still debated. In view of the high morbidity and mortality rates of PD patients, novel approaches are warranted and comprise the search for alternative osmotic agents and enrichment of PD fluids with specific pharmacologic agents, such as alanyl-glutamine, potentially counteracting local but also systemic sequelae of uremia and PD. Taken together, there is a high medical need to improve the clinical outcome of children who undergo PD. Current clinical and experimental data suggest that the nonphysiological composition of PD fluids plays a major role in putting the child at risk for infectious and noninfectious complications. This review first focuses on the current status of PD fluids then discusses underlying pathomechanisms that put patients at risk for peritoneal and systemic complications and, second, provides a short summary and outlook on current research and development to tackle these problems in order to improve PD therapy.Treatment with peritoneal dialysis (PD) allows home-based renal replacement therapy (RRT) and thus better patient integration into normal social life, such as school and other aspects important for quality of life (QoL), even though imposing a major burden to families. In pediatric nephrology, PD is the most frequently used dialysis modality until adolescence . More thThe indication for PD fluids is replacement of renal function in acute and chronic renal failure, thus the removal of water and solutes (including uremic toxins) from the uremic patient. The therapeutic effects of these fluids are achieved through their physicochemical composition, i.e., ultrafiltration (UF) of water by the nonphysiologically increased concentration of osmotically or oncotically active agents  and low concentrations or absence of solutes that should be removed . Currently, registered single-chamber-bag glucose-based PD fluids are CAPD®, Dianeal®, and Gambrosol®, and the so-called biocompatible multi-chamber-bag fluids are BicaVera®, Balance®, Gambrosol trio®, Physioneal®, and the non-glucose-based fluids Nutrineal® and Extraneal® , only 3 % of PD patients use amino-acid-containing fluids, presumably due to the limited anabolic effect and the widespread use of tube feeding.In addition, alternative osmotically  active agents were introduced in single-chamber bags, such as the glucose polymer icodextrin and a mixture of amino acids. Albeit reducing the typical toxicity of glucose (heat sterilization of icodextrin still results in glucose degradation byproducts), these novel compounds exhibit nonspecific (such as low pH) and novel substance-specific bioincompatible properties. For example, use of icodextrin resulted in sterile inflammation, most likely due to contamination with bacterial fragments during the production process , 15, andLow-sodium PD fluids have yielded ambiguous findings in adult PD patients. Applying dialysate solutions with <105 mmol/l of sodium resulted in loss of UF, unless the decline in solution osmolarity was compensated for by glucose . PD fluiWith regard to actual outcome data obtained in adequate clinical trials, the level of evidence for improved clinical outcome is still not undisputable in favor of the novel PD fluids. Early clinical trials demonstrated an improved biocompatibility based on effluent markers, such as mesothelial cell viability marker cancer antigen 125 (CA-125) , 24, intThe impact of low GDP fluids on preservation of long-term peritoneal membrane transport function, however, remains elusive. Small-sized RCTs in adults found no differences in peritoneal transport capacity with low and high GDP fluids over time , 38, witwww.eutripd.eu) demonstrate major transformation of the peritoneal membrane with low GDP fluids, including inflammation, progressive fibrosis and angiogenesis [Compelling evidence for a persistently high degree of bioincompatibility of low GDP fluids and thus the still great medical need to improve PD fluids to be used in the pediatric population is emerging from the International Pediatric Peritoneal Biopsy Study Group see Fig. . Data frogenesis , i.e. alogenesis , and cleogenesis . Quantitogenesis .Fig. 2RePD fluids play a major role in triggering and/or propagating pathomechanisms that are likely relevant for local and systemic complications in PD see Fig. . These cLocal infectious complications—in particular, peritonitis—still belong to the most frequent, debilitating, and costly complications of PD . There iInfectious stimuli together with repeated physicochemical insults by bioincompatible PD fluids propagate smouldering inflammation in the peritoneal cavity. Experimental research in animal and cell culture systems has established a clear role for IL6 in chronic smouldering inflammation triggered by infectious stimuli . IL6 is The combination of chronic infectious, inflammatory, and cytotoxic insults—in particular, in the context of a diabetes-like milieu—results in abnormal peritoneal repair and stress processes. In particular, regulation of epithelial mesenchymal transdifferentiation (EMT) has been shown to play a major role in peritoneal fibrosis and angiogenesis, ultimately resulting in UF failure in PD . RegardiUltimately, the above described pathomechanisms might not be relevant for local infectious and noninfectious complications only but also for systemic effects, such as further propagating uremia-induced systemic inflammation. Indeed, use of PD fluids that are more biocompatible has resulted in some evidence of reduced systemic inflammation  and bettBased on the concept that the common denominator for most pathomechanisms underlying local and systemic complications of PD is glucose-related toxicity, two PD-fluid-related work streams to improve PD therapy become obvious: replacement of glucose by alternative, biologically inert, but osmotic active substances; and/or addition of active compounds to counteract glucose-related toxicity.The main problem in replacing glucose in PD fluids comes from the massive total daily amount of any such osmotic compound needed. Provided an average prescription of four 2-l bags of 1.5 % glucose-based PD fluid to an adult patient, the local peritoneal exposure amounts to 120 g of glucose, of which more than half is systemically absorbed. Replacement of glucose by amino acids is therefore limited to a single exchange per day due to specific adverse effects, such as elevation of blood urea nitrogen, metabolic acidosis, and uremic symptoms . Absorptwww.clinicaltrials.gov, of these compounds, only glycosaminoglycans such as heparin and low molecular weight heparinoids have reached the clinical stage but have not resulted in reproducible clinical benefits (online search January 2016).Due to the relative safety of glucose experienced over >50 years of intraperitoneal use and its well-known side effects, an alternative approach is the addition of active compounds counteracting glucose-related toxicity see Fig. . The majhttp://www.uptodate.com/contents/peritoneal-dialysis-solutions). Of these compounds, only carnitine and alanyl-glutamine dipeptide are in clinical development. For both agents, favorable profiles of systemic effects have been previously reported, such as improved metabolism and immune competence [Another promising approach is the addition of nontoxic agents to PD fluids in doses shown to be safe by prior use as nutritional supplements in patients with stage 5 chronic kidney disease on dialysis (CKD5D). Historically, several such compounds were tested in experimental settings but did not successfully undergo further clinical development (mpetence . In a rempetence . Additiompetence , 60 and A large body of experimental evidence and respective RCTs have resulted in the introduction of the currently available, so-called biocompatible, PD fluids, which have yielded certain clinical benefits such as better preservation of residual renal function and, in the case of icodextrin, improved fluid control and blood pressure. However, they still exert considerable toxicity, and their benefits regarding infectious complications, PD membrane preservation, cardiovascular health, and patient survival still need demonstration. PD patient morbidity and mortality is still unacceptably high in both adult and pediatric patients, mandating innovative approaches, i.e., PD fluids with less local and systemic toxicity and, ideally, even actively counteracting uremic sequelae such as carbonyl and oxidative stress, as may be the case with specific pharmacologic agents such as alanyl-glutamine dipeptide. Introduction of various novel PD fluids with specific local and systemic benefits should allow for a more personalized treatment of PD patients. Not all patients may benefit from these more costly PD fluids, and thus identification of those who will have a particularly high benefit from the novel therapy or who have a particularly high risk with the current standard fluids is required. Thus, development of new PD fluids must go hand-in-hand with the development of biomarkers to identify patients in need of these innovative therapies. How far this approach will improve overall outcome and QoL of PD patients will be the topic of future research.


Cognitive theories of attention-deficit/hyperactivity disorder (ADHD) propose that high within-subject fluctuations of cognitive performance in ADHD, particularly reaction time (RT) variability (RTV), may reflect arousal dysregulation. However, direct evidence of arousal dysregulation and how it may account for fluctuating RTs in ADHD is limited. We used skin conductance (SC) as a measure of peripheral arousal and aimed to investigate its phenotypic and familial association with RTV in a large sample of ADHD and control sibling pairs.N = 292), consisting of 73 participants with ADHD and their 75 siblings, and 72 controls and their 72 siblings, completed the baseline  and fast-incentive conditions of a RT task, while SC was simultaneously recorded.Adolescents and young adults (A significant group-by-condition interaction emerged for SC level (SCL). Participants with ADHD had decreased SCL, compared with controls, in the baseline condition but not the fast-incentive condition. Baseline SCL was negatively associated with RTV, and multivariate model fitting demonstrated that the covariance of SCL with RTV, and of SCL with ADHD, was mostly explained by shared familial effects.ADHD is associated with decreased, but modifiable, tonic peripheral arousal. A shared familial cause underlies the relationship between arousal and RTV and between arousal and ADHD. Given the malleability of SCL, if our findings are replicated, it warrants further exploration as a potential treatment target for ADHD. Attention-deficit/hyperactivity disorder (ADHD) has long been proposed to link to problems with the arousal system. Cognitive theories of ADHD, such as the state regulation model , 2 or moMeasuring skin conductance (SC) provides an objective, reliable measurement of arousal in the peripheral nervous system . SC sensThe aspect of cognitive performance that most strongly fluctuates in people with ADHD is their speed of responding on standard reaction time (RT) tasks, leading to high RT variability (RTV) , 25, 26.et al.  RT performance. Second, we aimed to test if a fast-incentive condition increases SC-indexed arousal, and if it does, whether it increases more in the ADHD group than in the control group. Third, for the SC variables that show group differences, we aimed to investigate their familial association with RTV and ADHD diagnosis, using sibling model fitting analyses, and to consider specific causal models that may explain the relationships that emerge.Participants are members of the Sibling EEG Follow-Up Study (SEFOS) , 33, 34,n = 9), extreme drowsiness (n = 1)] and 9 control participants [SC equipment failure (n = 8) and met ADHD criteria based on parent report (n = 1)]. The final sample consisted of 73 ADHD probands , 75 siblings of ADHD probands , 72 controls , and 72 control siblings .From the original follow-up sample of 404 participants, 311 had SC measured . We excluded from the analyses 10 ADHD participants [SC equipment failure (n = 144); siblings of ADHD probands were excluded unless they had an ADHD diagnosis themselves. For these analyses, the ADHD and control groups did not differ in sex  but did differ in age  and IQ . In all these analyses we included age as a covariate, and in additional analyses we added IQ as a second covariate. For the model fitting analyses (aim 3), all participants were included and differed in age , sex , and IQ . In these analyses we therefore used age and sex as covariates, with additional analyses also including IQ as a further covariate. All participants were of European Caucasian descent.For the ADHD control group differences analyses (aims 1 and 2), both members of control sibling pairs formed the control group  , a semisThe slow-unrewarded (baseline) condition consists of 72 trials, which followed a standard warned four-choice RT task. Four empty circles  first appeared for 8 seconds, after which time one of them (the target) was colored in. Participants were asked to press the response key that directly corresponded to the position of the target stimulus. After a response, the stimuli disappeared from the screen, and a fixed intertrial interval of 2.5 seconds followed. Speed and accuracy were emphasized equally in the task instructions. If the participant did not respond within 10 seconds, the trial terminated. A comparison condition of 80 trials with a fast event rate (fore-period of 1 second) and incentives followed the baseline condition . The fasSC data were measured by attaching a pair of reusable 8-mm-diameter silver-silver chloride electrodes on the palm of the hand (thenar eminence and hypothenar eminence) of the participant’s nondominant hand at the start of the testing session. A nonsaline gel was used to increase impedance and to help establish an electrical signal. A constant imperceptible voltage (0.5 V) was applied.SC was recorded using PSYCHLAB SC5 24-bit equipment system, which has an absolute accuracy of ±0.1 microsiemen . The SC5 was connected to a computer to run the PSYCHLAB software, where were monitored, recorded in real time, and automatically digitized. Stimulus onset and participant response events were recorded on a common timeline, which enabled SC activity to be stimulus locked.SC data values were calculated using a SC system that is based on a SC sigmoid-exponential model that allows the tonic measure of SCL to be disentangled from phasic, stimulus-associated SCRs and further allows the decomposition of overlapping SCRs , 39, 40.n = 15) removed; the pattern of results remained the same (results are available on request). Analyses were rerun using anxiety and depression scores from the Clinical Interview Schedule-Revised , condition (baseline vs. fast-incentive), and interactions for SC variables and RTV, the data were analyzed using random intercept models and logistic regression in Stata. The random intercept model is a multilevel regression model that can be used as an alternative to analysis of covariance to control for genetic relatedness  in a repeated-measures design, using a robust cluster command to estimate standard errors , 43, 44.Structural equation modeling in OpenMx  was usedMultivariate modeling on sibling data uses the additional cross-sib cross-trait information to decompose the observed phenotypic correlation (Rph) between traits into etiological factors. Similar sibling design analyses have been previously performed by our group [see  for a moBefore FNE modeling (described in the section above), sibling correlations were estimated from a constrained correlation model to give maximum likelihood estimates of correlations between the traits within and across pairs while applying some constraints. Applied constraints reflect the assumptions of the familial model, that is, that phenotypic correlations across traits within individuals are the same across siblings and that cross-trait cross-sibling correlations are independent of sibling order. Variables used in the sibling model fitting were selected by running phenotypic correlations on variables that showed group differences, and only variables that had a significant relationship with RTV were further analyzed.To further investigate a more etiological model that may account for the relationship between SC variables that are associated with both RTV and ADHD, and given the theoretical scope that RTV  may reflect hypoarousal , we hypothesized that RTV may mediate the relationship between SC-indexed arousal and ADHD. A phenotypic mediation model was fitted with SC variables that may be causally associated with both RTV and ADHD. Significant  mediation occurs when a third variable explains some of the association between two other variables . The pheF to 0.40, representing 80% genetic variance (in case shared environment = 0). In addition, the threshold on ADHD liability was fixed to a z value of 1.64 to correspond to a population prevalence of 5% [see Rijsdijk et al. (To account for the selected nature of the sample (selection on ADHD probands), the selection variable (ADHD status) was included in all models with its variables fixed to population-known values. In the correlation and mediation model this involves fixing the sibling correlation for ADHD status to 0.40 and in the FNE models fixing k et al.  for furtz = 8.95, p = .01) and group-by-condition interaction , but no main effect of group  , but not in the fast-incentive condition  , but the ADHD group had a greater increase than controls .For SCL data, a random intercept model indicated a significant main effect of condition (p = .85) . Post hop = .27) . Both ADz = .46, p = .61), condition  or group-by-condition interaction   .All group analyses were rerun with IQ as a covariate, but the significance of results remained unchanged. Analyses were rerun using three length-matched segments from the baseline condition and testing them separately against the fast-incentive condition, but the significance of results did not change  and S2. r = −.31, p < .01), but this correlation did not reach significance in the control group , and Fisher’s z test indicated that the correlations between the groups differed from one another at a trend level . In the fast-incentive condition, the RTV-SCL correlations were not significantly different between the groups . There were no significant SCR-RTV correlations.We ran additional phenotypic correlations to examine the SCL-RTV and SCR-RTV relationship in ADHD and control groups separately . In the Given that SCL showed a significant group-by-condition interaction, a significant correlation with RTV with large effect sizes and the biggest significant group difference in the baseline condition, we next investigated the phenotypic and etiological overlap between SCL, RTV, and ADHD in the baseline condition. The maximum likelihood phenotypic, cross-sibling, and cross-sibling-cross-trait correlations across SCL, RTV, and ADHD are presented in Sibling-pair multivariate model fitting was performed to decompose variance/covariance of traits into etiological factors F and NE . We calc2 statistic .Given the significant phenotypic and familial relationship of baseline SCL with RTV and with ADHD, we tested whether baseline RTV mediated the relationship between baseline SCL and ADHD status. In the mediation model, the causal paths specified were all significant, and partial mediation by RTV was indicated . HoweverIn a large sibling study of 292 participants, we show that tonic peripheral arousal, indexed with SCL, is decreased in young people with ADHD during performance on a baseline RT task but normalizes in a faster condition with incentives, indicating modifiable arousal dysregulation in ADHD. We further show that a substantial degree of familial sharing accounts for the significant phenotypic associations between SCL and RTV and between SCL and ADHD.The SC measure associated with ADHD was SCL. Lower SCL during baseline RT performance indicated a lower tonic level of peripheral arousal in individuals with ADHD, consistent with accounts of hypoarousal , 21, 49.Tonic peripheral arousal (SCL) normalized in the ADHD group in the fast-incentive condition, as indicated by a significant group-by-condition interaction and lack of a group difference in the fast-incentive condition. The malleability of SCL is in line with results of modifiable SC-indexed arousal , 21, 31 To investigate the familial association between SCL and RTV directly, we focused on the baseline condition that is most sensitive to ADHD. The SCL-RTV correlation was largely 94%) accounted for by shared familial influences, demonstrating that the association of underarousal with attentional fluctuations is mostly due to overlapping familial effects. Of the familial influences on RTV, half were correlated with those on SCL, indicating that peripheral arousal captures half of the familial influences that contribute to the attentional fluctuations. These findings are in line with theories linking RTV to arousal dysregulation % account, 55, 56.We further investigated the familial association between SCL and ADHD and found that shared familial effects accounted for 59% of the phenotypic correlation between them, providing further support for an etiological link between underarousal and ADHD. Of the familial influences on ADHD, a third correlated with those on SCL, demonstrating that peripheral arousal captures a third of the familial influence contributing to ADHD. However, two thirds of the familial influences on ADHD did not correlate with those on SCL, implying that there are also nonoverlapping familial influences that contribute separately to ADHD. These findings are in agreement with the view that arousal dysregulation is not the only contributing factor to ADHD, in line with the multifactorial nature of ADHD , 56, 57.In a novel attempt to investigate the causal pathways that underlie the phenotypic relationship between SCL-indexed arousal, RTV, and ADHD, we fitted a model that tests whether there are causal pathways from 1) SCL to RTV and 2) RTV to ADHD and 3) whether RTV mediates the association between SCL-indexed arousal and ADHD, or whether there is a direct causal pathway from SCL to ADHD. The mediation and causal paths between all variables were significant, suggesting that there are two pathways from SCL-indexed arousal to ADHD: an indirect causal pathway from arousal to RT fluctuations to ADHD and a direct casual pathway from arousal to ADHD. Overall, our statistical model is consistent with ADHD theories that suggest a role for arousal dysregulation in the etiology of ADHD and the observed lapses of attention , 5. It iBecause this is the first family study on skin conductance and ADHD to our knowledge, our findings await replication. SC should also be studied in relation to other cognitive tasks, to investigate the generalizability of the findings. In addition, twin studies are required to establish whether the familial influences we identified reflect largely shared genetic rather than shared environmental influences; because previous research suggests a limited role for shared environmental effects for ADHD , SC 59,,59, 60, In conclusion, we identify SCL as an informative index of underlying, malleable hypoarousal in ADHD. The demonstration of a link between SCL, RTV, and ADHD provides physiological support for the arousal dysregulation accounts , 3, 4, 5


Microorganisms in the human intestine (i.e. the gut microbiome) have an increasingly recognized impact on human health, including brain functioning. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with abnormalities in dopamine neurotransmission and deficits in reward processing and its underlying neuro-circuitry including the ventral striatum. The microbiome might contribute to ADHD etiology via the gut-brain axis. In this pilot study, we investigated potential differences in the microbiome between ADHD cases and undiagnosed controls, as well as its relation to neural reward processing.We used 16S rRNA marker gene sequencing (16S) to identify bacterial taxa and their predicted gene functions in 19 ADHD and 77 control participants. Using functional magnetic resonance imaging (fMRI), we interrogated the effect of observed microbiome differences in neural reward responses in a subset of 28 participants, independent of diagnosis.Bifidobacterium genus was observed in ADHD cases. In a hypothesis-driven approach, we found that the observed increase was linked to significantly enhanced 16S-based predicted bacterial gene functionality encoding cyclohexadienyl dehydratase in cases relative to controls. This enzyme is involved in the synthesis of phenylalanine, a precursor of dopamine. Increased relative abundance of this functionality was significantly associated with decreased ventral striatal fMRI responses during reward anticipation, independent of ADHD diagnosis and age.For the first time, we describe gut microbial makeup of adolescents and adults diagnosed with ADHD. We found that the relative abundance of several bacterial taxa differed between cases and controls, albeit marginally significant. A nominal increase in the Our results show increases in gut microbiome predicted function of dopamine precursor synthesis between ADHD cases and controls. This increase in microbiome function relates to decreased neural responses to reward anticipation. Decreased neural reward anticipation constitutes one of the hallmarks of ADHD. Attention-deficit/hyperactivity disorder (ADHD) is a common neuropsychiatric disorder, characterized by symptoms of inattention and/or impulsivity and hyperactivity. ADHD has been associated with abnormalities in the monoamine neurotransmitter systems dopamine and noradrenaline . StimulaFig 1). Consequently, differences in abundance and/or metabolic activity of monoamine precursor-producing inhabitants of the gastrointestinal tract may affect monoamine-related brain functioning and behavior relevant to ADHD. Indeed, a lowered abundance of Bifidobacterium in infancy has been associated with increased risk of developing ADHD and Asperger syndrome in childhood in a study focusing on particular microbiota [Meta-analyses of non-pharmacologic treatment interventions for ADHD showed that restriction diets for ADHD patients  may lead to a significant reduction in ADHD symptoms, although there is heterogeneity across studies –12. Conccrobiota . HoweverHere, we present the first microbiome study in ADHD patients versus healthy controls. We used bacterial 16S ribosomal RNA (rRNA) marker gene sequencing to characterize microbial communities in adolescents and young adults with ADHD and self-reported healthy controls. We specifically investigated the difference in relative abundance of monoamine precursor-related predicted genes between these microbiomes, and their potential effect on brain activity. We focused on the precursors in the monoamine biosynthesis, as monoamines themselves cannot cross the blood-brain barrier , and indFig 2; S1 Appendix). ADHD cases were derived from the follow-up of the NeuroIMAGE study [For the microbiome analyses, we included 96 participants, of whom 19 had been diagnosed with ADHD and 77 were healthy . More participants were unaffected than affected as this was a follow-up study and part of the children with ADHD no longer met the diagnostic criteria in adolescence or adulthood of the current study. Of the 63 controls, 39 participants were unaffected siblings of ADHD probands, and 24 participants were healthy controls.For the fMRI analyses, we included 87 participants from the above-mentioned ADHD cohort (NeuroIMAGE II project), of whom 24 had ADHD and 63 were unaffected  due to less than 10 correct trials per cell in the factorial design, five participants due to moving more than 5mm .Fig 2; Table 1). Of the overlapping 22 controls, 13 participants were unaffected siblings, and 9 participants were healthy controls. The regression analyses between the microbiome and the fMRI measure in this sub-sample were performed across diagnosis (n = 28); see below.For 28 included participants from the fMRI sample, group microbiome data was available: 6 patients with ADHD and 22 controls overlapped between both sample groups . The study was approved by the regional medical ethics committee .Fig A in S1 Appendix), adapted from a previous study [Reward anticipation was assessed during fMRI in the context of a rewarded Stroop task . All data were pre-processed and analyzed with SPM8 (http://www.fil.ion.ucl.ac.uk/spm/). Echo combination, realignment, slice timing, co-registration, normalization, and spatial smoothing are described in Text B in S1 Appendix.Whole-brain functional images (multi-echo) and a high-resolution anatomical scan were acquired on a 1.5T MR scanner (Text B in S1 Appendix). We performed one-sample t-tests to assess the main effects of reward anticipation (high (15 ct) > low (1 ct) reward cues) in the total sample (n = 87) as well as in the smaller sub-sample with microbiome information (n = 28). To further account for motion, we added a summary motion score for every subject in all second level analyses as covariate of non-interest (Text B in S1 Appendix). Statistical inference (p < 0.05) was performed at the cluster level, correcting for multiple comparisons  over the search volume, i.e. the whole brain.For each participant, the resulting pre-processed fMRI time-series were analyzed at the first level using an event-related approach in the context of a general linear model, including 24 motion parameters as regressors of non-interest (Text B in S1 Appendix).We investigated the effects of diagnosis using a ventral striatum region of interest (ROI), i.e. an anatomically-defined bilateral nucleus accumbens region  as described earlier . The DNAText C in S1 Appendix, where additionally sequencing and data acquisition is outlined. In short, hypervariable V3-V4 region of the 16S rRNA gene was sequenced on the 454 Life Sciences GS-FLX platform using Titanium sequencing chemistry .Preparation of the amplicon pool for pyrosequencing followed established protocols , and is http://qiime.org) [Text C in S1 Appendix).For gene sequencing analysis, a customized Python workflow based on Quantitative Insights Into Microbial Ecology (QIIME version 1.2) was adopted (ime.org)  (Text C Text C in S1 Appendix). Based on the KEGG (http://www.genome.jp/kegg/) pathway maps of ‘Phenylalanine, tyrosine and tryptophan biosynthesis’ , the 17 candidate reactions/enzymes that directly result in production of phenylalanine, tyrosine, or tryptophan were selected: EC:1.3.1.43; EC:1.3.1.78; EC:1.3.1.79; EC:1.4.1.20; EC:1.4.3.2; EC:1.14.16.1; EC:2.6.1.1; EC:2.6.1.5; EC:2.6.1.57; EC:2.6.1.58; EC:2.6.1.9; EC:4.1.99.1; EC:4.1.99.2; EC:4.2.1.20; EC:4.2.1.51; EC:4.2.1.91 and EC:5.1.1.11 [Fig B in S1 Appendix). From the relative abundances of gene functions predicted by PICRUSt we focused on candidate reactions/enzymes, by applying in-house bioinformatic (Perl-coded) scripts. Relative abundances of reactions/enzymes were calculated (Text C in S1 Appendix) for each individual sample based on all observed KEGG Orthologs, for each hierarchical functional KEGG level. To determine the contribution of the candidate microbial taxa, i.e. those  differing between ADHD and controls, to differences observed in the selected monoamine precursors, the PICRUSt analysis was repeated on the subset of candidate taxa only to find the taxa responsible for the observed functional effects.Based on the generated 16S profiles of the gut microbiome for ADHD and control subjects for the full microbiome cohort, we predicted the presence of Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthologs and subsequent functional and metabolic pathways using PICRUSt  using a non-parametric Mann–Whitney U (MWU) test with Bonferroni correction for (i) multiple comparisons across all microbial taxa in all levels of phylogenetic classification , or (ii) across all pathways of the same KEGG hierarchical pathway level , unless stated otherwise. Relative abundance of taxa was correlated with relative abundance of enzymes/reactions (i.e. K numbers) using Spearman correlation. Subsequently, we repeated this analysis with multiple regression  to assess the unique contribution of taxa to the predicted reaction of interest, using the 16S-based taxa as predictors and the enzymes/reactions predicted to be present in those taxa (by PICRUSt), as dependent variables. For any additional downstream sequencing-related data analysis, figures, and statistics, Microsoft® Office Excel® 2007 and GraphPad Prism version 5.03 were used.Statistics on the relative abundances of selected monoamine biosynthetic pathways (K numbers: KEGG orthology groups) or taxa between sample groups was performed with SciPy (http://www.ebi.ac.uk/ena) under study accession number PRJEB11512 (or secondary accession number ERP012909) [The raw, unprocessed 16S 454-sequencing reads are publicly available for download at the European Nucleotide Archive (ENA) database (P012909) . The seqText B in S1 Appendix).In final analyses, we tested how microbiome function related to brain function. Across the whole sub-sample with both microbiome and fMRI measures available (n = 28), we assessed the effects of relative abundance of a candidate predicted microbiome-derived enzyme/reaction as covariate of interest on whole-brain reward anticipation responses (in SPM) with the reward anticipation (high > low) images .We also performed multiple linear regression with the ventral striatum ROI betas for reward anticipation (see above) as dependent variable and the microbiome measure as well as ADHD diagnosis, age, gender, and stimulant medication use as predictors (Table 1). No differences in age were present for patients and healthy persons in the fMRI samples, but for the microbiome analyses, controls were older (Table 1). This was due to the fact that the 39 BIG participants were older (33.1 years ± 17.7 SD). Controls from the NeuroIMAGE II study  did not differ from the cases (t(55) = -1.46, p = .15). Consequently, the functional (PICRUSt) analyses below were performed with and without the older BIG controls to balance power and homogeneity of the sample.The ADHD cases and healthy participants did not differ in BMI and gender in the three groups  , Actinobacteria (15.68%) and Bacteroidetes (6.05%) (S2 Table). We found an increase of Actinobacteria , which seemed to occur mainly at the expense of Firmicutes , as Bacteroidetes (and other phyla) did not differ significantly  in relative abundance between healthy participants and those with ADHD . Interestingly, in more taxonomic detail, within the phylum Actinobacteria, the genus Bifidobacterium was significantly increased in ADHD cases  . Bifidobacterium dominates the gut early in life, and slowly decreases in relative abundance during ageing [Bifidobacterium, we defined an age-matched subsample for our microbiome cohort (Text D in S1 Appendix). Using 15 pairs of ADHD cases and age-matched controls we find a similar rise in Bifidobacterium in ADHD-affected individuals (controls: 13.77% to ADHD: 18.90%) . Furthermore, the order Clostridiales, within the phylum Firmicutes, was found to be decreased in ADHD cases  and best explains the observed drop in Firmicutes, but no specific taxonomical entity belonging to the order Clostridiales was found to be responsible for this effect . In conclusion, the genus Bifidobacterium shows the most  strong and taxonomic most specific change as effect of ADHD status.For sample characteristics, diversity metrics and read counts, see g ageing . In ordeBifidobacterium (genus) and Eggerthella (genus).We selected five candidate taxa with the greatest difference between ADHD and controls to be used in our subsequent analyses of metabolic potential (by PICRUSt): Clostridiales (order), Rikenellaceae (family), Porphyromonadaceae (family), Fig 1).We predicted bacterial gene function using PICRUSt, focusing on pathways involved in the synthesis of phenylalanine, tyrosine, and tryptophan, which can serve as precursors of human dopamine, noradrenaline and serotonin  were predicted based on the total gut microbiome of healthy controls and ADHD cases. For these 17 a priori selected candidates, 15 K numbers could be identified in the microbiome (S3 Table). One predicted enzyme, cyclohexadienyl dehydratase , was found to be significantly more abundant  in the microbiome of ADHD cases  (S3 Table). Moreover, CDT ranked among the top ~1% of a total of 7545 reactions . Similar results of ADHD cases versus controls were obtained with the sample groups matched for age (i.e. without the older BIG controls), though significance is lost . Using logistic regression to control for age  the results in the age-matched sample (i.e. without the BIG controls) hint to the fact that the change in ADHD for K01713 is a significant effect (p = 0.024), but the marginal effect in the complete sample (p = 0.070) shows that our results cannot be completely disconnected from the age confounder present in the cohorts studied (Text D in S1 Appendix).Relative abundances of the 17 candidate reactions/enzymes (pathways) that are directly involved in the production of phenylalanine, tyrosine, or tryptophan  contributed most to the observed difference in the predicted enzyme CDT, we repeated the functional (PICRUSt) analysis for these candidate taxa only: Clostridiales (order), Rikenellaceae (family), Porphyromonadaceae (family), Bifidobacterium (genus) and Eggerthella (genus) . Differences in relative abundance of the genus Bifidobacterium uniquely contributed to the observed differences in the predicted phenylalanine pathway enzyme CDT in a multiple regression analysis (p < 0.001). The same conclusion was drawn from the fact that in the cohort Bifidobacterium contributed 99.9% of the predicted CDT (K01713) counts relative to the predicted CDT counts based on PICRUSt analysis of the entire microbiome .To assess which of the five taxa differing between cases and controls (S1 Appendix). Taking the anatomically-defined ventral striatal ROI, we indeed found decreased ventral striatal responses for reward anticipation in patients with ADHD versus controls (t(85) = 2.1, p = 0.038) . This difference was not significant in the sub-sample with microbiome data (t(26) = 0.2).In a partly overlapping sample of the same cohort, we tried to replicate reduced reward anticipation in ADHD versus controls see . Across Fig 5C). This effect was also significant in the ventral striatal ROI analysis , when including possible confounding factors  in the analysis. Also, when adding stimulant use (duration times dose) to control for long-term medication effects, predicted CDT relative abundance was still significantly associated with reward anticipation responses in ventral striatum .Finally, we assessed how the functional microbiome measure found to be different for ADHD relative to controls (i.e. predicted CDT), would relate to neural reward anticipation in the sub-sample with both microbiome and fMRI data. Across the whole sub-sample (n = 28), independent of diagnosis, we observed a negative association (whole-brain) of the relative abundance of predicted CDT with reward anticipation responses in bilateral ventral striatum (Bifidobacterium (within the phylum Actinobacteria)–also more abundant in (age-matched) ADHD versus controls (uncorrected for multiple comparisons)–appeared to be solely responsible for this predicted enzyme CDT increase. Across the sample (i.e. independent of diagnosis), the abundance of predicted CDT genes from the microbiome correlated negatively with bilateral ventral striatal BOLD responses for reward anticipation , typically reduced in ADHD [This is the first study investigating microbiome differences between ADHD cases and controls. Observed differences in taxa in this exploratory study were strongest for the phylum Actinobacteria, which was more abundant in cases, apparently at the expense of Firmicutes, for which abundance was lower in cases; uncorrected for multiple comparisons. In addition, we describe for the first time effects of a genetically encoded capacity for production of monoamine precursors in the gut microbiome of ADHD: the enzyme CDT, involved in the synthesis of a dopamine precursor , was predicted to be significantly more abundant in the microbiome of ADHD cases compared to healthy individuals (corrected for multiple comparisons). This effect appeared dependent on age, although significant effects were obtained with age-matched samples. The genus  in ADHD –7 and reBifidobacterium (e.g. Bifidobacterium longum) in 3 and 6 months old children (treated with either a Lactobacillus-based probiotic or a placebo) predicted ADHD or Asperger syndrome manifestation at age 13 years [Bifidobacterium, between ADHD and controls. This difference with our marginally significant result of increases in Bifidobacterium in ADHD might be explained by (i) our more sensitive method (16S marker gene sequencing versus PCR), and (ii) larger sample size (19 ADHD and 77 controls versus 6 ADHD/Asperger and 69 controls) or (iii) differences introduced by experimental procedures or between cohorts. The observed differences in Bifidobacterium genus between controls and adolescents/adults with ADHD (current study) or infants developing ADHD later in life [Bifidobacterium in early infancy versus increased abundance in early adulthood might reflect delayed gut microbiome maturation in ADHD, as Bifidobacterium is known to decline with older age [Bifidobacterium in infancy predicted ADHD and Asperger syndrome. Changes in microbiome composition have indeed consistently been found in autism spectrum disorder [Pärtty and colleagues  recently13 years . At the lder age . Pärtty disorder . AlthougBifidobacterium was responsible for increases in the predicted function of CDT (K01713)—involved in the synthesis of phenylalanine —in ADHD cases versus controls. Phenylalanine is an essential amino acid, which cannot be synthesized by humans and has to be absorbed from the gut[Fig 1). These neurotransmitters are highly affected in ADHD, nevertheless, the exact mechanisms of involvement are still ambiguous [m the gut. Phenylam the gut .mbiguous –37. Predmbiguous . Our finmbiguous . Howevermbiguous , 41 and mbiguous . Future Fig 1) positively or negatively (by inhibiting tyrosine hydroxylase) [Moreover, the mechanism by which the increased predicted CDT function  would result in decreased striatal BOLD responses for reward anticipation remains to be investigated. Many different routes for a potential causal relationship are possible. Microbial phenylalanine could be absorbed in the blood stream, cross the blood-brain barrier, and influence dopamine synthesis (oxylase) . Alternaoxylase) , 44, 45.oxylase) . Neverthacross subjects. ADHD status-specific conclusions about this gut-brain relation might be made in future studies with larger group sizes. Finally, given the generally observed beneficial effects of Bifidobacterium [Our study should be viewed in the context of its strengths and limitations. Obvious strengths include the 16S microbiome analysis (instead of preselected candidate taxa), larger sample size than previous studies in ADHD , and ouracterium , 48, it Bifidobacterium. This increase was associated with significantly enhanced predicted biosynthesis potential of a dopamine precursor in the gut microbiome of ADHD patients versus controls, which was linked to altered reward anticipation responses in the brain, a neural hallmark of ADHD. With this mechanistic approach, we hypothesize that presumed differences in dopamine precursor production at the gut microbiome level in ADHD might be related to dopamine disturbances at the neural level associated with reduced brain reward responses. This study highlights the importance of investigating the functional effects of microbiome differences in neuropsychiatric disorders.This is the first study demonstrating differences in the gut microbiome between patients with ADHD and healthy individuals, using a comprehensive 16S microbiome analysis, and showing–if anything–an increase in the genus S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 AppendixFile including Figures A-C  and Text A-F .(PDF)Click here for additional data file.


Secale cereale L. has been used worldwide as a source of genes for agronomic and resistance improvement. In this study, a stable wheat-rye substitution line and 3 primary 1RS.1BL translocation lines were selected from the progeny of the crossing of the Chinese local rye Aigan variety and wheat cultivar Mianyang11. The substitution and translocation lines were identified by molecular cytogenetic analysis. PCR results, fluorescence in situ hybridization and acid polyacrylamide gel electrophoresis indicated that there were a pair of 1R chromosomes in the substitution line which have been named RS1200-3, and a pair of 1RS.1BL translocation chromosomes in the other 3 translocation lines, which have been named RT1163-4, RT1217-1, and RT1249. When inoculated with stripe rust isolates, these 4 lines expressed high resistance to several Puccinia striiformis f. sp Tritici pathotypes that are virulent on Yr9. Moreover, the different response pattern of resistance among them suggested that the diversity of resistance genes for wheat stripe rust exists in the rye. These 4 lines also showed better agronomic performances than their wheat parent. The GS indices also showed the genetic diversity of the 1RS which derived from same rye variety. The present study indicates that rye cultivars may carry untapped variations that could potentially be used for wheat improvement. Triticum aestivum L.) is one of the most important crops in the world. The development of new wheat cultivars with higher yield and good resistance to diseases is the eternal goal of breeders. The alien substitutions or translocations of chromosomes between wheat and its relative species have played an important role in wheat improvement [Secale cereale [Hordeum californicum [Leymus mollis [Agropyron elongatum [Haynaldia villosa [Thinopyrum [Aegilops peregrina [Common wheat  is the most valuable relative genera for the improvement of wheat genetics [Lr26) [Sr31) [Yr9) [Pm8) [Rye  , stem ru) [Sr31) , stripe 9) [Pm8) . Therefo9) [Pm8) . However9) [Pm8) .Puccinia striiformis f. sp Tritici (Pst) is usually considered to be a devastating disease in cooler areas with higher latitudes and/or altitudes [Yr9 gene from Petkus rye has not provided protection against these pathogens, due to the prevalence of virulent pathotypes [Stripe rust, which was caused by ltitudes , 21. Sinthotypes . For morthotypes  put forwS. cereale L.) is a Chinese local rye variety which was collected from northwestern China. No specific permissions were required for Aigan rye in this study. The field studies did not involve endangered or protected species. The common wheat (Triticum aestivum L.) cultivar Mianyang 11 (MY11) contains the kr1 gene and can easily be crossed with rye. Seeds of MY11 used in the present study were produced by single spike descent across several generations to create pure genetic stocks. The F1 seedlings of MY11 x Aigan were soaked in 0.05% colchicine + 3% dimethyl sulfoxide for 8 h to produce the amphidiploid (C1). The details of C1 plant production was described by Ren [Aigan rye  assays and replace the genomic DNA of rye, as a probe, to discriminate rye chromosomes in wheat backgrounds [In situ hybridization was conducted according to Fu et al. [In this study, 5 probes were used. Three of them, Oligo-KUD15, Oligo-pSc200, and Oligo-pSc250, can be used for non-denaturing fluorescence kgrounds ,25,26,27Glu-B3 gene on 1BS [Sec-1 gene on 1RS [2, 200 μM dNTPs, 10 μmol primer, and 50 ng DNA in a total volume of 25 μL. After initial denaturation for 4 min at 94°C, each cycle included 30 s of denaturation at 94°C, 30 s of annealing at 60°C , and 1 min of extension at 72°C. A final extension for 5 min at 72°C followed the 30 cycles. The products of PCR amplification were separated on 1% agarose gel. Baesd on the results of SSR, genetic similarity (GS) indices of 1RS arm among 4 lines were calculated by the software NTSYS-PC (version 2.10e) [Total genomic DNA was isolated from young leaves by the surfactant cetyltrimethyl ammonium bromide (CTAB). Four sets of primer pairs were used to detect the present of 1R or 1B chromosomes. One primer pair was O11B3 (5’-ggtaccaacaacaacaaccc-3’) and O11B5 (5’-gttgctgctgaggttggttc-3’) from the e on 1BS . The sece on 1RS . The thie on 1RS . The foue on 1RS . 8 SSR me on 1RS  PCR was n 2.10e) .Electrophoretic detection of ω-secalin and gliadin proteins (A-PAGE) was conducted as described by Li et al.  with minPst pathotypes CYR31, CYR32, CYR33, and a new emerging isolate SY5, which is virulent in the field to many newly released wheat cultivars, were used to inoculate the wheat plants. The Pst pathotypes and isolates were provided by the Plant Protection Institute, Gansu Academy of Agricultural Sciences, China. Infection types (IT) are scored based on the 0–9 scale, as described by Wan et al. [The substitution and translocation lines and their parents were examined for resistance to stripe rust in the greenhouse as described . The Pstn et al. . IT 0–3 2. A 1-m length from the center row of each plot was cut at ground level before harvest to determine yield components [Plants were grown in Qionglai District at Chengdu Plains, China, in 2015 following standard cultivation practices and under irrigated conditions. Entries were arranged in a randomized, complete block design with three replications, in 3 m-long plots, each consisting of eight rows spaced 25 cm apart, at a plant density of 160 seedlings/mTo identify the chromosome construction of the progeny of 1R monosomic addition lines which were derived from the cross of MY11 and Aigan rye, FISH, PCR, and A-PAGE were used. Three new primary translocation lines and a substitution line were selected. Seeds used in this study were harvested from 3 generations. The results of FISH  showed tMolecular markers were also used for identify these wheat translocation/substitution lines. Primer pairs O11B3 and O11B5 are the specific primers of 1BS, and a 630 bp fragment would be amplified by them. Another primer pair, ω-sec-P1 and ω-sec-P2, are the specific primers of 1RS, and they can amplified a 1076 bp fragment. The PCR results showed that only wheat cultivar MY11 amplified a 630 bp band. Both the translocation lines and the substitution line can amplify a 1076 bp band but no 630 bp band was amplified .The primer PrCEN-2 can amplify a fragment of about 1000 bp of rye centromere repetitive sequence, and the results showed that all lines amplified a band with the expect size, and no product was amplified from the DNA of MY11. It was indicated that not only the substitution line, but also the translocation lines contained the full 1RS arm of rye .The primer TNAC1021 can amplify a specific fragment from the rye 1RL chromosome arm. This primer could distinguish the 1RL chromosome under the wheat genome background . The resThe expression of the genes at the Sec-1 locus in the 1RS chromosome arm was investigated by A-PAGE. All lines exhibited normal expression for the genes at the Sec-1 locus . It was Pst pathotypes, while Aigan rye was highly resistant (Yr9), were also highly susceptible to four Pst pathotypes (Wheat parent MY11 was highly susceptible to the 4 esistant . Wheat cthotypes . The trathotypes .Significant differences (P < 0.05) between the translocation/substitution lines and their wheat parent were observed. Compared with their wheat parent MY11, RT1163-4 showed significantly reduces of plant height (PH), and displayed significantly increased of spikelet number per spike (SLN), kernel number per spike (KN) and grain yield (GY); RT1249-3 showed significantly reduced of KN; RT1217-1 showed significantly increased of number of spikes per square meter (NS); The substitution line RS1200-3, showed significantly reduced of 1,000-kernel weight (TKW), kernel weight per spike (KW), harvest index (HI), PH and GY, but displayed significantly increased of SLN, KN and NS .8 SSR primers  were utiGS indices for the 4 1RS chromosomes of translocation/substitution lines were calculated based on the patterns of bands amplified with the 8 SSR primers. GS indices varied broadly from 0.4444 to 0.8056. The highest GS index (0.8056) among the 1RS chromosomes was observed between the tranlocation lines RT1217-1 and RT1163-4, whereas the lowest GS index (0.4444) was found between the translocation line RT1163-4 and RT1249, as well as the substitution line RS1200-3 . These rTranslocation and substitution lines are important materials in the study of genetics, physiology, and phytopathology, and also are important genetics resources for wheat breeding ,35,36,37in situ hybridization (GISH) is a powerful technique for visualizing alien chromatin in wheat-alien hybrids. However, GISH cannot distinguish which alien chromosomes were transferred to the wheat genome. In the present study, FISH, using oligonucleotide as probes, showed that a pair of rye 1R chromosomes or a pair of 1RS.1BL translocation chromosomes was transferred in the wheat genome. These oligonucleotide probes have provided an easier, faster, and more cost-effective method for the FISH analysis of wheat [Since large populations need to be screened to obtain translocations or substitutions, a more reliable and easier means of identifying the alien chromatin is needed. A-PAGE can identify the expression of the Sec-1 locus of chromosome 1R (1RS) . Althougof wheat . Moreoveof wheat ,40,41. Mof wheat ,40,42,43of wheat . In thisThe 1RS.1BL translocation from the Russian wheat cultivar Aurora or Kavkaz, in which the 1RS arm was derived from Petkus rye, has been the most widespread alien translocation in wheat breeding ,15,45,46Yr9, Pm8, Lr26, and Sr31, located on the 1R chromosome, have lost resistance against new respective pathogens [Since the 1980s, the resistant genes athogens . Therefoathogens ,39. The athogens ,15,31. IPst pathotypes. All 4 lines showed better resistance to the wheat cultivar CN11 with the gene Yr9  in these lines must be located on these 1R chromosomes. The interesting thing is that, although the 1R (1RS) chromosome of these 4 lines came from the same rye variety Aigan, they showed different responses to inoculated Pst pathotypes in the greenhouse. The rye chromatins of these 4 lines were coming from the same rye variety. But they did not come from the same pollen. It suggested that there is high sense of genetic diversity in rye varieties, even in a same rye population [In the present study, 3 new primary 1RS.1BL translocation lines and a new 1R (1B) substitution line, whose 1R chromosomes were derived from same Chinese rye variety Aigan, exhibited different phenotypes for resistance to infection by 4 gene Yr9 . Becausepulation . In the pulation ,15,31, aThe 4 1RS chromosomes which were derived from Aigan rye showed highly genetic diversity . Rye is The relative effects of the 1R chromosome on agronomic characters were determined in the previous studies ,31,48. T


Bartonella spp. are erythrocytic bacteria transmitted via arthropod vectors, which infect a broad range of vertebrate hosts, including humans. We investigated transmission dynamics and host-parasite-vector relationships for potentially zoonotic Bartonella spp. in invasive Rattus rattus hosts and associated arthropod ectoparasites in Madagascar. We identified five distinct species of Bartonella  infecting R. rattus rodents and their ectoparasites. We fit standard epidemiological models to species-specific age-prevalence data for the four Bartonella spp. with sufficient data, thus quantifying age-structured force of infection. Known zoonotic agents, B. elizabethae 1 and 2, were best described by models exhibiting high forces of infection in early age class individuals and allowing for recovery from infection, while B. phoceensis 1 and B. rattimassiliensis 1 were best fit by models of lifelong infection without recovery and substantially lower forces of infection. Nested sequences of B. elizabethae 1 and 2 were recovered from rodent hosts and their Synopsyllus fonquerniei and Xenopsylla cheopsis fleas, with a particularly high prevalence in the outdoor-dwelling, highland-endemic S. fonquerniei. These findings expand on force of infection analyses to elucidate the ecological niche of the zoonotic Bartonella elizabethae complex in Madagascar, hinting at a potential vector role for S. fonquerniei. Our analyses underscore the uniqueness of such ecologies for Bartonella species, which pose a variable range of potential zoonotic threats. Bartonella spp. have been documented infecting a wide range of mammals  can maintain transmission in human hosts  is an exception in causing widespread and life-threatening hemolytic anemia in its primary host  are associated with rodent reservoirs , suggestive of immune clearance mechanisms , the ‘spillover force of infection,’ or the force of infection from animals to humans, is a product of its constituent factors: infection dynamics within the reservoir animal population, animal-human contact rates, and the probability of viable human infection given such a contact . Vector ecology has been largely understudied in Bartonella systems  model the age-prevalence of specific rodent-borne Bartonella genotypes to develop our understanding of the influence of age-structured force of infection on their transmission, and (3) construct a Bartonella spp. phylogeny from both rodent hosts and associated ectoparasites to elucidate possible vector relationships in our system.Previous work has reported man lice  and noveat flies  in Madagdagascar , the rec22.1R. rattus were trapped and lethally sampled in two different regions in central Madagascar: in peridomestic communities surrounding Ambohitantely Special Reserve, District of Ankazobe, in July 2013 and in both forest and human communities surrounding Ranomafana National Park in June 2014  and in strict accordance with guidelines posted by the American Veterinary Medical Association. All field protocols employed were pre-approved by the Princeton University Institutional Animal Care and Use Committee , and every effort was made to minimize discomfort to animals.All field studies were carried out under permit authorization from Madagascar National Parks and the Madagascar Ministry for Water and Forests  gene, which is optimal for tissue studies , Fort Collins, CO. Briefly, DNA was extracted using Qiagen QIAamp tissue kits  according to the manufacturer’s instructions, and DNA extractions were examined for  studies .R. rattus in the District of Ankazobe were processed at the University at Buffalo , following the same procedures outlined for host tissues. DNA was extracted from a subset of samples, representing at least of one of each arthropod type recovered from an individual rat host . Extractions were amplified for Bartonella spp. DNA by conventional PCR targeting the nuoG gene. Ectoparasite voucher specimens were slide-mounted and identified using available taxonomic keys.Ectoparasite samples collected from nuoG sequences from both rodents and arthropod ectoparasites were clustered to the putative species level by comparing similarity with Bartonella spp. sequences previously deposited in GenBank (Table S1), including species identified in rats of the genus Rattus. Further details of our molecular processing, including primer sequences, are delineated in Text S1.The obtained 2.4Bartonella highlighted in our molecular analyses, allowing for age-specific variation in the force of infection and accounting for deviations across sampling sites; and (2) Focusing on the subset of our dataset for which ectoparasite samples were available, we statistically described the distribution and prevalence patterns of arthropod ectoparasites of R. rattus rodents in Madagascar, highlighting phylogenetic relationships across the host-parasite-vector continuum that could underpin observed patterns of infection in the host community. We here summarize our data analysis procedures in brief; the full details of our methodology are elucidated in Text S2.All quantitative analysis was conducted in R v. 3.1 for MacIntosh . Analyses followed two major themes: (1) Building on techniques from epidemiology, we first modeled age-prevalence for each of the four main species of rodent-borne 2.4.1R. rattus is virtually impossible; thus, we instead estimated age from the rodents’ weights using the von Bertalanffy equation (W(t) = −k(t−t0))W = terature , though ia order . We calcia order .Bartonella spp. and quantify the age-specific force of infection , or the per capita rate at which susceptible hosts become infected. In the case of a non-immunizing persistent infection, hosts are born susceptible, and the total time of infection exposure increases with age, resulting in higher prevalence in later age classes because individuals of a given age (a) will have experienced a cumulative FOI throughout their lifetime. Such data will be best fit by a Susceptible-Infected (SI) model. Similar methods can be applied to estimate FOI for transient, fully immunizing infections from age-seroprevalence data; in such cases, seroprevalence will also increase with age, and corresponding data (not available in this study) can be fit by a Susceptible-Infected-Recovered (SIR) model. By contrast, non-immunizing, transient infections should demonstrate an age-prevalence profile that first increases in early age classes, reaches a peak, then declines to some lower endemic prevalence in later age classes. Older individuals will have had greater opportunity to progress through the compartments of a corresponding Susceptible-Infected-Susceptible (SIS) model, resulting in the return of many late life individuals to the susceptible class.Using these estimates for rodent age, we followed Bartonella spp. in our age-prevalence dataset. We employed partial profile likelihood to compute confidence intervals on each age-specific FOI estimate in the final, best fit model (We used likelihood ratio tests (LRTs) to separately select amongst SI and SIS models of varying numbers of age bins for each of the four it model . We compThis framework makes several assumptions: that rodents experience no heterogeneity in infection exposure, that the sensitivity of our PCR assay is perfect, and, in the case of the SI models, that infection is lifelong and infection-induced mortality negligible. Because our data are derived from two cross-sectional sampling events during the same month of two different years, we also ignore the possibility of seasonal variations in the age-specific force of infection.2.4.2Bartonella spp. genotypes recovered from rats and their arthropods. Phylogenetic alignment was performed using Brucella abortus as an outgroup and the default parameters in MUSCLE (Focusing on the subset of our data for which ectoparasite information was available (District of Ankazobe), we next sought to understand host-parasite-vector relationships that might elucidate the force of infection trends outlined above. Using representative nuoG sequences obtained from both host and ectoparasite PCR assays , we constructed a maximum likelihood phylogeny highlighting nested relationships between n MUSCLE . We usedn MUSCLE  and evaln MUSCLE .Bartonella spp. infection in the host rat predicted infection in the associated arthropod ectoparasite. We chose this model form over the more intuitive inverse (i.e. vector predicting infection in host) due to uncertainty over the extent to which we completely sampled the entire vector community on a given host. Because we may have missed collection of some arthropods on some hosts, we cannot accurately assess the extent to which ectoparasite infections predict host infections. We are confident in the validity of our host sampling and PCR-assay , and therefore, chose to instead evaluate host infection status as a predictor for infection status in arthropods. Because B. phoceensis 1 and B. rattimassiliensis 1 were each recovered from one ectoparasite only, we restricted our analysis to B. elizabethae 1 and 2 only. Using the glmer function in the lme4 package in R  and infection status in the rat host , with a random effect on rat identity to allow for recovery of multiple arthropods from the same rat host.We next used logistic regression to quantify the extent to which age in R , we relaBartonella spp. infections within each host-ectoparasite pair (i.e. separately for each ectoparasite type) was greater than expected under the null hypothesis that the Bartonella spp. infections of a host and its ectoparasite are independent (details in Text S4). We computed a one-tailed p-value as the proportion of permutated datasets that yielded a proportion of concordance more extreme than the corresponding statistic as calculated in the unpermuted, raw data.We also quantified host-ectoparasite interactions using a permutation test to examine whether the proportion of concordant Bartonella species ranges might be limited by habitat restrictions in the vector population, with a focus on the arthropods for which sufficient data were available: the three fleas . We employed logistic regression to compare the occurrence of each flea species in turn against the presence of the other two flea species on the same rat host, as well as sampling site .Finally, we explored the question of whether 3R. rattus rats were sampled in this study, 93 of which (58.9%) were positive for some species of Bartonella, which we identified as belonging to five distinct genotypes : B. elizabethae 1 , B. elizabethae 2 , B. phoceensis 1 , B. rattimassiliensis 1 , B. tribocorum 1 . Because only one positive sample was recovered for B. tribocorum 1, the remainder of our quantiative analyses report results for the first four species of rodent-borne Bartonella only.A total of 158 3.1Bartonella spp. genotypes, and Text S2 describes the analytical process in detail, including relevant R-code. Text S3 and Table S3 provide a detailed analysis of the sensitivity of our results to variations in the age-for-weight estimates and the duration of each age bin.We depict rodent age estimates from each of four sampling sites in B. elizabethae 1 and 2 genotypes, while an SI model with constant FOI best represented the B. phoceensis 1 data. Like B. phoceensis 1, the B. rattimassiliensis 1 data were also best described by an SI model (i.e. persistent infection), though inclusion of age structure (three classes) here significantly improved the model’s ability to recover patterns in the data. Models for B. elizabethae 1 and 2 demonstrated substantially higher estimates for FOI in the juvenile age class (15–30 day) than in both the infant (0–15 days) and post-juvenile (30+) classes, while the B. rattimassiliensis 1 model exhibited its highest FOI in the infant class. For these three Bartonella species, young rodents appear to represent the age cohort supporting the majority of transmission, while B. phoceensis 1-infected rats experience a constant FOI across a lifespan , these models were fit with only the subset of data from sites for which the pathogen was present. For this reason, we present the results for both model forms. It is possible that our more restrictive models  violate the assumption of no heterogeneity in infection exposure if certain sampling sites are outside the habitat range for the parasite or the vector; in this case, our summary models likely under-predict the true FOI for B. elizabethae 1 and 2. If this assumption is not violated, then it is also possible that our more relaxed models allowing for site-specific deviations in λ over-predict the true FOI for B. elizabethae 1 and 2 because data from low prevalence sites (zeroes in our sampling) have been excluded. More extensive field sampling of both rat hosts and ectoparasite vectors will be critical to resolving this uncertainty in the future.To explore the possibility of geographic range limits for our various  the FOI ; Fig. 2.3.2Bartonella spp. isolates recovered from nuoG sequencing of both host and arthropod ectoparasite samples. B. elizabethae 1 and 2 sequences recovered from S. fonquerniei and X. cheopsis fleas and B. phoceensis 1 and B. rattimassiliensis 1 sequences recovered from Polyplax spp. lice nest with the same respective genotypes recovered from rodent hosts. That said, the ecological landscape of these interactions was complex and revealed considerable discordance between sequences obtained from several host-ectoparasite pairs, a pattern which has been reported in host-ectoparasite relationships for Bartonella spp. elsewhere (Bartonella spp. for S. fonquerniei occurred more frequently than would be expected by chance (p = 0.007), suggestive of transmission between host-ectoparasite pairs. All cases of rat-S. fonquerniei concordancy were for Bartonella elizabethae genotypes (6/8 for B. elizabethae 1 and 2/8 for B. elizabethae 2). By contrast, no host-ectoparasite concordance was observed for X. cheopsis (p = 1).lsewhere . Indeed,S. fonquerniei fleas recovered from B. elizabethae 1 or B. elizabethae 2-positive rats were PCR- positive for the same Bartonella spp. identified in the rodent host. By contrast, none of the S. fonquerniei fleas recovered from B. phoceensis 1-positive rats were PCR-positive for the Bartonella spp. recovered from their hosts; notably, all of those fleas were PCR-positive for different Bartonella spp. than their host rat (most with B. elizabethae 1 and one with B. elizabethae 2). No X. cheopsis recovered from B. phoceensis 1-positive rats were PCR-positive for the host rat Bartonella spp., but one tested positive for B. elizabethae 1. No fleas of any species recovered from B. rattimassiliensis 1 positive-rats were positive for any sequence of Bartonella.Polyplax spp. lice from a B. phoceensis 1 positive rat and a B. rattimassiliensis 1 positive rat. Both lice tested positive for the Bartonella genotype with which their host rats were infected. Table S6 reports raw prevalence of each Bartonella spp. genotype recovered from the arthropod ectoparasites assayed in our study. In addition to those arthropods already highlighted, we also sequenced several E. gallinacea fleas, Haemaphysalis spp. ticks, and mesostigmatid mites; of these, only one mite tested positive for one Bartonella genotype (B. elizabethae 1), suggesting a relatively minor role for these additional arthropods in transmission of the four Bartonella genotypes explored in this analysis.Though not the focus of our sampling, we opportunistically collected two S. fonquerniei was significantly, negatively correlated with infestation with both X. cheopsis and E. gallinacea; in fact, no individual rats were co-infected with S. fonquerniei and X. cheopsis, though E. gallinacea co-infested with both other flea types  infecting R. rattus rats and/or associated arthropod ectoparasites in two disparate ecosystems of central Madagascar. We first explored the transmission dynamics of each of four Bartonella species for which there were sufficient data; age-structured SIS models best recovered patterns in the age-prevalence data exhibited by rats infected with B. elizabethae 1 and 2, while SI models provided a better fit to the data for B. phoceensis 1 and B. rattimassiliensis 1. We then investigated host-parasite-vector relationships in our data, recovering nested sequences of all four Bartonella spp. in rat hosts and associated ectoparasites, with B. elizabethae 1 and 2 found in S. fonquerniei and X. cheopsis fleas and B. phoceensis 1 and B. rattimassiliensis 1 in Polyplax spp. lice. These patterns are suggestive of a role for these arthropods in the transmission of respective Bartonella spp. In particular, concordant pairings of B. elizabethae 1 and 2 genotypes in host-ectoparasite pairs occurred more frequently than expected by chance for S. fonquerniei fleas, underlining the likely importance of these arthropods in the transmission dynamics of the B. elizabethae complex. We demonstrated significant habitat restrictions on the range of S. fonquerniei, which could limit the range of the B. elizabethae, a known zoonotic agent, in Madagascar.We here report the first account of Bartonella species demonstrated higher forces of infection than genotypes not known to pose zoonotic risks. Indeed, B. elizabethae 1 and 2 in juvenile rats demonstrated forces of infection some ten orders of magnitude higher than anything recovered for B. rattimassiliensis 1 or B. phoceensis 1, thus offering a potential avenue for zoonotic transmission. Additionally, higher forces of infection are generally associated with more virulent, highly replicating pathogens, likely to cause pathogen-induced mortality and/or place greater evolutionary pressure on hosts to develop resistance; such theories are also consistent with our finding that a transmission model incorporating infection recovery (indicative of immune defense) provided the best fit to the B. elizabethae 1 and 2 data. Intriguingly, our SIS model for B. elizabethae 1 and 2 did not completely recapitulate the data trend of prevalence declining to zero in highest age class rodents , suggesting that a Susceptible-Infected-Recovered (SIR) model form, by which pathogen clearance is immunizing, or an SI model incorporating pathogen-induced mortality might be more appropriate. Careful genotype-specific reporting of age-structured seroprevalence patterns against particular Bartonella spp. antigens would enable these inferences; previous attempts to investigate the serological landscape of rat-borne Bartonella spp. have yielded conflicting results  to infection . Though  results .B. elizabethae 1 and 2 than our cumulative estimates in models not allowing for location-specific deviations because sites in which the pathogen was not detected were excluded while fitting the deviation-permitting model. We are left with the question of whether our cumulative model under-predicts the force of infection for these Bartonella spp. by including susceptible hosts outside the habitat range of the pathogen or whether our site-deviation model over-predicts the force of infection by excluding data from low prevalence sites.Our site-specific estimates for age-specific FOI were also substantially higher for S. fonquerniei, and, to a lesser extent, X. cheopsis, in the transmission of B. elizabethae 1 and 2 among Malagasy R. rattus rats. The ranges of vector-borne parasites are limited by the ranges of their hosts and vectors, and ectoparasite infestations in R. rattus examined in our study showed distinctive signatures of habitat limitations and mutual exclusion. S. fonquerniei was recovered predominantly from outside sampling sites and never in concert with X. cheopsis, which was found exclusively on rats trapped inside households. If S. fonquerniei plays an essential role in the transmission cycle of B. elizabethae, then we might expect rats recovered from indoor sampling sites to experience a lower force of infection for B. elizabethae genotypes than rats trapped outside; broadly, this is the pattern exhibited in our data. Previous work demonstrates that S. fonquerniei is limited in geographic extent to outdoor localities above an 800 m elevation gradient in Madagascar, a factor believed to be important in predicting the range limits for another zoonotic bacterial pathogen for which S. fonquerniei serves as a vector: Yersinia pestis, the causative agent in bubonic plague.Our subsequent descriptive analyses build on our FOI analyses to offer evidence for the first hypothesis, supporting the role of S. fonquerniei. It is possible that S. fonquerniei fleas and the pathogens which they support are consequently less abundant in this locale. Future studies including more categorically lowland sites will be critical to evaluating whether B. elizabethae 1 and 2 can be independently maintained among R. rattus rodent hosts and X. cheopsis arthropods in Madagascar, or whether, as seems to be the case for plague, these Bartonella subtypes require the contributions of S. fonquerniei to their transmission cycle for persistence. Additionally, if future serological work confirms that a model incorporating parasite-induced mortality better recapitulates the dynamics of B. elizabethae infections in rodent hosts, then a strategy of independent pathogen maintenance in arthropod vectors (i.e. S. fonquerniei) may be necessary to explain Bartonella spp. persistence. Recent work posits a role for Bartonella spp. as intracellular endosymbionts of their arthropod vectors (similar to Wolbachia spp.)  of the highland distribution (800 m-restricted) of ia spp.) ; the divB. phoceensis 1 and B. rattimassiliensis 1 in Polyplax spp. lice and associated rodents, suggestive of a role for lice in the transmission of these other Bartonella species. This interaction is inconclusive due to sampling size; however, B. phoceensis and B. rattimassiliensis sequences have been recovered from louse ectoparasites of sampled rodents before    and coul’s life) , and wei’s life) .Bartonella spp. for each positive rodent host and arthropod vector. Mixed infections of multiple Bartonella spp. in the same host and/or vector have been reported in the past  remains a question for further study.Finally, we report the results of PCR amplification and sequence alignment of one genotype of the past , and it 5Bartonella spp. infections in Rattus spp. hosts and associated arthropod ectoparasites in Madagascar. We distinguish between the infection dynamics of the B. elizabethae complex, a known zoonotic pathogen, which appears to maintain a transient presence in its rodent hosts, and those exhibited by B. phoceensis and B. rattimassiliensis, which appear to operate as persistent infections. Many ecological studies have reported data from diverse Bartonella species as a single composite Bartonella spp. in the past (e.g. Our analyses shed light on the transmission dynamics and genotype-specific host-parasite-vector associations for ast e.g. . While sThe authors declare that they have no competing interests.CEB, APD, CJEM, MYK, and KD devised the study. CEB, EOY, and HCR collected field samples. YB and MYK performed molecular analyses of rodent tissue samples. HS and KD performed molecular analyses and species identifications of arthropod ectoparasites. CEB, CJM, EOY, and APD carried out the statistical analyses of the data. CEB, CJEM, APD, MYK, and KD wrote the manuscript, which all other authors read and edited.


Bartonella quintana and typhus group rickettsiae in body lice from homeless persons in Bogotá, Colombia. We found B. quintana–infected body lice and seroprevalence of this microorganism in 19% of homeless persons and typhus group rickettsiae in 56%. Public health professionals should start preemptive measures and active vector control.In 2015, we investigated  Bartonella, Rickettsia, and Borrelia are of great importance; louseborne B. quintana is the main microorganism associated with infections in homeless persons , in lice and related human infections in Bogotá was evident only during 1918–1922 and 1941  approved this study . All participants read, accepted, and signed the informed consent form.Pediculus humanus humanus, according to standard taxonomic keys  and considered these lice a positive indication of body louse infestation. Of the study participants, 132 were men, 17 were women, and 4 were transgender . Participants’ mean age was 39.6 (SD ± 11.65) years. Eighteen (11.7%) were infested with body lice; lice were preliminarily identified as mic keys .Bartonella spp. (B. quintana antigen) and TGR (R. typhi antigen) in serum samples, we used commercially sourced indirect fluorescent antibody (IFA) kits . We screened serum at a dilution of 1:64. Among the 153 study participants, 29 (19.0%) had IgG that reacted exclusively against Bartonella spp. at a titer of >64, and 86 (56.2%) had IgG that reacted exclusively against TGR at a titer of >64. Twenty (13.1%) participants had IgG against both Bartonella spp. and TGR.To detect specific IgG to Bartonella spp. and Rickettsia spp. from louse samples, we organized 201 body lice into 39 pools and extracted DNA from each pool . We screened all louse pools by standard PCR for Bartonella spp. (citrate synthase gene [gltA] and 16S–23S rRNA intergenic transcribed spacer region [ITS-1]) and Rickettsia spp.  as described  (For identification of Y605045) , panel AP. h. humanus captured in this study, we tested DNA samples from 2 randomly selected lice and PCR amplified the cytb genes as reported (cytb sequences obtained in our study with known head and body louse sequences from the 5 P. h. humanus clades (A–E)  , panel BP. h. humanus) infected with B. quintana and exposure to TGR in homeless persons in Bogotá. The rate of body louse infestation in the studied population (11.7%) was within the range reported elsewhere (7%–30%) (Bartonella spp. (19.0%) in line with the range reported in other studies worldwide (0.4%–62%)   than with murine typhus (R. typhi infection)  was considerably higher than levels reported in previous studies (≈0.54%–22%)  (Bartonella and Rickettsia infections in homeless persons in Bogotá, confirm circulation of specific species of these microorganisms, and screen for Bartonella spp. endocarditis by blood culture among homeless persons who have high antibody titers.Our study is subject to several limitations. The detection of


Shared genetic influences between attention-deficit/hyperactivity disorder (ADHD) symptoms and autism spectrum disorder (ASD) symptoms have been reported. Cross-trait genetic relationships are, however, subject to dynamic changes during development. We investigated the continuity of genetic overlap between ASD and ADHD symptoms in a general population sample during childhood and adolescence. We also studied uni- and cross-dimensional trait-disorder links with respect to genetic ADHD and ASD risk.N ≤ 5551, Social and Communication Disorders Checklist, SCDC) and combined hyperactive-impulsive/inattentive ADHD symptoms  were repeatedly measured in a UK birth cohort . Genome-wide summary statistics on clinical ASD  and ADHD  were available from the Psychiatric Genomics Consortium. Genetic trait variances and genetic overlap between phenotypes were estimated using genome-wide data.Social-communication difficulties  as those between repeated measures of the same trait . Shared genetic influences between traits, especially during later adolescence, may implicate variants in K-RAS signalling upregulated genes (p-meta = 6.4 × 10−4).In the general population, genetic influences for SCDC and SDQ-ADHD scores were shared throughout development. Genetic correlations across traits reached a similar strength and magnitude (cross-trait R2 = 0.084%). An age-specific genetic overlap between clinical ASD and social-communication difficulties during childhood was also shown, as per previous reports. Cross-dimensionally, however, neither SCDC nor SDQ-ADHD scores were linked to genetic risk for disorder.Uni-dimensionally, each population-based trait mapped to the expected behavioural continuum: risk-increasing alleles for clinical ADHD were persistently associated with SDQ-ADHD scores throughout development  contains supplementary material, which is available to authorized users. An aetiological link between attention-deficit/hyperactivity disorder (ADHD) symptoms and autism spectrum disorder (ASD) symptoms has been supported by family , 2 and tAlthough twin and family studies suggest that ADHD and ASD symptoms are co-heritable , 9, studBeyond the concept of dichotomous entities, common disorders such as clinical ASD and ADHD can be understood as extreme values on one or more continuous underlying scales of liability , due to The aim of this work is to provide insight into the genetic overlap across ADHD- and ASD-related dimensions during the course of child and adolescent development. For this, we investigate a phenotypically rich longitudinal population-based cohort from the UK, the Avon Longitudinal Study of Parents and Children (ALSPAC), as well as summary statistics from the largest publicly available clinical ADHD and ASD samples collected by the Psychiatric Genomics Consortium (PGC) , 33. GivPopulation-based analyses were performed in ALSPAC children, a UK population-based longitudinal pregnancy-ascertained birth cohort (estimated birth date: 1991 to 1992) . Please The Strengths and Difficulties Questionnaire (SDQ)  is a behQuantitative social-communication difficulties in ALSPAC participants were measured with the 12-item Social and Communication Disorders Checklist  . The SCDρ = 0.46 to 0.70; Pearson r = 0.46 to 0.69; SCDC, Additional file ρ = 0.39 to 0.57; Pearson r = 0.38 to 0.61), as previously reported for the SCDC . Pev2.r644)  in 1000 v2.r644) ).The publically available results from an internationally collaborative mega-analysis of clinical ADHD, conducted by the Psychiatric Genomics Consortium (PGC), were utilised in this study. This sample consisted of 4163 cases and 12,040 controls/pseudo-controls of predominantly European descent , 33. ADHA genome-wide scan of 5305 ASD cases and their parents (PGC-ASD), all of European ancestry (2015 freeze), has been completed by the PGC. Cases obtained an ASD diagnosis using research standard diagnoses and expert clinical consensus diagnoses at the age of 3 years or above. More than 90% of all patients had also a diagnosis of autism from the Autism Diagnostic Interview-Revised  and/or tThere is no sample overlap between population-based and clinical samples Table .g), as tagged by common SNPs on a genotyping chip. Analyses were carried out with Genome-wide Complex Trait Analysis (GCTA) software [g are equivalent to estimates of SNP-h2, as the phenotypic variance has been standardised to one. Genetic relationship matrices (GRMs) were constructed using directly genotyped SNPs, excluding individuals with a pairwise relationship >0.025 [Longitudinally assessed SCDC scores and SDQ-ADHD scores were studied using genetic-relationship-matrix restricted maximum likelihood (GREML) to estimate the proportion of phenotypic variation due to genetic factors  that capture the extent to which two phenotypes share genetic factors (ranging between −1 and 1), as tagged by common variation. In addition, we report genetic covariances (Covg). These estimates represent, for rank-transformed traits [re) between measures. Within-trait analyses for SCDC scores correspond to previously published findings [Bivariate GREML  was carrd traits , the pard traits . Geneticfindings  and are p values. In addition, we studied the presence of age-specific and trait-specific changes in explained genetic variance using a random effects meta-regression approach (R:metafor), accounting for two interrelated traits  and assuming multivariate normality. In addition to a random intercept, the model included fixed effects for age at assessment and fixed effects for trait (SCDC scores coded as zero and ADHD-SDQ scores coded as one). Beta coefficients measure here the increase in variance per one year as well as the difference in explained variance between the two traits. A variance/covariance matrix across the nine measures was approximated analogous to models accounting for correlated phylogenetic histories [We used the molecular signatures database (MSigDB) hallmark gene set collection  to disseistories  includinp value thresholds (0.001 < PT ≤ 1), PGS for ASD (based on PGC-ASD) and ADHD (based on PGC-ADHD) were constructed in ALSPAC  according to current guidelines [R2. Longitudinal analyses were carried out using a mixed effects regression modelling framework (R:lme4). Repeatedly measured untransformed SDQ scores were regressed on PGS including fixed effects for PGS, sex and age at assessment, and random slopes (for age) and intercepts. Beta coefficients for PGS quantify here the increase in trait scores for each increase in one standard deviation of PGS. Differences in sample dropout across time were accounted for through bootstrapping, and we generated parametric 95% bootstrap confidence intervals (95% CIBootstrap with NBootstrap = 500). In the presence of association, we estimated the marginal regression R2 [2 = 0.17; PGC-ADHD: prevalence = 0.05, liability-scale SNP-h2 = 0.28).Polygenic risk scores (PGS) , 58 wereidelines  using PLidelines  softwaresion R2   = 0.19(0.07), p = 0.002; age 13: GREML-Varg(SE) = 0.18(0.07), p = 0.003; Fig. g(SE) = 0.24(0.07), p = 7.0 × 10−5; age 11: GREML-Varg(SE) = 0.16(0.07), p = 0.005; age 17: GREML-Varg(SE) = 0.45(0.09), p = 3.0 × 10−9; Fig. g(SE) = 0.08(0.07), p = 0.10), similar to previously reported findings on the SCDC [Within ALSPAC, the strongest evidence for genetic effects contributing to SDQ-ADHD scores, as captured by common variants, was identified during late childhood and early adolescence  = 1.00(0.26), p = 3 × 10−4 versus SDQ-ADHD age10_SDQ-ADHDage12: GREML-rg(SE) = 0.94(0.13), p = 7 × 10−4). The respective genetic covariances were nearly identical (SCDCage11_SDQ-ADHDage13: GREML-Covg(SE) = 0.17(0.05) versus SDQ-ADHDage10_SDQ-ADHDage12: GREML-Covg(SE) = 0.17(0.06)). The relative contribution of genetic to phenotypic covariance  may, hog (SE) = 0.19(0.07), p = 0.002; Fig. Genetic correlations between SDQ-ADHD and SCDC scores were, however, developmentally sensitive and dropped for cross-trait genetic links involving SDQ-ADHD scores during very early adolescence (~12 years of age), despite cross-trait phenotypic correlations Fig. . Note thGiven the evidence for persistent genetic cross-trait links throughout development, we investigated the hypothesis that phenotypic variation in both traits is influenced by similar biological pathways. As multivariate approaches to annotate the genetic covariance between phenotypes are currently not available yet, we dissected the genetic variance of SCDC scores and SDQ-ADHD scores  according to MSigDB hallmark gene sets and then combined the evidence.pmeta = 0.000012; pmeta (pathway-adjusted) = 6.1 × 10−4; including all repeatedly assessed measures). The largest variance contributions were found in later adolescence, where K-RAS signalling upregulated genes explained nearly 3% in phenotypic variation in SDQ-ADHD scores and over 6% in SCDC scores . Using a random effects meta-regression approach showed that the genetic variance accounted for by the K-RAS signalling upregulated gene set increases with progressing age (βAge(SE) = 0.0029(0.0013), p = 0.023), with little support for trait-specific effects (βTrait(SE) = −0.013(0.0093), p = 0.16) or trait-specific developmental changes (βTrait × Age (SE) = -0.001(0.002), p = 0.67). There was also little evidence for the contribution of other pathways to shared phenotypic variation across traits, beyond chance  signalling upregulated genes accounted for genetic variance in both traits  = 0.067 , p = 0.0043) with little evidence for developmental changes in effect (βADHD-PGS × age (95% CIBootstrap) = 0.0008; p = 0.80). The marginal adjusted R2 in SDQ-ADHD scores due to ADHD-PGS at PT = 0.5 was 0.084%, adjusted for age and sex, and the corresponding genetic covariance was estimated as 0.10  . An age-specific genetic link between clinical ASD and SCDC scores at age 8 (but not later during development) has been previously reported [reported , 41.Cross-dimensional trait-disorder overlap: We found little support that alleles more common in ASD cases than in controls/pseudo-controls are associated with variation in SDQ-ADHD scores, including both age-specific  = −0.0034 , p = 0.32).βADHD-PGS_age14(SE) = 0.028(0.01), p = 0.049). Longitudinal analyses of untransformed scores provided, however, little evidence for a consistent effect (for example at a PGS threshold PT = 0.5: βADHD-PGS (95% CIBootstrap) = 0.019 , p = 0.26) or for a change in polygenic effect over time (βADHD-PGS × age(95% CIBootstrap) = 0.002 .There was also little evidence that alleles more common in ADHD cases than in controls/pseudo-controls are robustly related to variation in SCDC scores at 8, 11, 14 or 17 years . This may refer to potential differences between traits, disorders and different types of genetic markers. The variance contributions explained by K-RAS signalling upregulated genes increased for both, SDQ-ADHD and SCDC scores, during development and were largest during later adolescence with age-dependent developmental but no trait-dependent changes. Thus, our results do not necessarily imply that developmentally shared genetic influences between ADHD symptoms and social-communication difficulties are genetically stable but that aetiological mechanisms in both traits are developmentally coupled. This clearly underlines the need to investigate genetic trait variances jointly as part of multivariate analysis approaches, once these methods become computationally feasible.Joint genetic influences implicated in both ADHD symptoms and social-communication difficulties may include variation within K-RAS upregulated genes  explainiRELN  or are iLN [GRM3 , 65. A fLN [GRM3 , 65 . In light of aetiological differences between subclinical variation in population-based symptoms and severe neurodevelopmental conditions, it is thus likely that a considerable proportion of genetic factors contributing to shared genetic links between social-communication difficulties and ADHD symptoms on the general population level will be non-specific to either disorder. It is furthermore conceivable that genetic links between comorbid ADHD and ASD symptoms are domain dependent. For example, inattentive symptoms tend to be more persistent than hyperactive-impulsive problems, as the latter tend to resolve with progressing age , possiblA limitation of our study is that we cannot fully exclude the possibility of transformation-related bias with respect to the studied population-based traits. However, genetic links between ADHD symptoms and clinical ADHD and between SCDC scores and clinical ASD, as previously reported , were coIn the general population, genetic aetiologies between social-communication difficulties and ADHD symptoms are linked throughout childhood and adolescence and may implicate similar biological pathways that are developmentally coupled. Risk-increasing alleles for disorder can account, however, only for a very small proportion of trait variance, even for symptoms mapping to the same behavioural dimension, suggesting that much larger clinical samples are required to reliably detect cross-dimensional trait-disorder relationships.Additional file 1:Table S1. Descriptives of SDQ-ADHD and SCDC scores in ALSPAC. Table S2. Phenotypic correlations of SDQ-ADHD scores in ALSPAC. Table S3. Phenotypic correlations of SCDC scores in ALSPAC. Table S4. Univariate GREML of SDQ-ADHD scores in ALSPAC. Table S5. Univariate GREML of SCDC scores in ALSPAC. Table S6. Bivariate GREML of SDQ-ADHD scores in ALSPAC. Table S7. Bivariate GREML of SCDC scores in ALSPAC. Table S8. Bivariate GREML and Pearson correlations of SDQ-ADHD and SCDC scores in ALSPAC. Table S10. Association between ADHD polygenic scores and SDQ-ADHD scores in ALSPAC. Table S11. Association between ASD polygenic scores and SDQ-ADHD scores in ALSPAC. Table S12. Association between ADHD polygenic scores and SCDC scores in ALSPAC. (DOCX 92 kb)Additional note. Selection of SDQ-ADHD measures. Additional note. Meta-analysis of correlated test statistics from pathway analysis. Additional note. Additional references. Additional note. Web resources. Additional file 2: Table S9.Pathway-based dissection of additive genetic variance in SDQ-ADHD and SCDC scores according to 50 molecular signatures database hallmark gene set collections. (XLSX 20 kb)