To generate new mechanistic hypotheses on the pathogenesis and disease progression of neuroHIV and identify novel therapeutic targets to improve neuropsychological function in people with HIV, we investigated host genes and pathway dysregulations associated with brain HIV RNA load in gene expression profiles of the frontal cortex, basal ganglia, and white matter of HIV+ patients. Pathway analyses showed that host genes correlated with HIV expression in all three brain regions were predominantly related to inflammation, neurodegeneration, and bioenergetics. HIV RNA load directly correlated particularly with inflammation genesets representative of cytokine signaling, and this was more prominent in white matter and the basal ganglia. Increases in interferon signaling were correlated with high brain HIV RNA load in the basal ganglia and the white matter although not in the frontal cortex. Brain HIV RNA load was inversely correlated with genesets that are indicative of neuronal and synaptic genes, particularly in the cortex, indicative of synaptic injury and neurodegeneration. Brain HIV RNA load was inversely correlated with genesets that are representative of oxidative phosphorylation, electron transfer, and the tricarboxylic acid cycle in all three brain regions. Mitochondrial dysfunction has been implicated in the toxicity of some antiretrovirals, and these results indicate that mitochondrial dysfunction is also associated with productive HIV infection. Genes and pathways correlated with brain HIV RNA load suggest potential therapeutic targets to ameliorate neuropsychological functioning in people living with HIV. HIV RNA load in the central nervous system (CNS) tends to be associated with a range of neurological manifestations18. Individuals with neurological symptoms but sustained serum HIV suppression often have significant cerebrospinal fluid (CSF) HIV RNA loads17. Asymptomatic CSF virus escape has also been documented by lumbar punctures19. Combination antiretroviral therapy decreases brain viral load, improves immunohistochemical markers of neuronal injury in primates20, and is associated with a reduction of neurodegeneration in humans21. The low CNS bioavailability of antiretrovirals  has been implicated in higher CSF HIV-1 RNA load, together with the accumulation of resistance mutations in the CNS22. Neurologically symptomatic CSF escape has been linked to drug resistance mutations24.HIV persists in the brain despite combination antiretroviral therapy (cART)25. Relatively few transcriptomic studies of HIV have been conducted in human samples, although they have provided considerable insights into pathogenesis, disease progression, latency, and reactivation30.Despite the advent of genome-wide Omics strategies, including transcriptomic, proteomic, and metabolomic investigations, their implementation in studies of the pathogenesis of neuroHIV and the identification of druggable targets remain somewhat limited28. We used gene set enrichment analysis (GSEA) for pathway analysis31 in conjunction with genesets from the Molecular Signatures Database (MSigDb), including canonical pathways in the C2 collection, including Kyoto Encyclopedia of Genes and Genomes (KEGG)33. This approach aids interpretations of genome-wide expression profiles by revealing common biological processes that are dysregulated in pathological conditions. Transcriptional analyses showed that multiple molecular systems correlated with brain HIV RNA load. Genesets that correlated with brain HIV RNA load and were concordantly dysregulated in the three brain regions studied could mostly be grouped into three broad biological processes: greater inflammation, increased transcriptional evidence of neurodegeneration, and bioenergetics dysfunction. Transcriptional evidence of neurodegeneration and bioenergetics dysfunction were also correlated with HIV RNA levels in gene expression profiles of the prefrontal cortex in a rodent model of HIV. Results also indicate that HIV expression is itself associated with mitochondrial dysfunction and that impairments in brain energetics are a central aspect of the progression and severity of neuroHIV.The present study sought to identify correlations between host gene expression patterns and brain HIV RNA loads in gene expression profiles of cases in the National NeuroAIDS Tissue Consortium (NNTC), consisting of samples from three brain regions: white matter, the basal ganglia, and the frontal cortex26. Briefly, CEL files that were retrieved from GEO (Accession No. GSE35864) were analyzed using the GC Robust Multi-array Average (GCRMA) package available from Bioconductor (http://www.bioconductor.org). Normalization was implemented with the GCRMA procedure with the option fast set to FALSE in order to correctly retrieve gene–gene correlation34. RNA load was determined by PCR of HIV gag/pol by NNTC in each sample.The gene expression dataset consisted of Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays of the frontal cortex (Brodmann area 9), basal ganglia (head of the caudate nucleus), and white matter  and was conducted by NNTC (GEO Accession No. GSE35864). A summary of the patients' clinical and demographic aspects is in Supplemental Table 31. GSEA was used in conjunction with genesets from the Molecular Signatures Database (MSigDb), including canonical pathways in the C2 collection35. GSEA uses the Kolmogorov–Smirnov statistical test to assess whether a predefined geneset, here a pathway from the C2 collection, is statistically enriched in differentially expressed genes by testing their distribution in the full list of genes ranked by their differential expression between two biological states31. The MSigDB is a comprehensive database of genesets for performing geneset enrichment analysis that represents a wide range of biological processes and diseases35. A total of 1322 canonical Pearson pathways from the MSigDB C2 collection were scored and ranked using the GSEA algorithm as described also in12 using R. Multiple testing adjustment was performed and significance was assessed using the False Discovery Rate (FDR) in GSEA algorithm.For pathway analysis, we used Gene Set Enrichment Analysis (GSEA), a computational method to assess whether a priori defined sets of genes show statistically significant differences between biological statesp values between the expression levels of host genes and HIV RNA load. HIV transcription was used as a single value to estimate the overall virus expression level. Adjustments of p values were performed using the Benjamini–Hochberg FDR and significantly correlated host genes with HIV transcripts were selected by applying an appropriate threshold . Next, we separated the samples into two groups based on the mean expression of HIV transcripts to create groups with graded levels of HIV transcription . Differential expression between groups was computed with Limma and used as an input for GSEA to identify pathways that were differentially regulated between samples with high and low HIV expression . Pathways that were tested were extracted from the MSigDb C2 canonical pathway collection. We selected pathways that were significantly associated with brain HIV load using the GSEA FDR < 0.05.We first computed Pearson correlations and associated 36. Male HIV Tg rats, weighing between 350 and 400 g, were housed two per cage on a reverse 12 h/12 h light/dark cycle (lights off at 8:00 AM) in a temperature (20–22°C) and humidity (45–55%) controlled vivarium with ad libitum access to tap water and food pellets . All of the procedures were conducted in strict adherence to the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee of The Scripps Research Institute and ARRIVE guidelines (http://www.nc3rs.org.uk/page.asp?id=1357). A total of 18 gene expression profiles from prefrontal cortices of HIV Tg rats profiled by RNA-Seq as previously described36 were separated into two groups based on the mean expression of brain HIV transcripts to create groups with graded levels of HIV transcription (n = 7 for high brain HIV RNA load and n = 10 for low brain HIV RNA load). Differential expression between groups was computed with DESeq2 and used as an input for the GSEA to identify pathways that were differentially regulated between samples with high and low HIV expression. A normalized enrichment score and p-value were calculated for each pathway.HIV transgenic (Tg) rats on a mixed Wistar-Fischer background, which ensures variability of host genes and HIV expression levels, were used for the studyValidation of gene expression was carried out by quantitative real-time PCR (RT-PCR) analysis in an independent set of human frontal cortex samples with either high or low HIV brain RNA loads as determined by RT-PCR (n = 5) also obtained from the NNTC. RT-PCR experiments were performed on the CFX Real-Time PCR Detection System . Total RNA was reverse transcribed using the iScript cDNA Synthesis kit (Bio-Rad) with 100 ng of total RNA. Amplification was performed on a cDNA amount equivalent to 1.25 ng total RNA. Oligonucleotide primers were designed using the PrimerQuest online tool . Calculations of the relative abundance of the genes under study were done by the comparative cycle threshold (Ct) method and expressed as 2-exp(ΔCt) using an empirically determined Ct of 40 as the detection threshold.26. Pearson correlations and associated p values were computed between the expression levels of host genes and HIV transcripts for each brain region as outlined in the Methods section. Genes with a high degree of correlation with brain HIV RNA loads in each of the three brain regions studied are shown in Fig. Gene expression profiles from three brain regions of HIV-infected patients, including the basal ganglia, white matter, and the frontal cortex, were used for the study. The gene expression data were filtered and normalized as described previously31, to compare gene expression profiles associated with high brain HIV RNA loads. GSEA was used in conjunction with genesets from the MSigDb, including canonical pathways in the C2 collection32. In total, we identified 254 pathways in the basal ganglia, 104 pathways in white matter, and 393 pathways in the frontal cortex that were significantly enriched/decreased in samples with high brain HIV load (FDR < 0.05).We applied GSEA, a computational method that assesses whether a priori-defined genesets show statistically significant differences between biological statesThe top differentially regulated pathways in the three brain regions  ranked by the normalized enrichment score (NES) are shown in Fig. d-glycerate 1,3-bisphosphate, and the mitochondrial membrane ATP synthase subunit C locus 3 (ATP5G3), Supplemental Table Pathways that are representative of bioenergetics, including pathways that are related to oxidative phosphorylation, electron transfer, and the tricarboxylic acid (TCA) cycle, were inversely correlated in all three brain regions studied Fig. . TranscrThe differential expression of genes involved in energy metabolism and the TCA cycle was confirmed in an independent set of frontal cortices from individuals with high and low brain HIV RNA loads by quantitative real time PCR (RT-PCR), Fig. Pathway dysregulations in each of the three brain regions studied are reviewed next..The pathways that showed the highest correlation with brain HIV load in the basal ganglia included pathways indicative of inflammation and immune system activation, including interferon type I and II and cytokine signaling, Toll-like receptor signaling, tumor necrosis factor-α signaling, JAK-STAT signaling, and interferon Figs.  and 4. ITranscriptional evidence of immune activation and inflammation in white matter that were positively correlated with brain HIV RNA load included pathways indicative of increases in cytokines, interferons, and chemokines, including increases in JAK STAT and nuclear factor κB (NFκB) signaling, interferon signaling, and inflammasome activation Figs. . TranscrPathways reflecting changes in the expression of neuronal genes, including axonal and synaptic transcripts, trophic signaling, and mitochondrial and other key genes that are associated with neurodegenerative conditions were dysregulated in the frontal cortex Figs.  and 8. L39. These rats also have similar brain gene expression changes as humans with HIV38. The repertoire of pathway dysregulations in humans with HIV in the present study was generally consistent with observations in HIV transgenic rats38. As depicted in Fig. 36, the expression of genesets representative of neurodegeneration and bioenergetics, although not inflammation-related genesets, was significantly correlated with brain HIV RNA loads in the frontal cortex in HIV transgenic rats, despite a lower HIV RNA dynamic range  and microtubule-associated protein-2 (MAP2)73. In one study, amyloid-β plaques in HIV+ brains, although immunohistologically different from those in symptomatic AD brains, were associated with HAND among apolipoprotein E (APOE) ε4 carriers, suggesting the possibility of convergent mechanisms of pathogenesis61. In vitro studies indicate that HIV proteins can disrupt different steps of amyloid pathways76. The HIV protein Tat, which is produced by the HIV reservoir in the brain even in the setting of the suppression of viral replication77, has been shown to bind amyloid-β peptide, promoting amyloid aggregation and neurotoxicity78. When Tat was injected in the brains of amyloid precursor protein and presenilin-1 transgenic mice (APP-PS1), it colocalized with APP78. Furthermore, crossing Tat transgenic mice with APP-PS1 transgenic mice resulted in an increase in amyloid-β deposition, neurodegeneration, neuronal apoptotic signaling, and phosphorylated Tau compared with PSAPP mice79, indicating that Tat likely contributes to AD-like pathology in HIV. As the HIV-positive population ages, individuals with both HIV and canonical AD pathology are beginning to be diagnosed. A recent case report of AD in an HIV-infected patient who was positive for amyloid on positron emission tomography80 was followed by approximately 20 more cases81. Thus, current issues of debate include the possibility of HAND/AD mixed dementia and the hypothesis that HIV may represent a predisposing factor for AD83. Supporting the latter possibility, combined CSF biomarker risk for AD in an Australian HIV-positive cohort was found to be more than 10-times greater than in age-matched controls82.Emerging neuropathology concepts in neuroHIV also suggest a degree of pathogenic similarity and possibly overlap between neuroHIV and AD. Several studies reported increases in amyloid deposition in older HIV-infected individuals, suggesting that long-term HIV and antiretroviral therapy might interfere with the clearance of proteins, such as amyloid-β peptide, and worsen neuronal damage and cognitive impairment in this population86 and PD87), aging88, and neuroHIV, particularly in adverse ageing in older people who live with HIV89. Frontal cortices from patients with HAND showed mitochondrial abnormalities, including a reduction of mitochondrial biogenesis90. The accumulation of mutations and deletions of mitochondrial DNA (mtDNA)92 altered mitochondrial fission and fusion93. Experimental evidence shows that HIV products, such as Tat and gp120, can cause mitochondrial damage100. Mitochondrial dysfunction is also implicated in the toxicity of some antiretrovirals101. The present data indicate that mitochondrial dysfunction is a key component of the neuropathogenesis of HIV infection itself.The present data also show that the gene expression evidence of impairments in bioenergetics are associated with brain HIV RNA load and are correlated with transcriptional evidence of inflammation and neurodegeneration. Mitochondrial dysfunction has been implicated in neurodegenerative disease  but not in neurons105. The present pathway analysis underscores the potential contribution of disruptions of the TCA cycle and β-oxidation in the mitochondrial matrix, in which oxidative phosphorylation depends on the supply of reducing equivalents from the end-oxidation of nutrients106. The TCA cycle and oxidative phosphorylation are tightly coupled. The TCA cycle produces the reducing equivalents NADH and FADH2, which are required for electron transfer through the mitochondrial respiratory chain or electron transport chain (ETC). The oxidation of NADH and FADH2 in complexes I and II of the ETC is required to maintain the function of the TCA cycle106. As electrons are transported through the complexes, the ETC produces a mitochondrial membrane potential that generates ATP. Mitochondrial complexes I and II in the ETC replenish NAD+ and FAD, respectively, allowing the oxidative TCA cycle to function106. Thus, lower expression of TCA cycle-related genes may play a crucial role in impairments in mitochondrial oxidative phosphorylation in neuroHIV. Tricarboxylic acid cycle metabolites are also important for the biosynthesis of nucleotides, lipids, and proteins106. Mounting evidence indicates that metabolites in the TCA cycle are also involved in controlling chromatin modifications, DNA methylation, and post-translational modifications of proteins106. Acetyl-coenzyme A levels regulate chromatin dynamics by providing acetyl groups for the acetylation of histones by histone acetyltransferases110. The TCA cycle predominates in neurons, whereas glycolysis predominates in astrocytes111. Thus, decreases in TCA cycle-related gene expression in neuroHIV may be a correlate of lower neuronal energy metabolism. Evidence of alterations of TCA cycle capacity and expression that are induced by HIV infection in the scientific literature is limited. Interestingly, CSF metabolomics showed that worsening cognitive status in HIV-infected patients is associated with the accumulation of citrate and acetate, which are suggestive of disruptions of TCA cycle function112. A separate CSF metabolomic analysis of young individuals with HIV who were on ART found a dysregulation of TCA cycle intermediates, such as succinate and malate, among other metabolites, differed when compared with HIV-negative controls113. Relevant to the present results, we recently found that escalated (dependent) methamphetamine self-administration in HIV transgenic rats was associated with the lower expression of TCA cycle-related genes in conjunction with transcriptional evidence of increases in inflammation and neurodegeneration36.Emphasis has been rightfully placed on impairments in oxidative phosphorylation in neuroHIV and degenerative diseases, such as ADSelected genes that encode enzymes that are involved in glycolysis, such as ENO1 and GAPDH, were concordantly dysregulated in all three brain regions that were studied herein, and the expression of genesets that are representative of glycolysis was reduced particularly in the basal ganglia and frontal cortex, thus indicating that alterations of glycolysis and reductions of the TCA cycle and electron transport are key bioenergetic alterations that are correlated with brain HIV RNA load.In conclusion, we explored functional pathways that are associated with brain HIV load as a correlate of neuroHIV disease severity and progression by the pathway analyses of transcriptional profiles from brain regions of humans with HIV. We found that brain HIV RNA load correlated with transcriptional evidence of an increase in inflammation. Evidence of increased cytokine signaling was associated with high HIV RNA across the three brain regions studied. Increases in interferon signaling were correlated with high brain HIV RNA load in the basal ganglia and white matter although not in the frontal cortex. We also found transcriptional evidence of neurodegeneration that involved pathways that reflect changes in axonal and synaptic genes and trophic signaling, among others. Lastly, we found transcriptional evidence of impairments in bioenergetics that involved oxidative phosphorylation and the TCA cycle across all three brain regions. Transcriptional evidence of mitochondrial dysfunction was also associated with brain HIV RNA load and correlated with transcriptional evidence of inflammation and neurodegeneration, indicating that mitochondrial dysfunction is a key component of the neuropathogenesis of HIV infection itself.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.


Stroke is among the most common causes of death and disability worldwide. Despite the relevance of stroke-related disease burden, which is constantly increasing due to the demographic change in industrialized countries with an ageing population and consecutively an increase in age-associated diseases, there is sparse evidence concerning acute stroke treatment and treatment-related outcome in the elderly patient group. This retrospective study aimed at analysing patient characteristics, therapy-related complications and functional outcome in stroke patients aged 90 years or older who underwent acute stroke treatment .We identified files of all inpatient stays at the Department of Neurology at Saarland University Medical Center (tertiary care level with a comprehensive stroke unit) between June 2011 and December 2018 and filtered for subjects aged 90 years or older at the time of admission. We reviewed patient files for demographic data, symptoms upon admission, (main) diagnoses, comorbidities, and administered therapies. For patients admitted due to acute stroke we reviewed files for therapy-related complications and functional outcome. We compared the modified Rankin scale (mRS) scores upon admission and at discharge for these patients.n = 22, mechanical thrombectomy n = 26, intravenous thrombolysis followed by mechanical thrombectomy n = 16) and showed a significant improvement in their functional status as measured by change in mRS score  with 7 (10.9%) observed potentially therapy-related complications . One intravenous thrombolysis was stopped because of an uncontrollable hypertensive crisis. Patients who did not qualify for these treatments (including those declining acute treatment) did not show a change of their functional status between admission and discharge (p 0.064).We identified 566 inpatient stays of subjects aged 90 years or older. Three hundred sixty-seven of the 566 patients (64.8%) were admitted and discharged due to symptoms indicative of stroke. Two hundred eleven patients received a diagnosis of ischaemic stroke. These 211 patients were analysed subsequently. Sixty-four patients qualified for acute stroke treatment (intravenous thrombolysis Our data indicate that acute stroke treatment is effective and safe in the oldest old. Age alone is no criterion to withhold an acute intervention even in oldest old stroke patients.The online version contains supplementary material available at 10.1186/s12877-021-02566-3. Owing to the demographic change in industrialized countries, the health-related and socioeconomic burden of stroke is constantly increasing . While tTaking into consideration that physiological age-related changes make elderly subjects respond differently to therapeutic interventions, evidence gained in younger patient groups with acute stroke cannot directly be transferred to geriatric patients.This paucity of evidence consecutively leads to a lack of acute stroke treatment recommendations regarding geriatric patients and potentially exposes these patients to suboptimal care.The primary goal of the present study was therefore analysing the clinical characteristics, administered therapeutic interventions, therapy-related complications and the functional outcome of the oldest old patients (aged 90 years and older) admitted due to acute stroke to our department, a tertiary care medical center with a comprehensive stroke unit. Moreover, we also aimed at identifying factors preventing these patients from receiving a potentially disability-reducing treatment.This study was approved by the local IRB . The registration number of the study is 82/19. Due to the retrospective study design and the use of only anonymised data, the need for informed consent was waived by the IRB. The protocol was performed in accordance with the relevant local guidelines and regulations, including regulations on data protection. Anonymised data were retrieved from patients’ electronic medical files.In a first step, we identified all emergency admissions to the Department of Neurology of the Saarland University Medical Center between June 2011 and December 2018 that resulted in an in-patient hospital stay. Hereafter, we selected all patients above the age of 90 and classified cases based on the discharge diagnosis as either admissions due to acute stroke or other conditions. For non-stroke cases, basic demographic data and diagnoses were analysed. Patients with acute stroke were grouped into one of the three following categories: a) patients with intracranial haemorrhage, b) patients diagnosed at discharge with a transient ischaemic attack (TIA) and c) patients with a diagnosis of ischaemic stroke. For the latter group, we checked whether or not an acute therapeutic intervention (i.e. intravenous thrombolysis with recombinant tissue plasminogen activator (rtPA), mechanical thrombectomy or both) had been performed  within each of the two groups, we used the McNemar test. A functional status was defined as good for a mRS score of ≤2 and as poor if the mRS was > 2.Statistics were carried out using IBM SPSS statistics version 25.0 . The significance threshold was set at a We identified 566 cases aged 90 years or older treated during the investigated 7½-year period. The median age in the total cohort was 91 years (range 90 to 102 years), median duration of inpatient stay was 5 days (range 1 to 40 days), 71% patients (399 of 566) were female.One hundred ninety-nine cases were referred to our department due to conditions other than stroke. Categories for main diagnoses of these patients are listed in Additional Table Most patients aged 90 years or older  were referred to our department due to symptoms indicating acute stroke. These patients were analyzed subsequently. The main focus of this study was on patients with ischaemic stroke. Demographic and clinical data of these patients are summarised in Additional Table Thirty-six of 367 (9.8%) stroke patients showed cerebral haemorrhage on the computed tomography-scan (CT). In 30 of the 36 cases (83.3%) hemorrhage was intracerebral, 3 cases (8.3%) showed subarachnoidal, and 2 cases (5.6%) subdural haemorrhage. One patient showed haemorrhage in more than one space.p < 0.001) and higher mRS scores upon admission (p < 0.001) as shown in Table One hundred twenty patients 32.7%) of all patients admitted due to acute stroke received a diagnosis of transient ischaemic attack (TIA). The remaining 211 patients (57.5%) with persistent stroke symptoms and/or acute lesions in cerebral imaging were categorized as either receiving an acute therapeutic intervention for stroke (i.e. intravenous thrombolysis with recombinant tissue plasminogen activator (10.4%), mechanical thrombectomy (12.3%) or both (7.6%) or not receiving such an intervention. Demographic and clinical data of these two patient groups with acute stroke are summarised in Table .7% of alOf the 211 cases with ischemic stroke, 88 vessel occlusions were confirmed by CT-angiography at the time of admission.n = 27) was the most common location of vessel occlusion, followed by the M2-segment (n = 25) and the internal carotid artery . The most frequent reason for not proceeding with a mechanical recanalisation was a prolonged time window (symptoms onset to admission): 20 patients have already showed signs of ischaemia on CT scans, 7 patients were admitted with an unknown time of symptoms onset.The M1-segment of the middle cerebral artery .Sixty-four 30.3%) of the 211 patients admitted with stroke symptoms underwent acute stroke treatment. The most frequent reasons for withholding an acute therapeutic intervention were: signs of manifest ischaemia on CT scan (27.5%), unknown or prolonged time since onset of symptoms (24.7%), spontaneous improvement of symptoms at the time of admission (4.7%) and anticoagulant therapy (6.6%). Clinical data of these subgroups are demonstrated in Table 0.3% of tThirty-eight patients received intravenous thrombolysis with rtPA. Subsequently, 16 of these 38 patients also underwent an attempt for mechanical thrombectomy due to large vessel occlusion. Two of these 16 patients with intravenous thrombolysis and a subsequent attempt for mechanical thrombectomy showed a spontaneous recanalisation without the need for mechanical thrombus extraction. 26 patients underwent an attempt for mechanical recanalisation without preceding intravenous thrombolysis with rtPA therapy. One of these 26 patients showed a spontaneous recanalisation without the need for mechanical thrombus extraction. With regard to the effectiveness of the mechanical thrombectomy, 29 cases of the 42 cases (i.e. 83.3%) who underwent an attempt for mechanical thrombectomy reached a TICI score of TICI 2b  or TICI 3 (complete reperfusion). Intervention-associated data of patients receiving acute stroke treatment are summarized in Table Secondary intracranial haemorrhage, as a potential complication of an acute stroke intervention, was diagnosed using CT scan imaging, routinely performed 24 h after the acute stroke intervention and at any time during the inpatient stay if the patient developed new neurological symptoms. Secondary intracranial haemorrhage was observed in 2 patients who underwent intravenous thrombolysis (5.3%), in 2 patients of the patients that underwent mechanical thrombectomy (4.8%) and in 2 patients without any acute intervention (1.4%).Extracranial bleeding  was observed in 1 patient after intravenous thrombolysis (2.6%). Two patients (4.8%) showed a clinically relevant drop in haemoglobin  after mechanical thrombectomy. Arterial dissection as a potential complication of mechanical thrombectomy was not observed in our cohort. Eight of the 64 patients (12.5%) who received an acute stroke intervention and 16 of the 147 patients (10.9%) who did not receive an acute stroke intervention died during the inpatient stay. There was no significant difference in intra-hospital mortality within the acute stroke treatment group (i.e. between patients receiving thrombectomy and patients receiving intravenous thrombolysis p 0.734) and no significant difference between the cohort receiving an acute stroke treatment and the cohort not receiving acute stroke treatment (p 0.661).To investigate whether our cohort of elderly patients enjoyed an improvement in their functional status  after acute stroke treatment, we compared individual mRS scores upon admission to mRS scores at discharge. A good functional status was defined as a mRS score of ≤2, a poor functional status was defined as a mRS score > 2. Patients receiving an acute stroke treatment  showed a significant improvement in their functional status . For patients who did not qualify for or who declined such treatment, there was no significant change with regard to their functional status  have been intensively investigated. However, geriatric patients, especially nonagenarians, are underrepresented or even excluded in most relevant studies , 15. OnlDue to the retrospective character of this study, all procedures  were uncontrolled. Yet, the same highly standardized operating procedures (SOPs)  applied We observed a significant difference in the way of admission between patients receiving an acute therapeutic intervention and those not receiving such a therapy. This difference was particularly pronounced for admissions by an emergency response physician (76.6% versus 3.4%). The more severe symptoms (in terms of higher mRS and NIHSS score) in the group that later received an acute therapeutic intervention might have triggered the admission by an emergency response physician.Concerning mechanical thrombectomy, successful recanalisation rates (defined as a mTICI score 2b or 3) after large vessel occlusion were described in 71%  in a metA meta-analysis of five randomized trials investigating mechanical thrombectomy reported a 90-day-mortality of 15.3% in patients with a median age of 68 years . Yet, moIntravenous thrombolysis for acute stroke has previously been reported being associated with a significant favourable functional outcome . Intracrn = 610, data not shown) in the investigated 7½-year-period. For the patients aged 90 years or older, only 23.7% had a door-to-needle time of less than 30 min, while 44.1% of all inpatients had a door-to-needle time of less than 30 min (data not shown). This difference might be partially explained by difficulties in obtaining medical history and establishing an intravenous line in this particular patient cohort. Yet, the lower percentage of geriatric patients with an optimal door-to-needle time indicates that there is still room for improvement concerning acute medical care for elderly stroke patients. We observed a similar difference for the door-to-groin time between patients aged 90 years or older (9.5% had a door-to-groin time of less than 1 h) and all inpatients that underwent mechanical thrombectomy .Our data endorse the safety and efficacy of acute therapeutic interventions in stroke patients aged 90 years and older. Calendar age per se does not justify withholding these potent therapy options for the oldest old. As the time window between symptom onset and application of acute stroke treatment determines outcome, the time-is-brain-concept needs to be kept in mind when treating our oldest citizens, too. Indeed, the most frequent reason for not qualifying for an acute stroke intervention in our cohort was a protracted time window between onset of symptoms and admission to our hospital (52.2%). We compared the door-to-needle time between the reported 38 patients aged 90 years or older who received intravenous thrombolysis and all inpatients treated with intravenous thrombolysis for stroke in our hospital  the interventions were not controlled for and therapeutic decisions might have been biased by the treating physicians’ individual judgement; b) all analyses rely on the documented diagnoses at discharge; c) a selection bias concerning the admissions to our hospital  cannot be excluded and d) there is a lack of (90 day) follow-up data concerning functional outcome and mortality. With regard to the latter aspect, our data only reflect short-term outcome and mortality . Nevertheless, our data can be considered as an approximation concerning overall safety and effectiveness.Our results show a significant improvement in the functional status (measured as change in mRS at the time of admission to mRS at discharge) in our geriatric cohort receiving intravenous thrombolysis, mechanical thrombectomy or both. The rates of treatment-related complications were comparable to those reported for younger age groups , 25–27. Additional file 1. Additional Table 1a Categories of main diagnoses for patients admitted due to conditions other than stroke. Additional Table 1b Demographic and clinical data of all patients admitted due to acute stroke.


Cardiac metastases in hepatocellular carcinoma patients are infrequently encountered and usually associated with a very poor prognosis.Hereby, we report a case of an acute pulmonary embolism (PE) on top of HCC with direct cardiac invasion to the right atrium (RA) through the inferior vena cava with another metastasis to the right ventricular apex in the form of highly mobile cauliflower mass protruding through the tricuspid valve into RA and nearly obliterating right ventricular outflow tract in a multi-centric hepatocellular carcinoma patient.Acute dyspnea in a patient with a long history of hepatitis C virus infection raises the suspicion of acute PE due to either hypercoagulable state induced by malignancy or by cardiac extension of the tumor which usually carries high mortality rates. To the best of our knowledge, this case is the first case in the literature to show cardiac metastases in HCC with two different pathological mechanisms.The online version contains supplementary material available at 10.1186/s43044-021-00193-2. Cardiac complications of hepatocellular carcinoma (HCC) are quite rare. Right atrial invasion with right ventricular outflow obstruction and Budd Chiari syndrome was previously reported while cardiac metastases in HCC patients are rarely encountered and mostly are associated with high mortality.2 saturation of 92% on 2 L nasal cannula. There was a loud S2 on cardiac auscultation, but the rest of his physical exam was unremarkable. Electrocardiogram (ECG) revealed sinus tachycardia and s1q3t3 pattern. Urgent transthoracic echocardiography (TTE) was done revealing a large solid mass extending through the inferior vena cava (IVC) to the right atrium (RA) with another highly mobile cauliflower mass at the right ventricular (RV) apex occupying the RV cavity, protruding into RA through TV and nearly obliterating RV outflow tract into the pulmonary artery  for 20 years yet did not receive any treatment. He was admitted to our medical facility for resolved hepatic encephalopathy, decompensated liver failure, and acute renal failure due to hepatorenal syndrome type I. Suddenly while admitted, he started to complain of acute onset of acute chest pain, severe dyspnea, and tachycardia. On clinical examination, he was distressed with a deep icteric tinge, tachypneic with a thready pulse, massive ascites, and bilateral lower limb pitting edema. His vital signs showed a heart rate of 120 bpm, blood pressure of 90/60, temperature of 36.5 °C, respiratory rate of 30, OInvolvement of the heart in HCC is rarely encountered and usually develops in advanced stages of HCC . PreviouTo the best of our knowledge, this case is the first case in the literature to demonstrate cardiac metastases to several cardiac chambers, to RA by direct extension through IVC as well as the RV mostly by hematogenous spread.Acute dyspnea in a patient with HCC raises the suspicion of acute PE induced by either a hypercoagulable state in malignancy or by tumor thrombus through malignant cardiac extension.Additional file 1: Video 1. Transthoracic Echocardiography revealing a large solid mass extending through IVC to RA with another highly mobile cauliflower mass at the RV apex occupying The RV cavity, protruding into RA through TV and nearly obliterating RVOT into the pulmonary artery.


We hypothesise that peripheral IFN stimulation results in a brain inflammatory response via pathways of neuroimmune communication which in turn results in sickness-behaviour and depressive phenotype. We aim to determine if peripheral IFN stimulation results in brain inflammatory response including upregulation of inflammatory cytokines and chemokines.There is increasing interest in the role of dysregulated immune function and inflammation in the pathogenesis of psychiatric disorders including mood disorders and dementias. Immune mechanisms offer a new approach to investigating mechanism in addition to offering hope for new avenues of treatment.Interferon (IFN) therapy in humans is known to be associated with a significant risk of developing depression, both during therapy and increasing risk of relapse in the years following exposure, yet the mechanism remains unclear. IFN stimulation in animal models may offer insights into this phenomenon, in addition to furthering our understanding the role of immune mechanisms in the development of psychiatric phenotypes.Mice (n. 42) were exposed to either IFN-alpha, IFN-gamma or vehicle control using either osmotic pump or intraperitoneal injection over the course of 7 days. Mice were scarificed, brains were dissected and RNA extracted. Inflammatory gene transcription within the brain was determined using real time quantitative polymerase chain reaction (RTqPCR). Absolute quantification was achieved using standard curves and reference gene. Statistical significance was determined using Mann-Whitney or ANOVA/Kruskal-Wallis depending on normality of data and number of groups.IFNγ stimulation is associated with a significant brain upregulation of a number of inflammatory cytokines and chemokines including Il1β, Tnfα, Il10, Ifnγ, Ccl2, Ccl5, Ccl19, Cxcl10 and Ccr5. However, unexpectedly we did not find IFNα stimulation to associate with brain inflammatory transcriptional changes.This work demonstrates a brain inflammatory response to peripheral IFNγ stimulation. The inflammatory profile, including upregulated chemokines, suggests that recruitment of leukocytes across the blood brain barrier may be part of the immune response. Further experiments using existing tissues will explore if there are structural/cellular changes within the brain parenchyma. Further experiments within the group will seek to demonstrate if IFN treatment associates with sickness behaviour in order to determine if this is a clinically meaningful model. Suprisingly, we did not see similar changes in the IFNα treated group, which requires further investigation.Funding: University of Glasgow, The Sackler Trust


A novel graph data model of non-small cell lung cancer clinical and genomic data has been constructed with two aims: (1) provide a suitable model for facilitating graph analytics within the Neo4j framework or through tools which can interact through existing Neo4j APIs; and (2) provide a base model extensible to other cancer types and additional datasets such as those derived from electronic health records and other real world sources.Clinical and genomic data integrated with a novel property graph database schema from publicly available datasets and analyses based on The Cancer Genome Atlas lung cancer datasets augmented by with subgraphs patient-patient social network from similarity and correlation as well as individual based biological networks. The pathobiology of cancer involves the coordinated dysregulation of multiple processes across molecular, cellular, tissue, and organism scales . SomaticThis note describes a representation of non-small cell lung cancer in Neo4j, a property graph database platform which natively stores and processes graph data models. A version is available in a GPL3-licensed open-source community edition .Non-small cell lung cancer is the most common cause of cancer deaths worldwide , 8, and By adopting graph database technology, this data resource aims to provide a platform to explore the utility of integrative graph-based systems biology analyses to decode the molecular and clinical underpinnings of complex diseases.All data files and datasets are deposited in the Harvard Dataverse repository in dataset “A Cancer Graph: A Lung Cancer property graph using Neo4j” . A file The property graph database consists of (a) publicly available open access data of patients with non-small cell lung cancer and (b) derived variables augmented by relationships defining different subgraphs. The database contains data from > 1000 patients from the Cancer Genome Atlas (TCGA) which contain clinical, diagnostic, and therapeutic data , as well as multiple genomic measures . Additional attributes are derived from independent published analyses based on these data, providing signatures related to immunologic, DNA repair, molecular portrait subtypes, and profiles from a variety of biological pathways –15. The CancerCase (Patient-based) networks provide graphs of the relationships between patients based on calculation of similarity and correlation scores of molecular signatures such as immune scores or DNA repair profiles.Intra-patient biological signaling activation networks InFlo  is a robThe primary value of the dataset come from calculated relationships which create subgraphs that serve as a substrate for the application of exploration and application of graph algorithms –22. ThesIn summary, a novel graph data model has been constructed integrating clinical and molecular data of non-small cell lung cancer patients with aims: (1) a graph model for facilitating graph analytics within the Neo4j framework or through tools via the Neo4j application programming interface (API); and (2) exploratory basis extension to other tumor types or clinical datasets derived from electronic health records.The database is limited in the number of variables, which may not satisfy specific needs.The schema of the database.TCGA is rich in omics but relatively poor in clinical details .And other sources with additional modifications .


Trajectories were labelled: Continuously Improving (27%) and Improving then Plateauing (73% of sample). Children in the two trajectories differed in levels of symptom severity, language, cognitive, and adaptive functioning skills. Study findings can inform the development of more personalized services for children with ASD transitioning into the school system.This study examined the trajectories of autistic symptom severity in an inception cohort of 187 children with ASD assessed across four time points from diagnosis to age 10. Trajectory groups were derived using multivariate cluster analysis. A two trajectory/cluster solution was selected. Change in trajectory slopes revealed a The online version of this article (10.1007/s10803-021-04949-2) contains supplementary material, which is available to authorized users. ASD is often diagnosed in early childhood and, in most cases, persists throughout the lifespan . These designs have the advantage of allowing researchers to collect longitudinal data over a shorter time period than with a single cohort design  for a total of 748 observations across all four assessment points: T1 , T2 , T3 , and T4 . The decision to include only children with complete data in the final analyses was made because 43% of the ADOS (main measure of autistic symptom severity) scores at T4 were missing in children with at least three available scores. Using sensitivity analysis, it was determined that imputation strategies, including ones that accounted for the longitudinal structure of the data, produced weak label agreement between the complete data and imputed data and the resulting mean trends were not the same. For a detailed description and comparison of the two samples (included and excluded) see Online Resource 1. The study was approved by the Research Ethics Boards at all participating sites.The original Hus et al., Clustering variables were selected as those variables that best ‘explained’ the assignment of individuals into distinct trajectory groups. Exploratory analysis using boosted regression trees  and subsequent assessment points (T2/T3/T4) were selected as proxy indicators of the DSM-5 ills APA : was used to collect information on the family’s socioeconomic status (SES) including marital status, education (years of schooling), employment status, annual income , and ethnic/racial background based on Canadian Census categories.turning points by calculating cluster slopes between assessment intervals; and (4) exploring within trajectory group (cluster) variability by mapping individual child trajectories from the point of diagnosis and up to age 10. All analyses were carried out in R version 3.4.4.  using longitudinal data on autistic symptom severity indicators to assign children into distinct trajectory groups (clusters); (2) characterizing the derived trajectory groups (clusters) in terms of their data on variables of interest (cluster descriptors); (3) identifying possible trajectory First, children were clustered into distinct trajectory groups (clusters) by treating their longitudinal data as three-way: child/case, variables, and assessment points. To derive the trajectory groups (clusters), we used a grouping of four variables: ADOS CSS, ADOS SA, ADOS RRB, as well as age at ADOS assessment. As noted above, boosted regression trees showed that SA and RRB added unique value to the prediction of autistic symptom severity. As a result, they were included, along with ADOS CSS and age at ADOS assessment, as clustering indicators part of the three-way data in the multivariate longitudinal model.A finite mixture of matrix variate variance-gamma distributions was used  were characterized using data at baseline (T1) and all subsequent assessment points (T2/T3/T4) with means and standard deviations for continuous variables and counts and percentages for categorical variables of interest. Effect sizes  were estimated to compare the derived clusters on variables of interest, including language skills (PLS-4), cognitive ability (M-P-R), adaptive functioning skills (VABS II), and family socioeconomic status (FBIQ).turning points – marked by change in the slope of a trajectory – cluster slopes between assessment intervals  were calculated using linear regression . Due to the accelerated longitudinal design with children entering the study at different ages and being assessed at irregularly spaced time points, we treated the individual trajectories as sparse functional data. Sparse Functional Principal Component Analysis (SFPCA) was used to characterize the main modes of variation of the trajectories around an overall mean trend function  whereas Trajectory Group 2, comprised 73% of the sample (n = 136). The modelled mean ADOS CSS at each assessment by trajectory group are presented in Table A two-group clustering solution was selected based on BIC criteria , in which smaller values are preferred Schwarz, . TrajectTrajectory Group 1 were significantly lower (i.e. less symptomatic) than those of Trajectory Group 2. The mean scores of the DSM-5 specifiers at baseline  were significantly higher for Trajectory Group 1 than those of Trajectory Group 2. Trajectory Group 1 had a lower proportion of children from families of lower socioeconomic status (indexed by income < $80 K and mothers’ years of schooling < 13 years). All effect sizes for differences between the derived clusters in DSM-5 specifiers ranged from moderate to large. Similar cluster differences were evident across all assessment points .The baseline characteristics of trajectory groups (clusters) are presented and compared in Table Trajectory Group 1 improving faster. A turning point marked by change in the slope – from negative to positive – of a trajectory was evident after T3 (80 months of age). Specifically, during the third (T3–T4) assessment interval, Trajectory Group 2 showed no further improvement, whereas Trajectory Group 1 continued to improve but at a slower rate. Based on these results we labeled Trajectory Group 1 “Continuously Improving” and Trajectory Group 2 “Improving then Plateauing”.The modeled mean ADOS CSS over time, as determined by the clustering model, were plotted for each group in Fig. Trajectory Group 1 remain negative across assessment intervals (indicating a continuous reduction in symptom severity), the slopes for Trajectory Group 2 start off negative and end up positive (right above the zero mark), indicating a plateauing in symptom reduction after T3 (after age 6).Using the mean ADOS CSS, the estimated slopes between visits for each trajectory group are presented in Table rate of change and turning points marked by change in the slope of a trajectory.The within-trajectory group (cluster) variability is shown using the smoothed trajectories of individual children see Fig. . A visuachronogeneity) is different in the two groups of children (see Online Resource 3). The Improving then Plateauing trajectory has a banded correlation structure, traditionally associated with longitudinal data while children in the Continuously Improving trajectory show little correlation between their measurements over time.The correlations between time points (T1 to T4) captured by the clustering model reinforce the idea that the within-trajectory longitudinal heterogeneity  in autistic symptom severity in a large inception cohort of children with ASD from the time of diagnosis in the preschool years to age 10. In addition to mapping variability in the developmental trajectories of symptom severity, to our knowledge, this study is the first to examine possible Continuously Improving Trajectory; 27%) in this sample demonstrated continuous improvement  over time. However, the majority of children  started off on an improving trajectory followed by a slowing rate of change that plateaus around age 6, a time which for most children falls within the period of transition into the school system. In line with previous investigations, group trajectories of symptom severity seem to, on average, diverge with time  demands for learning in formal educational settings  to the use of data on symptoms and clinical specifiers collected around the time of diagnosis Harris, . To gainFrom a methodological point of view, investigation of turning points requires careful attention to measurement issues, the examination of systematic intra-individual change over time as well as the experience (in this case transition to school) which might have brought about the change Rutter, . This isContinuously Improving trajectory. One interpretation of this finding may be that a group-level approach to mapping average trajectories may be less useful when studying developmental periods marked by rapid and variable change. This becomes especially important when studying children with ASD during the preschool years, a period during which most children receive early, and often intensive, intervention   during their preschool years Supplementary file2 (DOCX 18 kb)Supplementary file3 (DOCX 177 kb)Below is the link to the electronic supplementary material.


AHDC1) gene that are predicted to lead to truncated AHDC1 protein synthesis. More than 270 individuals have been diagnosed with XGS worldwide. Despite the absence of an independent assay for AHDC1 protein function to corroborate potential functional consequences of rare variant genetic findings, there are also reports of individuals with XGS-like trait manifestations who have de novo missense AHDC1 mutations and who have been provided a molecular diagnosis of the disorder. To investigate a potential contribution of missense mutations to XGS, we mapped the missense mutations from 10 such individuals to the AHDC1 conserved protein domain structure and detailed the observed phenotypes. Five newly identified individuals were ascertained from a local XGS Registry, and an additional five were taken from external reports or databases, including one publication. Where clinical data were available, individuals with missense mutations all displayed phenotypes consistent with those observed in individuals with AHDC1 truncating mutations, including delayed motor milestones, intellectual disability (ID), hypotonia, and speech delay. A subset of the 10 reported missense mutations cluster in two regions of the AHDC1 protein with known conserved domains, likely representing functional motifs. Variants outside the clustered regions score lower for computational prediction of their likely damaging effects. Overall, de novo missense variants in AHDC1 are likely diagnostic of XGS when in silico analysis of their position relative to conserved regions is considered together with disease trait manifestations.Xia-Gibbs syndrome  is a phenotypically heterogeneous neurodevelopmental disorder (NDD) caused by newly arising mutations in the AT-Hook DNA-Binding Motif-Containing 1 ( De novo stop-gain and frameshift mutations in the gene encoding the AT-Hook DNA-Binding Motif-Containing 1 (AHDC1) protein that are predicted by conceptual translation to lead to truncated AHDC1 protein synthesis are well-established as an underlying cause of Xia-Gibbs syndrome .14 Reported truncating mutations span most of the length of the protein and include some sites of recurrent, independently arising de novo events. AHDC1 likely has a function in the nucleus mediated by its AT-hook binding motifs that are associated with DNA binding.16 Following the identification of the first four XGS cases,12 more than 270 individuals with XGS have been identified throughout the world by the XGS family support group and staff at the Baylor College of Medicine (BCM) Human Genome Sequencing Center (HGSC). Eighty-four of these individuals have provided consent for further research and detailed phenotype and AHDC1 mutation information, which is housed in a dedicated and secure XGS Registry.8AHDC1.12 In the majority of cases, de novo, pathogenic AHDC1 mutations were identified via trio exome sequencing, while plausible variants in other genes were not identified or were excluded based upon absent genotype-phenotype correlation.12 In instances where de novo mutation status could not be determined due to the lack of trio-based sequencing data or the lack of DNA samples from both biological parents for segregation studies, the pathogenicity of a truncating AHDC1 variant was established based on the similarity of the clinical manifestations to other individuals with XGS, coupled with predicted damaging effects of the truncating variants.As clinical manifestations of XGS overlap with the multitude of other heterogeneous individually rare NDD traits, all diagnoses so far have been dependent on molecular diagnostic testing by DNA sequencing approaches, and the disease is essentially defined by the molecular diagnostic determination of a pathogenic or likely pathogenic variant identified in AHDC1 truncating mutations, it remains challenging to determine which amino acid changes may be deleterious for AHDC1 function. This challenge is further exacerbated by lack of a “biomarker” or laboratory assay to assess protein function. AHDC1 is well conserved across most vertebrates, with 94% identity between human and mouse proteins. The gene is overall intolerant to missense variation, with a positive missense Z score of 2.86 and a missense observed-versus-expected mutation ratio of 0.75 reported in the Genome Aggregation Database (gnomAD v.2.1.1).17 There are many known rare and ultrarare AHDC1 variants in the gnomAD population control cohort, however, including 528 missense variants, of which 98% (518) have a minor allele frequency (MAF) < 0.001. It is not known how many individuals who harbor rare variant AHDC1 alleles as reported in gnomAD may potentially have a mild NDD. Therefore, neither the specific amino acid change nor the allelic frequency of a missense variant is sufficient to infer pathogenicity.Compared to AHDC1 have been reported in the literature or in accessible public databases .18 This is a position with identical recurring de novo frameshift mutations in at least five other XGS individuals with no reported craniosynostosis,1 and whether this is a phenotypic expansion of the XGS trait or potentially represents a clinical manifestation due to a dual molecular diagnosis and multilocus pathogenic variation (MPV) remains a question.19To date, a total of five putatively pathogenic missense variants in atabases . Each re20 indicate de novo mutations at positions 548, 549, and 1,348  that have been ascribed to XGS. One variant reported by GeneDx indicates a possible XGS diagnosis for an individual with a de novo change at position 487 (Arg487Trp). While the de novo origin of these missense variants and shared phenotypes between these individuals and the previously reported XGS clinical spectrum are strongly suggestive of XGS molecular diagnoses, there is no independent functional testing method to show the impact of these changes on molecular function or cellular phenotype to objectively and independently corroborate the findings by an orthogonal experimental functional assay. In some cases, it is not clear which criteria were used to eliminate other possible variants in the genome as potential factors contributing to disease. Therefore, the assignment of each of these AHDC1 mutations as the underlying cause of the clinical manifestations of these individuals may be premature.Three entries in DECIPHERAHDC1 who were provided a molecular and clinical diagnosis of XGS. The genotypic profiles from these individuals, together with the five from earlier reports of missense variants in AHDC1, are analyzed . This allelic series is the largest and only such study of the AHDC1 locus. Moreover, we report the objective quantitative analysis of XGS trait manifestations in comparison to well-established pathogenic AHDC1 truncating variant alleles and to other Mendelizing disorders. Collectively, these analyses provide additional evidence for pathogenicity for some, but not all, of the missense variants in AHDC1 that have been ascribed to XGS.In this study, we report an additional five individuals with missense mutations in 6 Third, one family provided data as approved by protocol IRB #170447 (Genomic Sequencing in Neurologic Disorders) approved by the University of California at San Diego IRB and Rady Children’s Hospital Research Compliance. As a result, the mutation data for all 10 individuals were available. Partial phenotype data were also available for the five “external” individuals, and detailed clinical data were available for the five individuals who had consented to participation in this study via the XGS Registry.Approvals for data use for this study fell into three categories. First, the five individuals who joined the XGS Registry consented for participation under approval by the BCM Institutional Review Board (IRB), protocol number H-39945. Second, data from four individuals were used according to the DECIPHER allowable use agreement or were from published information.21 hosted in a local Health Insurance Portability and Accountability Act (HIPAA)-compliant server. Following initial contact, parents of probands were queried for participation in the XGS Registry and presented with initial consent forms. Next, they were invited to fully consent and to either directly deposit clinical records or to enable their healthcare provider to share their history. Genetic reports and clinical reports were then independently reviewed by BCM HGSC investigators. Additional included individuals (not enrolled in the XGS Registry) were identified through Genematcher22 and DECIPHER.20Affected individuals were initially recruited through social media, e-mail, physician contact, or by word of mouth. The XGS Registry was configured in a RedCap environment,The initial molecular diagnoses were by a variety of next-generation DNA sequencing methods . Follow-8 Affected individuals with a report of low muscle tone or hypotonia were indicated under one phenotypic category (“hypotonia”) summarizing the phenotype. Additional phenotypic features that were not part of the previously reported XGS spectrum were also noted. Limited phenotype data were available for three of the five individuals who did not join the XGS Registry, where caregivers provided information . The OMIM disease/XGS individual similarities were determined using cosine similarity algorithm based on the co-occurrences of HPO terms, normalized by term frequency-inverse document frequency aggregated from all the OMIM diseases (scikit-learn package in Python). This procedure resulted in pairwise phenotypic similarities between all the OMIM diseases and individuals with XGS. Pairwise phenotypic similarity scores ranged from 0 (no match) to 1 (highest possible match) and were plotted into networks using igraph in R. We also trimmed the OMIM disease node to keep the diseases with at least one neighbor with similarity score > 0.1 .We compared Human Phenotype Ontology (HPO) terms representing the phenotypes of both individuals with XGS due to protein-truncating mutations (n = 34) and the five individuals from the XGS Registry with missense variants to data from Online Mendelian Inheritance in Man (OMIM). The HPO descriptions for OMIM diseases with at least five HPO terms were obtained from the Jackson Laboratory HPO database.AHDC1 missense variants were analyzed by multiple in silico pathogenicity prediction algorithms. These methods included Missense Tolerance Ratio (MTR),24 Combined Annotation Dependent Depletion (CADD v.1.6),25 Functional Analysis through Hidden Markov Models (FATHMM-XF),26 and REVEL.27 These scores were then compared to those calculated for AHDC1 missense variants reported in the Genome Aggregation Database (gnomAD v.2.1.1) control cohort. All variants in this study were scored using American College of Medical Genetics and Genomics (ACMG) criteria utilizing VarSome.28AHDC1 missense mutations were studied. Five of those individuals were from external reports, and a further five individuals with missense variants in AHDC1 were separately enrolled in the XGS Registry  within or flanking the region of the highly conserved AT-hook domain 2 and cluster 2, a conserved REV3L domain (Domain of Unknown Function 4683 [DUF4683]) . Cluster 2 consisted of three variants that spanned 131 residues near the C terminus of the protein, within or near a second domain that is conserved with REV3L  . One of election .Three of the 10 missense variants fell outside the clusters. A variant at amino acid position 487 was within 51 residues of the first cluster, but where it “sits” in three-dimensional protein space and secondary and tertiary protein structure is unknown. The variants within individuals 1 (p.Pro47Ser) and 7 (p.Gly792Arg) did not cluster with other variants and the map to undefined AHDC1 protein regions, with no homology to other proteins.de novo or suspected de novo missense mutations in AHDC1 considered here were predicted as LP using in silico pathogenicity scores including CADD and FATHMM-XF  in FAT Atypical Cadherin 3 (FAT3). Although FAT3 has not been definitively associated with a Mendelian disorder, recently a FAT3 variant has been implicated as a potential contributor to autism spectrum disorder (ASD).29 Further, the AHDC1 de novo missense mutation in individual 1 was classified as LB according to ACMG criteria. This classification was supported by the observation of different alleles at the same amino acid position in two individuals in the gnomAD database—although those alleles were not observed in the gnomAD reported “normal” (control) cohort and may also have had disease association.Other data, including variants in genes other than AHDC1 missense mutation noted mosaicism, although further details were unavailable. Further, features that were atypical for XGS were noted , was potentially highly informative for this study, as the mutation occurred at position 487, which was near the proximal boundary of the cluster of four variants spanning amino acid positions 537–607. Consultation with the individual’s caregivers revealed, however, that the initial genetic evaluation of the re noted . OverallAHDC1 (p.Ser1348Pro) was confounded by the presence of a hemizygous LP de novo missense mutation in the HECT, UBA, and WWE Domain Containing E3 Ubiquitin Protein Ligase 1 (HUWE1 c.9070G>A [p.Ala3024Thr]) gene. Missense mutations in HUWE1 are highly constrained (missense Z score = 8.87);both SNV and duplication Copy Number Variation involving HUWE1 are known causes of X-linked intellectual disability .The diagnosis of individual 8, who harbored a COOH-terminal missense variant allele in No other potentially pathogenic variants were identified in other individuals in this study, although a series of variants were reviewed and determined to be VUS .AHDC1.1 The five core phenotypes were observed in >80% of XGS individuals and thus perhaps represent a clinical synopsis of the AD trait associated with this locus. These clinically observed findings include delayed motor milestones, ID, hypotonia, low muscle tone, and speech delay, potentially refining the core AHDC1-associated NDD trait. For the current study, we combined hypotonia and low muscle tone into a single category, although the two features were reported separately during most assessments. We compared the occurrence of these core and secondary phenotypes derived from individuals with truncating mutations to those with AHDC1 missense mutations. These comparisons provided evidence that the spectrum of clinical manifestations of individuals reported with putatively pathogenic missense mutations in AHDC1 largely overlapped with those harboring truncating mutations  versus loss-of-function (LoF) mutational effect.Of note, all five individuals from the XGS Registry with missense variants had ID, and four of the five exhibited delayed motor milestones, speech delay, and a diagnosis of hypotonia/low muscle tone. Individuals in this study who were ascertained through public databases or previous studies did not have the same consistent phenotypic assessment information available as the information content available for those from the XGS Registry. Phenotypes for subject 2 were not available, and the DECIPHER individuals had limited phenotypic information upon query . Howeverde novo missense versus a truncation mutation in AHDC1, a comparative analysis utilizing the 17 phenotypic terms collected from the XGS Registry was implemented. The 17 observed clinical phenotype features were converted into HPO terms and used to compare the phenotypic similarities among all the individuals with XGS, as well as the clinical manifestations of 3,464 human disease traits as defined by the clinical synopsis of individual entries from OMIM. Data from individuals with missense variants in AHDC1 clustered together with those from individuals harboring truncating mutations , correlation with clinical phenotyping and expected observations may be useful in molecular diagnostic interpretation, and the presence of multilocus pathogenic variation needs to be considered in the molecular differential.30The first 20 individuals reported with an XGS diagnosis bore AHDC1 variants reported as underlying XGS suggest a clustering of events in two primary sequence regions of the protein. One cluster was striking, as it contained 4/10 mutations within 71 amino acid residues—less than 5% of the protein. This cluster also contained an AT-hook binding domain, with two of the missense mutations in the core AT-hook 12-amino-acid motif.12 The second, more broadly defined cluster contained three missense variants and spanned 131 amino acid residues—about 8% of the protein. The three remaining mutations did not map to any defined cluster or associate with regions of strong conservation or computationally defined domains. Although the structure of AHDC1 is not solved, and 3D protein effects cannot yet be considered, these data suggest that the variants that arise in either of the two clusters are potentially more likely to perturb normal protein function.The amino acid positions of the 10 missense AHDC1 variants in this study passed the likelihood threshold for deleteriousness using the CADD scores (21.5 to 28.6) to predict their effects, those outside of the clusters had generally lower scores (21.5 to 24.8). FATHMM-XF analysis further differentiated the two classes of variants, with all mutations in the clusters reported as LP and the three additional variants with the lowest scores for pathogenicity  interpretation, there may be limitations to gnomAD-assisted variant interpretation when potentially dealing with GoF mutation effects.33Together, the data here suggest guidelines for consideration of the possible pathogenicity of newly observed missense variants in AHDC1 mutations result in XGS is unknown, and there have been prior suggestions of both haploinsufficiency and dominant negative effects.34 Missense mutations are expected to result in full-length protein products and are therefore more likely to act in dominant negative (antimorphic) or other GoF (neomorphic and hypermorphic) manners. Consequently, the report here and from prior missense mutations supports the dominant negative model, but it does not exclude that both haploinsufficiency (LoF) and dominant negative (GoF) mechanisms could be at play, depending on the primary mutation.The mechanism by which de novo truncating mutations leads to disease.37 There is, however, a paucity of well-studied examples where such loci are also damaged by missense variants and yield related phenotypes. This is in part likely due to the complexity of determining causation for missense variants in disease diagnosis. Where previous work has identified pathogenic missense variants in genes that are also associated with autosomal dominant disorders , the enrichment of missense variants in conserved protein domains has been noted.38 This trend is consistent with observations here of missense mutations clustering within and near the conserved AT-hook 2 domain and the REV3L homology domain (DUF4683) in AHDC1.There are a growing number of early-onset severe neurological Mendelian diseases for which, like XGS, a known gene with AHDC1 missense mutations—five novel putative XGS molecular diagnoses and five reported mutations. These novel missense AHDC1 variants newly described in this study provide confirmatory supportive evidence that some missense variants in AHDC1 can cause XGS. Quantitative clinical phenotyping reveals missense alleles share a core monoallelic NDD autosomal dominant trait phenotype with that observed in association with LoF variant alleles but suggests that seizures may more likely be observed in association with missense alleles. To what extent such findings might implicate potential GoF effects remains to be determined by more extensive studies of AHDC1 allelic series.In summary, we report quantitative phenotyping and analyses in 10 1


Oncorhynchus nerka) stocking program in Skaha Lake, British Columbia as a case study. Previous analyses detected significant levels of hybridization between the anadromous (sockeye) and freshwater resident (kokanee) forms of O. nerka, but were restricted to assigning individuals to pure-stock or “hybrid”. Simulation analyses indicated our GT-seq panel had high accuracy, efficiency and power (> 94.5%) of assignment to pure-stock sockeye salmon/kokanee, F1, F2, and B2 backcross-sockeye/kokanee. Re-analysis of 2016/2017 spawners previously analyzed using TaqMan® assays and otolith microchemistry revealed shifts in assignment of some hybrids to adjacent pure-stock or B2 backcross classes, while new assignment of 2019 spawners revealed hybrids comprised 31% of the population, ~ 74% of which were B2 backcross or F2. Overall, the GT-seq panel development workflow presented here could be applied to virtually any system where genetic stock identification and intra-specific hybridization are important management parameters.Stocking programs have been widely implemented to re-establish extirpated fish species to their historical ranges; when employed in species with complex life histories, such management activities should include careful consideration of resulting hybridization dynamics with resident stocks and corresponding outcomes on recovery initiatives. Genetic monitoring can be instrumental for quantifying the extent of introgression over time, however conventional markers typically have limited power for the identification of advanced hybrid classes, especially at the intra-specific level. Here, we demonstrate a workflow for developing, evaluating and deploying a Genotyping-in-Thousands by Sequencing (GT-seq) SNP panel with the power to detect advanced hybrid classes to assess the extent and trajectory of intra-specific hybridization, using the sockeye salmon ( In particular, 80% of salmonid populations in the Columbia River have experienced losses linked to hydroelectric development and the destruction of spawning and rearing habitat8. In response, management strategies have included incorporating fish passage at dams to mitigate impacts to migratory fish life histories and supplementing populations in decline with hatchery fry for stock enhancement. For example, Coho salmon (Oncorhynchus kisutch) have been restored in the lower Columbia River using hatchery stocking, resulting in the establishment of local naturalized populations9. Additionally, the enhancement of spawning habitat has been an effective strategy to restore fish populations, where the replacement of river substrate improved water velocities, dissolved oxygen, and usage of the site by spawning Chinook salmon (O. tshawytscha)10. Ultimately, the success of fish stocking programs that target species with diverse migratory and resident forms should include careful consideration of resulting hybridization dynamics and corresponding outcomes on recovery initiatives.Global fish populations are in decline due to agricultural development, rapidly rising riverine water temperatures, and reduced connectivityOncorhynchus mykiss) and cutthroat trout (O. clarkii)11, as well as within-species hybrids between different life history forms, such as the case between anadromous sockeye salmon and freshwater resident kokanee12. The long-term effects of hybridization are often complex, with the potential for both positive and negative outcomes13. Hybridization can be a powerful conservation tool harnessed to rescue populations with low genetic diversity and increase fitness through the integration of favorable traits such as larger body size, more offspring, and longer lifespans16. On the other hand, detrimental effects have been observed such as introgression with maladapted gene complexes17, decrease in reproductive success18, and negative impacts on growth19, with hybrid fitness theorized to decrease as the divergence between parental phenotypes increases20. Overall, the impacts of intra-specific hybridization in the wild are still not well understood, warranting further study, especially when observed as part of an active management program.Hybridization in fish species is well documented, including inter-specific hybrids such as rainbow  and freshwater resident kokanee (hereafter referred to as “kokanee”). Kokanee are much smaller than sockeye salmon (26 cm versus > 45 cm average adult fork length) and occur sympatrically in many lakes, but tend to exhibit different spawning habitat preferences and spawning periods23. Despite differences in spawning behavior, kokanee males are known to sneak on spawning sockeye salmon females24, and size-selective mating has been observed between male sockeye salmon and female kokanee25, allowing for gene flow between migratory forms. Sockeye-kokanee hybridization can lead to an increase in body size of resident hybrids that can increase angler satisfaction19 and bolster the overall genetic diversity of O. nerka in the system16. Conversely, hybrids can also experience negative impacts such as lower swimming capabilities than pure sockeye salmon26, medial seawater adaptabilities27, intermediate maturation time23, decreased egg survival rate28, and the loss of the iconic red coloration that is key in sexual selection25. With a broad range of potential outcomes, accurate assessment of the extent of introgression is critical for elucidating the long-term effects of hybridization associated with supplementation and reintroduction programs.O. nerka migratory and resident forms outside of a laboratory setting. Previous research examining hybridization between sockeye salmon and kokanee found O. nerka hybrids occupy intermediate morphologies and exhibit a largely resident life history, although this latter finding requires further investigation29. These studies employed genetic marker sets that were effective at differentiating pure-stock from hybrid, however, they had limited power for the identification of advanced hybrid classes that has become increasingly important for genetic monitoring as the reintroduction program matures. The detection of advanced hybrid classes within a species can be challenging due to the proximity of backcrosses to pure genotype frequencies. For example, allozyme loci were only able to reveal intermediate allelic frequencies suggesting hybridization between transplanted and native sockeye30, while microsatellite assays have the ability to detect introgression to the F1 level31, but both marker types experience difficulty in the detection of advanced hybrid classes based on the conventional number of loci employed33. In that regard, traditional markers may not have the statistical power to identify advanced hybrid classes34, with an estimated minimum of 70 markers required to discriminate between pure parental species and advanced backcrosses35. A previous simulation-based sensitivity analysis found that a panel comprised of 300 highly differentiated SNPs has the resolution needed to detect advanced hybrid classes between sockeye salmon and kokanee in Skaha Lake36. With this in mind, advancements in massively parallel sequencing can be leveraged to improve the identification of hybridization in systems by pooling barcoded amplicons to increase the power of genetic panels for classification37; Genotyping-in-Thousands by sequencing (GT-seq) is a particularly useful approach in cases where large sample sizes need to be cost-effectively genotyped38.To address large-scale declines in sockeye salmon populations in the Columbia River, an experimental reintroduction program was initiated in Skaha Lake within the Okanagan Basin of the Southern Interior of British Columbia, Canada. This program provides an excellent opportunity to investigate the extent and outcomes of intra-specific hybridization between 1, F2, B2 backcross-kokanee, B2 backcross-sockeye) with three different SNP datasets  to assess information content to inform panel construction. We then evaluated the accuracy, efficiency, and power of the optimized GT-seq panel using simulation analyses. We further assessed panel performance by re-genotyping individuals sampled in Skaha Lake in 2016 and 2017 that were previously genetically assigned as pure-stock or F1 hybrid at 32 SNPs and subjected to otolith microchemistry analysis to reconstruct migratory history29. Finally, we genetically assigned O. nerka spawners sampled in 2019 to pure-stock or hybrid class  to estimate stock proportions and examine trends in this system over time.Here, we demonstrate a workflow for developing, evaluating and deploying a GT-seq SNP panel with the power to detect advanced hybrid classes to assess the extent and trajectory of intra-specific hybridization, using the sockeye salmon reintroduction program in the Okanagan Basin as a case study. Using previously published restriction site associated DNA sequencing (RAD-seq) collected for this system, we first simulated two pure  and four hybrid classes  and 2017 (n = 59)29, and newly sampled in 2019  as deadpitch (post-mortem carcass collection after spawning) by personnel from the BC Ministry of Forests, Lands, Natural Resource Operations, and Rural Development. The section of river that was sampled represents the only spawning habitat that exists for O. nerka populations from Skaha Lake and all forms spawn within this section of river. Samples were collected on multiple dates that spanned the duration of the spawning run. Biological data (length and sex) and tissue samples (operculum punches preserved in ethanol) were collected from all sampled fish. Moreover, the 2016 and 2017 samples were previously genetically assigned to pure-stock or F1 hybrid using a panel of 32 TaqMan® SNP assays and subjected to otolith microchemistry analysis36 to determine fish migratory history and investigate maternal migratory signatures that are passed transgenerationally to offspring41.We genotyped tissue samples from 42 for: (1) Skaha Lake kokanee (n = 20) collected in 2003 prior to the sockeye salmon restocking program; and (2) Okanagan River sockeye salmon (n = 35) collected in 2012 downstream of historical migration barriers. Using the populations module in STACKS version 2.0 beta 843, we required all loci to be present in at least 60% of individuals in both populations, with a minor allele frequency greater than 0.05. Due to the salmonid whole genome duplication event, we filtered out suspected homeologs by removing any locus with a negative Fis or Hobs > 0.5 that occurred in both reference populations44. The resulting SNP dataset was then filtered for quality using VCFtools45 to remove SNPs not in Hardy–Weinberg equilibrium and to calculate Weir and Cockerham (1984)’s θ, an unbiased estimate of Fst46, between Skaha Lake kokanee and Okanagan River sockeye salmon following36. We removed loci with insufficient flanking sequence required for primer design by retaining loci with the SNP positioned between the 40th and 70th base pairs of the RAD tag sequences and selected the top 650 loci exhibiting the highest Fst. We then assessed all loci pairs for deviation from linkage equilibrium using GENEPOP 4.547, removed loci that were linked, and finalized selection of 600 candidate loci.We used previously published genotypic data collected via RAD-seq1, F2, B2 backcross-kokanee, B2 backcross-sockeye) with recom-sim.py . Separate simulations were conducted at: 1) 300 highest Fst SNPs; 2) 600 highest Fst SNPs; and 3) 350 SNPs randomly selected from the 600 highest Fst SNPs. We assigned simulated individuals to parental or hybrid class by calculating the posterior probabilities of membership as implemented in NEWHYBRIDS48 with the reference populations flagged as known genotypes with the “z” option, and constructed confusion matrices for each panel assessment. After in-silico assessment, we sent the full RAD tag sequences that were associated with the pool of candidate SNPs to GTseek LLC (https://gtseek.com/) for custom locus-specific primer design.To examine panel informativeness of candidate loci and forecasting of panel ability after primer dropout, we simulated parental and offspring individuals (n = 1000) for six hybrid classes . Library preparation followed the original protocol38, with the exception that we diluted the PCR1 product to 1:10 (10.17504/protocols.io.byvppw5n). The PCR2 product was quantified with Picogreen™  and each sample was normalized to a concentration of 10 ng/µL. The pooled library was purified with a MinElute PCR Purification Kit (Qiagen) and eluted into a final volume of 25µL. Test libraries were sequenced using a Mid Output Reagent Kit (300 cycles) on an Illumina MiniSeq within the Ecological and Conservation Genomics Laboratory at the University of British Columbia Okanagan.We constructed a GT-seq test library with the previously extracted DNA samples collected in 2016 (n = 45) and 2017 (n = 59) for which otolith microchemistry analysis was previously conductedhttps://github.com/GTseq/GTseq-Pipeline). We removed primers with non-specific in silico probes, candidates that were overrepresented (exhibiting > 2% of the raw read count), observed primer dimers, potential PCR artefacts, off-target amplification or in silico probe variation following previously published work49. A second and third test library with the same sample composition as the first test library were prepared and tested iteratively with optimized primer pools from previous libraries using the protocols detailed above for sample preparation, sequencing, and primer dropout . We filtered out individuals with > 25% missing data using PLINK50. We decided on a cutoff of 25% to minimize the proportion of missing data that can negatively impact the detection of population structure, particularly with the high resolution needed to elucidate hybrid classes within a species51.To compile the final dataset based on the optimized SNP panel for downstream analyses, raw sequencing files from individuals were concatenated across sequencing runs and processed with the GT-seq pipeline available on GitHub  with hybridpowercomp as implemented in the R package hybriddetective52. Specifically, we evaluated assignment probabilities based on: (1) accuracy ; (2) efficiency ; and (3) power (product of accuracy and efficiency). We also used hybriddetective52 to quantify error rates associated with individual assignment  as follows: (1) Type I: false positive error rate ; and (2) Type II: false negative error rate .We assessed the accuracy, efficiency and power of NEWHYBRIDS assignments with simulated individuals (n = 100) using two pools  were specified, and the analysis initiated with a burn-in period of 10,000 followed by 50,000 iterations. We applied the s and z flags to the reference sockeye salmon and kokanee populations to designate individuals of known genotype that were sampled separately from the test individuals. We then selected the maximum probability of assignment to assign individuals to the most likely class. Lastly, we compared the NEWHYBRIDS48 genetic assignment to pure-stock or hybrid class to the migratory history of the individuals, as previously inferred29 by way of otolith microchemistry analysis.We genetically assigned the 2016 and 2017 sampled individuals to pure-stock or hybrid class using NEWHYBRIDS2 of tissue, 5 µl proteinase K (10 mg/ml), and 195 ul 10% Chelex solution53. Plates were incubated for 4 h at 55 °C to digest tissue, and then 95 °C for 15 min using an Applied Biosystems Veriti thermal cycler . Library preparation was conducted as above with the optimized SNP panel. The multiplexed pooled library was sequenced using a partial High Output Reagent Kit (300 cycles) on the Illumina MiniSeq within the Ecological and Conservation Genomics Laboratory at the University of British Columbia Okanagan. Samples with individual missing data > 25% were filtered out for downstream analyses. Individual assignment to hybrid class was conducted using NEWHYBRIDS48 and the same parameters as above. Fisher’s Exact Test was conducted in R to determine if proportions of hybrid class were significantly different within a sample year and between sample years.For the new samples collected in 2019 (n = 218), DNA was extracted with a standard Chelex-based protocol in 96-well plates containing approximately 0.5 mm1, F2, B2 backcross-kokanee, and B2 backcross-sockeye).One-way ANOVA and post-hoc Tukey Tests were conducted in R to determine if mean fork length for the 2019 samples was significantly different between individuals assigned to pure-stock  and the various hybrid classes (FFst SNP panel performed best with high accuracy (> 0.98), followed by the top 600 Fst SNP panel (> 0.96) and the random 350 SNPs selected from the top 600 Fst SNPs (> 0.92).The simulated SNP panels provided comparable accuracy to assign individuals to hybrid class based on initial NEWHYBRIDS assignment of simulated individuals Table . The topFst SNPs, primers were successfully designed for 515 SNPs after in silico testing. Following three rounds of multiplex amplicon sequencing and primer pool optimization, the optimized GT-seq panel consisted of 342 SNPs  and 2017  (Table We successfully genotyped 2016 (n = 36) and 2017 (n = 55) Okanagan River spawners with the 342 SNP GT-seq panel after filtering for 25% missing data across individuals . Overall, assignments to pure-stock and hybrid class were generally similar between the different marker sets, although there was a slightly higher proportion of individuals assigned to pure-stock using the 342 SNP GT-seq panel Table . Our 34229 and 342 SNP GT-seq panel. In 2016, four hybrids were reclassified as sockeye salmon with three instances of microchemistry conflict, where the latter results suggested that these individuals did not migrate to the ocean  when compared to other hybrid classes.We genotyped 2019 Okanagan River spawners (n = 202) with the 342 SNP GT-seq panel after filtering for 25% missing data across individuals . The composition of the 2019 Okanagan River spawners revealed a higher proportion of kokanee . Across sample years, we found that the proportions of kokanee , F2  and B2 backcross-kokanee  were not significantly different. The proportions of sockeye salmon , F1 , and B2 backcross-sockeye  were significantly different across sample years.We found significant differences in proportions among hybrid classes within a year among all sample years , and displayed high assignment accuracy across all hybrid classes (> 94%), as well as comparable panel accuracy, efficiency, and power between the top 300 Fst SNP pool, random 350 SNP subset of the top 600 Fst SNP pool, and ultimately, our optimized 342 SNP panel  to an F1 hybrid. Microchemistry revealed saltwater inhabitance for the both this individual and their maternal parent; the reclassification to F1 hybrid suggests that that the paternal parent was likely a kokanee rather than a sockeye , likely due to a combination of factors such as the number of spawners and fry stocked, variable ocean survival, and high water temperatures during upstream migration57. The re-establishment of wild spawning sockeye salmon created the possibility of hybridization with resident kokanee in later years; our results demonstrate that hybrids most recently comprised 31% of the population  may develop, while the occurrence of pure-stock sockeye salmon is likely to remain due to on-going stocking programs using broodstock collected downstream from Skaha Lake.Sockeye salmon have been successfully re-introduced into the Okanagan Basin and comprised 41% of the 29 suggest that hybrid O. nerka overwhelmingly exhibit a resident life history, but can express a migratory life history with at least one F1 hybrid spending time in the marine environment  tags to mark juvenile movement, and subsequent comparison of recovered tags and genetic hybridization classes to evaluate survival and age at maturity59.Understanding the fitness outcomes of hybridization is important when considering the overall productivity of the system and long-term management goals. Our results and those of a previous study28. Conversely, the overall increase in body size of the resident population resulting from the presence of hybrids in the system may be advantageous for some management strategies, especially those that target increasing recreational angling quality or harvest opportunities for First Nations, as larger bodied fish generally have increased survival and are often more valued as a food source.The population level outcomes arising from an intermediate body size in hybrids may ultimately be detrimental to the fitness of migratory hybrids compared to the larger-bodied sockeye salmon, as body size is often directly correlated with fecundity2 backcross-kokanee, F2 and kokanee classes having no significant mean size differences60. Therefore, if stock assessment programs and management goals require an estimate of stock composition, genetic tools such as GT-seq panels can be an effective enumeration method for their ability to accurately detect hybrid classes and expand sample sizes.From a stock assessment perspective, our results emphasize that, though morphology and body size have been used historically to determine hybrid class in this system and others, advanced hybrid classes would be indistinguishable from pure-stock, with the BHere, we demonstrated the effectiveness of GT-seq for identifying advanced hybrid classes and, in this case, tracking the trajectory of sockeye salmon, kokanee, and hybrid stocks as part of a re-introduction program. Given the uncertainty in the long-term composition of the population and the range of potential outcomes and impacts on management goals, continued genetic monitoring of this system is recommended. More broadly, the GT-seq panel development workflow presented here could be applied to inform other sockeye salmon restoration initiatives in the Columbia and Fraser River systems, or in other species and systems where genetic stock identification and intra-specific hybridization are important management parameters.Supplementary Figure S1.Supplementary Figure S2.Supplementary Tables.