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Objective Approximately one-third of sepsis patients experience poor outcomes including chronic critical illness (CCI, intensive care unit (ICU) stay > 14 days) or early death (in-hospital death within 14 days). We sought to characterize lipoprotein predictive ability for poor outcomes and contribution to sepsis heterogeneity. Design Prospective cohort study with independent replication cohort. Setting Emergency department and surgical ICU at two hospitals. Patients Sepsis patients presenting within 24 h. Methods Measures included cholesterol levels (total cholesterol, high density lipoprotein cholesterol [HDL-C], low density lipoprotein cholesterol [LDL-C]), triglycerides, paraoxonase-1 (PON-1), and apolipoprotein A-I (Apo A-I) in the first 24 h. Inflammatory and endothelial markers, and sequential organ failure assessment (SOFA) scores were also measured. LASSO selection assessed predictive ability for outcomes. Unsupervised clustering was used to investigate the contribution of lipid variation to sepsis heterogeneity. Measurements and main results 172 patients were enrolled. Most (~ 67%, 114/172) rapidly recovered, while ~ 23% (41/172) developed CCI, and ~ 10% (17/172) had early death. ApoA-I, LDL-C, mechanical ventilation, vasopressor use, and Charlson Comorbidity Score were significant predictors of CCI/early death in LASSO models. Unsupervised clustering yielded two discernible phenotypes. The Hypolipoprotein phenotype was characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1), higher SOFA scores, and worse clinical outcomes (45% rapid recovery, 40% CCI, 16% early death; 28-day mortality, 21%). The Normolipoprotein cluster patients had higher cholesterol levels, less endothelial dysfunction, lower SOFA scores and better outcomes (79% rapid recovery, 15% CCI, 6% early death; 28-day mortality, 15%). Phenotypes were validated in an independent replication cohort (N = 86) with greater sepsis severity, which similarly demonstrated lower HDL-C, ApoA-I, and higher ICAM-1 in the Hypolipoprotein cluster and worse outcomes (46% rapid recovery, 23% CCI, 31% early death; 28-day mortality, 42%). Normolipoprotein patients in the replication cohort had better outcomes (55% rapid recovery, 32% CCI, 13% early death; 28-day mortality, 28%) Top features for cluster discrimination were HDL-C, ApoA-I, total SOFA score, total cholesterol level, and ICAM-1. Conclusions Lipoproteins predicted poor sepsis outcomes. A Hypolipoprotein sepsis phenotype was identified and characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1) and organ failure, and worse clinical outcomes.

The advent of sequencing technologies has revolutionized our understanding of disease. Researchers can now investigate the complex processes involved in the multi-layered transcription of genetic content, which regulates cell activity, homeostasis, and ultimately the organism's health. A disease can be conceived as a deviation from a homeostatic state, leading to cascading negative effects. A disease state, or more generally a disrupting factor (sometimes called a \"perturbagen\"), can be characterized by how it impacts the organism. This information constitutes its \"signature\", such as a list of differentially expressed genes or vectors of abundance of proteins or lipids. Significant efforts have focused on gathering these signatures into connectivity maps (CMAPs), which allow the identification of related disrupting factors based on the similarity of their signatures. CMAPs can overcome some limitations of traditional enrichment analysis. However, challenges remain. The integrative analysis of multi-domain data, as opposed to concurrent or sequential analysis, is still a challenge. The complexity of multi-omics analysis, involving retrieving datasets, annotations, and applying analytical pipelines, requires advanced programming skills, which can be a barrier for researchers without dedicated resources. Additionally, analysis pipelines need to scale up as assays become clinically available and more data is generated. To address these challenges, we developed machine learning tools to predict health outcomes, ranging from sepsis to dementia. Our goal is to build knowledge and expertise about integrative and extensible analytical pipelines for clinical, transcriptomics, and proteomics data. Specifically, we developed a statistical and machine learning model to classify patients by phenotype and predict mortality risk. We analyzed a prospective cohort of sepsis patients, selected predictive features, built and validated models, and then refined a robust model using only features available in the clinical environment. We also analyzed the post-synaptic protein-protein interactions with PSD95 (a key protein involved in neuronal signaling) in healthy subjects across four brain regions to establish a reference for future analyses of brain disorders. Furthermore, we evaluated the requirements and challenges of implementing an analytical tool at the bedside by integrating our sepsis phenotyping algorithm into a convenient docker container. In summary, the development and application of these machine learning tools provide systematic ways to combine multiple levels of complex clinical and/or multi-omics data. This translates into tangible and applicable analyses with potentially meaningful implications for healthcare.

Context:Central precocious puberty (CPP) results from premature activation of the hypothalamic–pituitary–gonadal axis. Few genetic causes of CPP have been identified, with the most common being mutations in the paternally expressed imprinted gene MKRN3.Objective:To identify the genetic etiology of CPP in a large multigenerational family.Design:Linkage analysis followed by whole-genome sequencing was performed in a family with five female members with nonsyndromic CPP. Detailed phenotyping was performed at the time of initial diagnosis and long-term follow-up, and circulating levels of Delta-like 1 homolog (DLK1) were measured in affected individuals. Expression of DLK1 was measured in mouse hypothalamus and in kisspeptin-secreting neuronal cell lines in vitro.Setting:Endocrine clinic of an academic medical center.Patients:Patients with familial CPP were studied.Results:A complex defect of DLK1 (∼14-kb deletion and 269-bp duplication) was identified in this family. This deletion included the 5′ untranslated region and the first exon of DLK1, including the translational start site. Only family members who inherited the defect from their father have precocious puberty, consistent with the known imprinting of DLK1. The patients did not demonstrate additional features of the imprinted disorder Temple syndrome except for increased fat mass. Serum DLK1 levels were undetectable in all affected individuals. Dlk1 was expressed in mouse hypothalamus and in kisspeptin neuron-derived cell lines.Conclusion:We identified a genomic defect in DLK1 associated with isolated familial CPP. MKRN3 and DLK1 are both paternally expressed imprinted genes. These findings suggest a role of genomic imprinting in regulating the timing of human puberty.Through a combination of linkage analysis and whole genome-sequencing, a mutation in the paternally expressed imprinted gene DLK1 in a family with central precocious puberty is identified.

Background Lipids play a critical role in defense against sepsis. We sought to investigate gene expression and lipidomic patterns of lipid dysregulation in sepsis. Methods Data from four adult sepsis studies were analyzed and findings were investigated in two external datasets. Previously characterized lipid dysregulation subphenotypes of hypolipoprotein (HYPO; low lipoproteins, increased mortality) and normolipoprotein (NORMO; higher lipoproteins, lower mortality) were studied. Leukocytes collected within 24 h of sepsis underwent RNA sequencing (RNAseq) and shotgun plasma lipidomics was performed. Results Of 288 included patients, 43% were HYPO and 57% were NORMO. HYPO patients exhibited higher median SOFA scores (9 vs 5, p  = < 0.001), vasopressor use (67% vs 34%, p  = < 0.001), and 28-day mortality (30% vs 16%, p  = 0.004). Leukocyte RNAseq identified seven upregulated lipid metabolism genes in HYPO ( PCSK9, DHCR7, LDLR, ALOX5, PLTP, FDFT1 , and MSMO1 ) vs. NORMO patients. Lipidomics revealed lower cholesterol esters (CE, adjusted p  = < 0.001), lysophosphatidylcholines (LPC, adjusted p  = 0.001), and sphingomyelins (SM, adjusted p  = < 0.001) in HYPO patients. In HYPO patients, DHCR7 expression strongly correlated with reductions in CE, LPC, and SM ( p  < 0.01), while PCSK9, MSMO1, DHCR7, PLTP, and LDLR upregulation were correlated with low LPC ( p  < 0.05). DHCR7, ALOX5 , and LDLR correlated with reductions in SM ( p  < 0.05). Mortality and phenotype comparisons in two external datasets (N = 824 combined patients) corroborated six of the seven upregulated lipid genes ( PCSK9, DHCR7, ALOX5, PLTP, LDLR, and MSMO1 ). Conclusion We identified a genetic lipid dysregulation signature characterized by seven lipid metabolism genes. Five genes in HYPO sepsis patients most strongly correlated with low CE, LPC, and SMs that mediate cholesterol storage and innate immunity.

Clinicians and researchers must contextualize a patient’s genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care. Three of the nation’s leading children’s hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype–phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

PurposeClinicians and researchers must contextualize a patient’s genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care.MethodsThree of the nation’s leading children’s hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model.ResultsHarmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype–phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications.ConclusionsThe GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

BACKGROUND: Turner syndrome (TS) results from a complete or partial loss of the second X chromosome and affects 25-50 per 100,000 females. TS is common in females with unexplained short stature, but the diagnosis is often not made until late childhood (8-9 years) if classic features are not present or recognized in infancy. This results in delayed medical intervention and screening for comorbid conditions. The aim of our study was to determine if an electronic health record (EHR) notification model improves the timing and rate of detection of TS. METHODS/RESULTS: A search of the EHR was performed to identify a cohort of females with idiopathic short stature (ISS) who were seen in the endocrine clinic from 2012-2017. Selection criteria included a height ≤ -2 SD, BMI > 5%-ile, absence of chronic illness, and presence of mid-parental height (MPH) data, which yielded 216 patients. Given that a height deflection from MPH %-ile is one of the better predictors of TS1, we focused on those ≥ 1 SD below MPH %-ile. Of these 189 patients, 72 (38%) hadn’t received prior genetic testing. This group formed our study population and microarray analysis was performed on available samples to assess for undiagnosed TS or other chromosomal abnormalities. A total of 39 patient samples were prospectively recruited or obtained from an IRB approved biobank (including 5 TS controls). Microarray data was generated for 37 samples, as 2 had an insufficient quantity of DNA, and data was manually analyzed by a cytogeneticist. We identified two cases of undiagnosed TS (6%) and one with a different chromosomal abnormality (3%). One of these patients returned to endocrine clinic prior to microarray analysis and had a karyotype of 45,X/46,XY. The two other chromosomal abnormalities were picked up by microarray. One had mosaic monosomy X (45,X/46,XX). The other had a 2.7 Mb deletion from chromosome 1 (1q25.3->1q31.1) and a 2.1 Mb duplication from 22q11.21, which has been associated with short stature. CONCLUSIONS: The EHR algorithm was effective at identifying patients at risk for TS that merit genetic testing. Of the ISS females ≥ 1 SD below their MPH %-ile, 38% never had a karyotype done and on further investigation two new cases of TS (6%) and one other chromosomal abnormality (3%) were found. Although not all patients had DNA available for microarray analysis, if we assume that all other cases were negative, the rate of undiagnosed TS would still approach 3%. Extrapolating this data to a larger scale suggests that the diagnosis of TS may be delayed for many females with ISS, even after evaluation in endocrine clinics. We recommend the implementation of additional tools in the clinic workflow, such as EHR alerts based on specific growth parameters, to increase clinical suspicion and testing for TS. REFERENCES: 1. Grote, FK, et al (2008). Developing evidence-based guidelines for referral for short stature. Archives of Disease in Childhood,93(3), 212-217.

Background: Short stature is a common reason for referral to a pediatric endocrinologist. Despite adequate evaluation, a pathological diagnosis is not identified in the vast majority of patients. It is often difficult to determine which of these many patients have an undiagnosed genetic cause. The electronic health record (EHR) improves our ability to identify cohorts of patients with a specific phenotype of interest, and it has become a valued tool for the augmentation of clinical characterization to drive discovery of phenotype-genotype associations. The aim of our study was to assess the feasibility of using dense phenotypic information from the EHR to systematically identify a distinct cohort of patients with short stature who have a high probability of harboring a monogenic etiology for their short stature. Methods/Results: As an initial proof of principle, we chose to focus on patients with Insulin-like growth factor I (IGF-I) resistance. We performed a targeted bioinformatics search of the EHR at three leading pediatric endocrine departments (Boston Children’s Hospital (BCH), Children’s Hospital of Philadelphia (CHOP) and Cincinnati Children’s Hospital Medical Center (CCHMC)). Inclusion criteria were height below -2 SD (for age and sex) and an IGF-I level above the 90thpercentile. Patients with known underlying genetic conditions, other chronic illnesses, or precocious puberty were excluded. All eligible patients were approached by mail or telephone to participate in the study. Whole exome sequencing (WES) was performed on DNA extracted from whole blood or saliva samples from ten patients and their immediate family members. The targeted bioinformatics search identified a total of 234 patients (104 at CCHMC, 64 at CHOP and 62 in BCH). Of these, 39 patients were eligible for recruitment and 10 were successfully recruited for genetic testing. WES identified two novel pathogenic variants in IGF1R including a novel missense variant (p.Val1013Phe) and a maternally inherited single amino acid deletion (p.Thr28del) in two patients. In addition, a third patient was found to have a novel missense variant in CHD2 (p.Val540Phe). Functional analyses confirmed the pathogenicity of the p.Val1013Phe variant, and are ongoing for the other two variants. Conclusion: This novel approach combining bioinformatics and modern genetics successfully led to the identification of a genetic etiology in 3 of 10 patients, giving a yield of 30% including identification of two patients with novel pathogenic mutations in IGFIR. Notably, these patients were otherwise missed clinically as having a genetic condition. Similar algorithms can be designed to identify diverse cohorts of patients likely to have genetic conditions. This approach can be integrated into the EHR to generate best practice advisories to advance our ability to diagnose rare genetic conditions and improve patient care.

The overarching goal of this exploratory study is to link subcellular microdomain specific protein-protein interactomes with big data analytics. We isolated postsynaptic density-95 (PSD-95) complexes from four human brain regions and compared their protein interactomes using multiple bioinformatics techniques. We demonstrate that human brain regions have unique postsynaptic protein signatures that may be used to interrogate perturbagen databases. Assessment of our hippocampal signature using the iLINCS database yielded several compounds with recently characterized “off target” effects on protein-protein interactions in the posynaptic density compartment.