Nanopore-based DNA long-read sequencing analysis of the aged human brain.

Aging disrupts cellular processes such as DNA repair and epigenetic control, leading to a gradual buildup of genomic alterations that can have detrimental effects in post-mitotic cells. Genomic alterations in regions of the genome that are rich in repetitive sequences, often termed "dark loci," are difficult to resolve using traditional sequencing approaches. New long-read technologies offer promising avenues for exploration of previously inaccessible regions of the genome. Using nanopore-based long-read whole-genome sequencing of DNA extracted from aged 18 human brains, we identify previously unreported structural variants and methylation patterns within repetitive DNA, focusing on transposable elements ("jumping genes") as crucial sources of variation, particularly in dark loci. Our analyses reveal potential somatic insertion variants and provides DNA methylation frequencies for many retrotransposon families. We further demonstrate the utility of this technology for the study of these challenging genomic regions in brains affected by Alzheimer's disease and identify significant differences in DNA methylation in pathologically normal brains versus those affected by Alzheimer's disease. Highlighting the power of this approach, we discover specific polymorphic retrotransposons with altered DNA methylation patterns. These retrotransposon loci have the potential to contribute to pathology, warranting further investigation in Alzheimer's disease research. Taken together, our study provides the first long-read DNA sequencing-based analysis of retrotransposon sequences, structural variants, and DNA methylation in the aging brain affected with Alzheimer's disease neuropathology.

FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization.

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .

Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package.

Analyzing different omics data types independently is often too restrictive to allow for detection of subtle, but consistent, variations that are coherently supported based upon different assays. Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package ( https://bioconductor.org/packages/iNETgrate/ ) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging.

BACKGROUND: Alzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults. OBJECTIVE: To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome. METHODS: Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up). RESULTS: Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities. CONCLUSIONS: Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.

Senolytic therapy in mild Alzheimer's disease: a phase 1 feasibility trial.

Cellular senescence contributes to Alzheimer's disease (AD) pathogenesis. An open-label, proof-of-concept, phase I clinical trial of orally delivered senolytic therapy, dasatinib (D) and quercetin (Q), was conducted in early-stage symptomatic patients with AD to assess central nervous system (CNS) penetrance, safety, feasibility and efficacy. Five participants (mean age = 76 + 5 years; 40% female) completed the 12-week pilot study. D and Q levels in blood increased in all participants (12.7-73.5 ng ml-1 for D and 3.29-26.3 ng ml-1 for Q). In cerebrospinal fluid (CSF), D levels were detected in four participants (80%) ranging from 0.281 to 0.536 ml-1 with a CSF to plasma ratio of 0.422-0.919%; Q was not detected. The treatment was well-tolerated, with no early discontinuation. Secondary cognitive and neuroimaging endpoints did not significantly differ from baseline to post-treatment further supporting a favorable safety profile. CSF levels of interleukin-6 (IL-6) and glial fibrillary acidic protein (GFAP) increased (t(4) = 3.913, P = 0.008 and t(4) = 3.354, P = 0.028, respectively) with trending decreases in senescence-related cytokines and chemokines, and a trend toward higher Aß42 levels (t(4) = -2.338, P = 0.079). In summary, CNS penetrance of D was observed with outcomes supporting safety, tolerability and feasibility in patients with AD. Biomarker data provided mechanistic insights of senolytic effects that need to be confirmed in fully powered, placebo-controlled studies. ClinicalTrials.gov identifier: NCT04063124 .

"iNETgrate": integrating DNA methylation and gene expression data in a single gene network.

Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package (https://bioconductor.org/packages/iNETgrate/) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

FastKnock: An efficient next-generation approach to identify all knockout strategies for strain optimization.

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using three Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more useful and important practical solutions. The availability of all the solutions provides the opportunity to further characterize and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock.

Senolytic therapy to modulate the progression of Alzheimer's Disease (SToMP-AD) - Outcomes from the first clinical trial of senolytic therapy for Alzheimer's disease.

Cellular senescence has been identified as a pathological mechanism linked to tau and amyloid beta (Aß) accumulation in mouse models of Alzheimer's disease (AD). Clearance of senescent cells using the senolytic compounds dasatinib (D) and quercetin (Q) reduced neuropathological burden and improved clinically relevant outcomes in the mice. Herein, we conducted a vanguard open-label clinical trial of senolytic therapy for AD with the primary aim of evaluating central nervous system (CNS) penetrance, as well as exploratory data collection relevant to safety, feasibility, and efficacy. Participants with early-stage symptomatic AD were enrolled in an open-label, 12-week pilot study of intermittent orally-delivered D+Q. CNS penetrance was assessed by evaluating drug levels in cerebrospinal fluid (CSF) using high performance liquid chromatography with tandem mass spectrometry. Safety was continuously monitored with adverse event reporting, vitals, and laboratory work. Cognition, neuroimaging, and plasma and CSF biomarkers were assessed at baseline and post-treatment. Five participants (mean age: 76±5 years; 40% female) completed the trial. The treatment increased D and Q levels in the blood of all participants ranging from 12.7 to 73.5 ng/ml for D and 3.29-26.30 ng/ml for Q. D levels were detected in the CSF of four participants ranging from 0.281 to 0.536 ng/ml (t(4)=3.123, p=0.035); Q was not detected. Treatment was well-tolerated with no early discontinuation and six mild to moderate adverse events occurring across the study. Cognitive and neuroimaging endpoints did not significantly differ from baseline to post-treatment. CNS levels of IL-6 and GFAP increased from baseline to post-treatment (t(4)=3.913, p=008 and t(4)=3.354, p=0.028, respectively) concomitant with decreased levels of several cytokines and chemokines associated with senescence, and a trend toward higher levels of Aß42 (t(4)=-2.338, p=0.079). Collectively the data indicate the CNS penetrance of D and provide preliminary support for the safety, tolerability, and feasibility of the intervention and suggest that astrocytes and Aß may be particularly responsive to the treatment. While early results are promising, fully powered, placebo-controlled studies are needed to evaluate the potential of AD modification with the novel approach of targeting cellular senescence.

The Spectrum of Alzheimer-Type Pathology in Cognitively Normal Individuals.

BACKGROUND: The strongest risk factor for the development of Alzheimer's disease (AD) is age. The progression of Braak stage and Thal phase with age has been demonstrated. However, prior studies did not include cognitive status. OBJECTIVE: We set out to define normative values for Alzheimer-type pathologic changes in individuals without cognitive decline, and then define levels that would qualify them to be resistant to or resilient against these changes. METHODS: Utilizing neuropathology data obtained from the National Alzheimer's Coordinating Center (NACC), we demonstrate the age-related progression of Alzheimer-type pathologic changes in cognitively normal individuals (CDR = 0, n = 542). With plots generated from these data, we establish standard lines that may be utilized to measure the extent to which an individual's Alzheimer-type pathology varies from the estimated normal range of pathology. RESULTS: Although Braak stage and Thal phase progressively increase with age in cognitively normal individuals, the Consortium to Establish a Registry for Alzheimer's Disease neuritic plaque score and Alzheimer's disease neuropathologic change remain at low levels. CONCLUSION: These findings suggest that an increasing burden of neuritic plaques is a strong predictor of cognitive decline, whereas, neurofibrillary degeneration and amyloid-ß (diffuse) plaque deposition, both to some degree, are normal pathologic changes of aging that occur in almost all individuals regardless of cognitive status. Furthermore, we have defined the amount of neuropathologic change in cognitively normal individuals that would qualify them to be "resilient" against the pathology (significantly above the normative values for age, but still cognitively normal) or "resistant" to the development of pathology (significantly below the normative values for age).

Rapid-SL identifies synthetic lethal sets with an arbitrary cardinality.

The multidrug resistance of numerous pathogenic microorganisms is a serious challenge that raises global healthcare concerns. Multi-target medications and combinatorial therapeutics are much more effective than single-target drugs due to their synergistic impact on the systematic activities of microorganisms. Designing efficient combinatorial therapeutics can benefit from identification of synthetic lethals (SLs). An SL is a set of non-essential targets (i.e., reactions or genes) that prevent the proliferation of a microorganism when they are "knocked out" simultaneously. To facilitate the identification of SLs, we introduce Rapid-SL, a new multimodal implementation of the Fast-SL method, using the depth-first search algorithm. The advantages of Rapid-SL over Fast-SL include: (a) the enumeration of all SLs that have an arbitrary cardinality, (b) a shorter runtime due to search space reduction, (c) embarrassingly parallel computations, and (d) the targeted identification of SLs. Targeted identification is important because the enumeration of higher order SLs demands the examination of too many reaction sets. Accordingly, we present specific applications of Rapid-SL for the efficient targeted identification of SLs. In particular, we found up to 67% of all quadruple SLs by investigating about 1% of the search space. Furthermore, 307 sextuples, 476 septuples, and over 9000 octuples are found for Escherichia coli genome-scale model, iAF1260.

Integrin alpha 6 is upregulated and drives hepatocellular carcinoma progression through integrin α6ß4 complex.

Integrin α6 (ITGA6) forms integrin receptors with either integrin ß1 (ITGB1) or integrin ß4 (ITGB4). How it functions to regulate hepatocellular carcinoma (HCC) progression is not well-elucidated. We found that ITGA6 RNA and protein expression levels are significantly elevated in human HCC tissues in comparison with paired adjacent nontumor tissues by RNA sequencing, RT-qPCR, Western blotting and immunofluorescence staining. Stable knockdown of ITGA6 with different ITGA6 shRNA expression lentivectors significantly inhibited proliferation, migration and anchorage-independent growth of HCC cell lines in vitro, and xenograft tumor growth in vivo. The inhibition of anchorage-dependent and -independent growth of HCC cell lines was also confirmed with anti-ITGA6 antibody. ITGA6 knockdown was shown to induce cell-cycle arrest at G0/G1 phase. Immunoprecipitation assay revealed apparent interaction of ITGA6 with ITGB4, but not ITGB1. Expression studies showed that ITGA6 positively regulates the expression of ITGB4 with no or negative regulation of ITGB1 expression. Finally, while high levels of ITGA6 and ITGB4 together were associated with significantly worse survival of HCC patients in TCGA data set, the association was not significant for high levels of ITGA6 and ITGB1. In conclusion, ITGA6 is upregulated in HCC tumors and has a malignant promoting role in HCC cells through integrin α6ß4 complex. Thus, integrin α6ß4 may be a therapeutic target for treating patients with HCC.

Blood biomarkers for cognitive decline and clinical progression in a Mexican American cohort.

Introduction: The clinical translation of biofluid markers for dementia requires validation in diverse cohorts. The study goal was to evaluate if blood biomarkers reflecting diverse pathophysiological processes predict disease progression in Mexican American adults. Methods: Mexican American adults (n = 745), 50 years of age and older, completed annual assessments over a mean of 4 years. Serum collected at baseline was assayed for total tau, neurofilament light (NFL), ubiquitin carboxyl-terminal hydrolase LI, glial fibrillary acidic protein (GFAP), soluble cluster of differentiation 14 (sCD14), and chitinase-3-like protein 1 (YKL-40). Results: Higher GFAP and NFL were associated with global cognitive decline. Only GFAP was associated with increased incident dementia risk (hazard ratio: 1.611 (95% confidence interval: 1.204-2.155)) and inclusion of additional biomarkers did not improve model fit. Discussion: Among a panel of six blood biomarkers previously associated with neurodegenerative disease, only GFAP predicted incident dementia in our cohort. The findings suggest that blood GFAP levels may aid dementia-risk prediction among Mexican American adults.

Differential protein expression in the hippocampi of resilient individuals identified by digital spatial profiling.

Clinical symptoms correlate with underlying neurodegenerative changes in the vast majority of people. However, an intriguing group of individuals demonstrate neuropathologic changes consistent with Alzheimer disease (AD) yet remain cognitively normal (termed "resilient"). Previous studies have reported less overall neuronal loss, less gliosis, and fewer comorbidities in these individuals. Herein, NanoString GeoMx™ Digital Spatial Profiler (DSP) technology was utilized to investigate protein expression differences comparing individuals with dementia and AD neuropathologic change to resilient individuals. DSP allows for spatial analysis of protein expression in multiple regions of interest (ROIs) on formalin-fixed paraffin-embedded sections. ROIs in this analysis were hippocampal neurofibrillary tangle (NFT)-bearing neurons, non-NFT-bearing neurons, and their immediate neuronal microenvironments. Analyses of 86 proteins associated with CNS cell-typing or known neurodegenerative changes in 168 ROIs from 14 individuals identified 11 proteins displaying differential expression in NFT-bearing neurons of the resilient when compared to the demented (including APP, IDH1, CD68, GFAP, SYP and Histone H3). In addition, IDH1, CD68, and SYP were differentially expressed in the environment of NFT-bearing neurons when comparing resilient to demented. IDH1 (which is upregulated under energetic and oxidative stress) and PINK1 (which is upregulated in response to mitochondrial dysfunction and oxidative stress) both displayed lower expression in the environment of NFT-bearing neurons in the resilient. Therefore, the resilient display less evidence of energetic and oxidative stress. Synaptophysin (SYP) was increased in the resilient, which likely indicates better maintenance of synapses and synaptic connections. Furthermore, neurofilament light chain (NEFL) and ubiquitin c-terminal hydrolase (Park5) were higher in the resilient in the environment of NFTs. These differences all suggest healthier intact axons, dendrites and synapses in the resilient. In conclusion, resilient individuals display protein expression patterns suggestive of an environment containing less energetic and oxidative stress, which in turn results in maintenance of neurons and their synaptic connections.

The landscape of human tissue and cell type specific expression and co-regulation of senescence genes.

BACKGROUND: Cellular senescence is a complex stress response that impacts cellular function and organismal health. Multiple developmental and environmental factors, such as intrinsic cellular cues, radiation, oxidative stress, oncogenes, and protein accumulation, activate genes and pathways that can lead to senescence. Enormous efforts have been made to identify and characterize senescence genes (SnGs) in stress and disease systems. However, the prevalence of senescent cells in healthy human tissues and the global SnG expression signature in different cell types are poorly understood. METHODS: This study performed an integrative gene network analysis of bulk and single-cell RNA-seq data in non-diseased human tissues to investigate SnG co-expression signatures and their cell-type specificity. RESULTS: Through a comprehensive transcriptomic network analysis of 50 human tissues in the Genotype-Tissue Expression Project (GTEx) cohort, we identified SnG-enriched gene modules, characterized SnG co-expression patterns, and constructed aggregated SnG networks across primary tissues of the human body. Our network approaches identified 51 SnGs highly conserved across the human tissues, including CDKN1A (p21)-centered regulators that control cell cycle progression and the senescence-associated secretory phenotype (SASP). The SnG-enriched modules showed remarkable cell-type specificity, especially in fibroblasts, endothelial cells, and immune cells. Further analyses of single-cell RNA-seq and spatial transcriptomic data independently validated the cell-type specific SnG signatures predicted by the network analysis. CONCLUSIONS: This study systematically revealed the co-regulated organizations and cell type specificity of SnGs in major human tissues, which can serve as a blueprint for future studies to map senescent cells and their cellular interactions in human tissues.

Molecular characterization of depression trait and state.

Major depressive disorder (MDD) is a brain disorder often characterized by recurrent episode and remission phases. The molecular correlates of MDD have been investigated in case-control comparisons, but the biological alterations associated with illness trait (regardless of clinical phase) or current state (symptomatic and remitted phases) remain largely unknown, limiting targeted drug discovery. To characterize MDD trait- and state-dependent changes, in single or recurrent depressive episode or remission, we generated transcriptomic profiles of subgenual anterior cingulate cortex of postmortem subjects in first MDD episode (n = 20), in remission after a single episode (n = 15), in recurrent episode (n = 20), in remission after recurring episodes (n = 15) and control subject (n = 20). We analyzed the data at the gene, biological pathway, and cell-specific molecular levels, investigated putative causal events and therapeutic leads. MDD-trait was associated with genes involved in inflammation, immune activation, and reduced bioenergetics (q < 0.05) whereas MDD-states were associated with altered neuronal structure and reduced neurotransmission (q < 0.05). Cell-level deconvolution of transcriptomic data showed significant change in density of GABAergic interneurons positive for corticotropin-releasing hormone, somatostatin, or vasoactive-intestinal peptide (p < 3 × 10-3). A probabilistic Bayesian-network approach showed causal roles of immune-system-activation (q < 8.67 × 10-3), cytokine-response (q < 4.79 × 10-27) and oxidative-stress (q < 2.05 × 10-3) across MDD-phases. Gene-sets associated with these putative causal changes show inverse associations with the transcriptomic effects of dopaminergic and monoaminergic ligands. The study provides first insights into distinct cellular and molecular pathologies associated with trait- and state-MDD, on plasticity mechanisms linking the two pathologies, and on a method of drug discovery focused on putative disease-causing pathways.

HspB1 Overexpression Improves Life Span and Stress Resistance in an Invertebrate Model.

To explore the role of the small heat shock protein beta 1 (HspB1, also known as Hsp25 in rodents and Hsp27 in humans) in longevity, we created a Caenorhabiditis elegans model with a high level of ubiquitous expression of the naked mole-rat HspB1 protein. The worms showed increased life span under multiple conditions and also increased resistance to heat stress. RNAi experiments suggest that HspB1-induced life extension is dependent on the transcription factors skn-1 (Nrf2) and hsf-1 (Hsf1). RNAseq from HspB1 worms showed an enrichment in several skn-1 target genes, including collagen proteins and lysosomal genes. Expression of HspB1 also improved functional outcomes regulated by SKN-1, specifically oxidative stress resistance and pharyngeal integrity. This work is the first to link a small heat shock protein with collagen function, suggesting a novel role for HspB1 as a hub between canonical heat response signaling and SKN-1 transcription.

DNA methylation analysis improves the prognostication of acute myeloid leukemia.

Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low-, intermediate-, and high-risk groups. The identified high-risk group had significantly shorter overall survival compared to the low-risk group (p-value ≤10-11). Specifically, our approach identified a particular subgroup of nine high-risk AML cases that died within 2 years after diagnosis. These high-risk cases otherwise would be incorrectly classified as intermediate-risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect.

Blood biomarkers for dementia in Hispanic and non-Hispanic White adults.

INTRODUCTION: The study evaluated if blood markers reflecting diverse biological pathways differentiate clinical diagnostic groups among Hispanic and non-Hispanic White adults. METHODS: Within Hispanic (n = 1193) and non-Hispanic White (n = 650) participants, serum total tau (t-tau), neurofilament light (NfL), ubiquitin carboxyl-terminal hydrolase LI, glial fibrillary acidic protein (GFAP), soluble cluster of differentiation-14, and chitinase-3-like protein 1 (YKL-40) were quantified. Mixed-effects partial proportional odds ordinal logistic regression and linear mixed-effects models were used to evaluate the association of biomarkers with diagnostic group and cognition, adjusting for age, sex, ethnicity, apolipoprotein E ε4, education, and site. RESULTS: T-tau, NfL, GFAP, and YKL-40 discriminated between diagnostic groups (receiver operating curve: 0.647-0.873). Higher t-tau (odds ratio [OR] = 1.671, 95% confidence interval [CI] = 1.457-1.917, P < .001), NfL (OR = 2.150, 95% CI = 1.819-2.542, P < .001), GFAP (OR = 2.283, 95% CI = 1.915-2.722, P < .001), and YKL-40 (OR = 1.288, 95% CI = 1.125-1.475, P < .001) were associated with increased likelihood of dementia relative to cognitively unimpaired and mild cognitive impairment groups. Higher NfL was associated with poorer global cognition (ß = -0.455, standard error [SE] = 0.083, P < .001), semantic fluency (ß = -0.410, SE = 0.133, P = .002), attention/processing speed (ß = 2.880, SE = 0.801, P < .001), and executive function (ß = 5.965, SE = 2.037, P = .003). Higher GFAP was associated with poorer global cognition (ß = -0.345, SE = 0.092, P = .001), learning (ß = -1.426, SE = 0.359, P < .001), and memory (ß = -0.890, SE = 0.266, P < .001). Higher YKL-40 (ß = -0.537, SE = 0.186, P = .004) was associated with lower memory scores. Interactions with ethnicity were observed for learning (NfL, GFAP, YKL-40), memory (NfL, GFAP), and semantic fluency (NfL; interaction terms P < .008), which were generally no longer significant in a demographically matched subset of Hispanic and non-Hispanic White participants. DISCUSSION: Blood biomarkers of neuronal/axonal and glial injury differentiated between clinical diagnostic groups in a bi-ethnic cohort of Hispanic and non-Hispanic Whites. Our results add to the growing literature indicating that blood biomarkers may be viable tools for detecting neurodegenerative conditions and highlight the importance of validation in diverse cohorts.

Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology.

Senescent cells contribute to pathology and dysfunction in animal models1. Their sparse distribution and heterogenous phenotype have presented challenges for detecting them in human tissues. We developed a senescence eigengene approach to identify these rare cells within large, diverse populations of postmortem human brain cells. Eigengenes are useful when no single gene reliably captures a phenotype, like senescence; they also help to reduce noise, which is important in large transcriptomic datasets where subtle signals from low-expressing genes can be lost. Each of our eigengenes detected ~2% senescent cells from a population of ~140,000 single nuclei derived from 76 postmortem human brains with various levels of Alzheimer's disease (AD) pathology. More than 97% of the senescent cells were excitatory neurons and overlapped with tau-containing neurofibrillary tangles (NFTs). Cyclin dependent kinase inhibitor 2D (CDKN2D/p19) was predicted as the most significant contributor to the primary senescence eigengene. RNAscope and immunofluorescence confirmed its elevated expression in AD brain tissue whereby p19-expressing neurons had 1.8-fold larger nuclei and significantly more cells with lipofuscin than p19-negative neurons. These hallmark senescence phenotypes were further elevated in the presence of NFTs. Collectively, CDKN2D/p19-expressing neurons with NFTs represent a unique cellular population in human AD with a senescence phenotype. The eigengenes developed may be useful in future senescence profiling studies as they accurately identified senescent cells in snRNASeq datasets and predicted biomarkers for histological investigation.

Two-Step Imputation and AdaBoost-Based Classification for Early Prediction of Sepsis on Imbalanced Clinical Data.

OBJECTIVES: Sepsis is a life-threatening response to infection that causes tissue damage, organ failure, and death. Effective early prediction of sepsis would improve patients' diagnosis and reduce the cost associated with late-stage sepsis infection by applying appropriate early intervention. However, effective early prediction is challenging because sepsis biomarkers are neither obvious nor definitive, and sepsis datasets are heavily imbalanced against positive diagnosis of sepsis while containing significant missing values. Early prediction of sepsis in ICUs using clinical data is the objective of the PhysioNet/Computing in Cardiology Challenge 2019. DESIGN: In this article, we proposed a machine learning algorithm to aid in the early detection of sepsis. SETTING: We applied linear interpolation and implemented a sample weighted AdaBoost model to predict sepsis 6 hours before clinical diagnosis. PATIENTS: Medical data contains more than 40,000 patients gathered from three geographically distinct U.S. hospital systems that consisted of a combination of hourly vital sign, lab values, and static patient descriptions. INTERVENTIONS: The challenge metric, however, did not directly reward models for their generalizability across institutions. MEASUREMENTS AND MAIN RESULTS: The article is evaluated using a new metric called Utility Score that is defined as Official scoring criteria. Our approach was among the top 10% of entries to the Challenge on a hidden test set. CONCLUSIONS: Herein, we demonstrate that our proposed approach was the most effective of the Challenge entrants when such generalizability is explicitly accounted for in model evaluation.