Biology subjects

Hood, L.

Publications and source records attributed to Hood, L..

At least 19 recordsLinked to original sources

bioRxiv2026

KRAKEN: A provenance-tracked knowledge graph for multiomic and wellness research

Existing general-purpose biomedical knowledge graphs tend to focus on disease mechanisms and drug repurposing, leaving multiomic and wellness-relevant content underrepresented. KRAKEN (Knowledge Research & Analysis Kit for Evidence Networks) addresses this gap by integrating existing graphs (including Translator KG Open, RTX-KG2, and ROBOKOP) with specialized sources such as RefMet, LIPID MAPS, NIH Common Data Elements, Polygenic Score Catalog, and derived wellness measures including biological age and biological BMI. The resulting graph spans ~15M nodes and ~113M edges across 62 entity types. KRAKEN adopts the Biolink Model as its semantic layer, ensuring compatibility with standardized resources emerging from the NIH NCATS Biomedical Data Translator program. A lightweight, modular build system rebuilds the full graph (including entity resolution), with peak memory consumption under 48 GB, and supports flexible inclusion or exclusion of sources, allowing the user to scope the graph to a domain of interest. Built-in analytical tools include multi-hop reasoning, subgraph extraction, text, vector and hybrid entity search, and enrichment analyses, all accessible through an interactive web interface, a REST API, and a Model Context Protocol server, the last enabling direct consumption by agentic and LLM-based systems. KRAKEN is freely available at https://app.krakenkg.com.

bioinformatics↗

bioRxiv2025

Identification and characterization of host-modulating effectors encoded by the Cluster F1 mycobacteriophage NormanBulbieJr

NormanBulbieJr (NBJ) is a temperate siphovirus isolated on the host Mycobacterium smegmatis mc2155 that encodes 102 gene products, 60 of which have no known function (NKF). Based on gene content, NBJ is classified as a Cluster F, Subcluster F1 phage and shares 70% of its encoded gene phamilies with Girr, another F1 mycobacteriophage that was recently analyzed in a genome-wide overexpression screen and found to encode 29 diverse gene products capable of inhibiting growth of M. smegmatis. Similar functional screens in other mycobacteriophages have uncovered a growing repertoire of diverse, phage-encoded bacterial growth inhibitors, providing prime candidates for dissecting novel bacterial-phage interactions. An arrayed overexpression library encoding all 102 genes was constructed and systematically screened using a plate-based cytotoxicity assay, identifying 29 genes that inhibit mycobacterial growth. Because mycobacteriophage genomes are also known to encode systems involved in phage-phage competition, we conducted additional phage defense assays for a subset of NBJ genes in our library. This analysis confirmed homotypic immunity by the predicted immunity repressor and identified gp45 as a second defense factor that protects against Cluster F phages and promotes NBJ lysogen stability. Additional mechanistic analyses indicate that gp45 functions in a secretion-dependent manner, acting after phage adsorption to reduce productive infection and early phage transcript accumulation. Escape mutant and bacterial two-hybrid analyses implicate Cluster F tape measure proteins as the likely target of gp45, consistent with disruption of an early step in phage genome delivery. Finally, we extended our analysis to explore the essentiality of all identified host-modulating genes in the NBJ life cycle, using CRISPR-enhanced recombineering to generate phage deletion mutants, revealing two host modulators that are critical for lytic growth. Conducted as part of the SEA-GENES undergraduate research consortium, this study adds to a growing functional genomics resource and provides new insights into the complex interactions between phage gene products and the mycobacterial host cell.

microbiology↗

bioRxiv2025

Multiomics Reveals Compartmentalized Immune Responses and Tissue-Vascular Signatures in Lyme Disease

Lyme disease (LD) is growing in incidence, with nearly 500,000 cases diagnosed annually in the United States. Despite treatment, some patients experience persistent symptoms. The immune mechanisms underlying LD remain poorly understood. We conducted a multiomic longitudinal analysis of 49 LD patients and matched controls, integrating plasma proteomics, metabolomics, PBMC immunophenotyping, and a meta-analysis of skin lesions. We identified compartmentalized immune responses in acute LD, with coordinated alterations in circulating plasma proteins and metabolites linked to endothelial barrier stability, metabolic reprogramming, and symptom severity, predominantly traced to tissue and vascular immune processes at the site of infection. In contrast, PBMCs remained largely quiescent, revealing a disconnect between localized tissue responses and systemic immunity. These findings provide novel insights into LD pathophysiology and highlight the potential for diagnostics leveraging tissue and vascular immune markers detectable in blood. They also provide a resource for biomarker discovery and predictive modeling to improve LD management.

immunology↗

bioRxiv2024

APOE Genotype and Biological Age Impact Inter-Omic Associations Related to Bioenergetics

Apolipoprotein E (APOE) modifies human aging; specifically, the {varepsilon}2 and {varepsilon}4 alleles are among the strongest genetic predictors of longevity and Alzheimers disease (AD) risk, respectively. However, detailed mechanisms for their influence on aging remain unclear. Herein, we analyzed inter-omic, context-dependent association patterns across APOE genotypes, sex, and health axes in 2,229 community-dwelling individuals to test APOE genotypes for variation in metabolites and metabolite-associations tied to a previously-validated metric of biological aging (BA) based on blood biomarkers. Our analysis, supported by validation in an independent cohort, identified top APOE-associated plasma metabolites as diacylglycerols, which were increased in {varepsilon}2-carriers and trended higher in {varepsilon}4-carriers compared to {varepsilon}3-homozygotes, despite the known opposing aging effects of the allele variants. Omics association patterns of {varepsilon}2-carriers and increased biological age were also counter-intuitively similar, displaying increased associations between insulin resistance markers and energy-generating pathway metabolites. These results provide an atlas of APOE-related omic associations and support the involvement of bioenergetic pathways in mediating the impact of APOE on aging.

systems biology↗

bioRxiv2024

Placental Network Differences Among Obstetric Syndromes Identified With An Integrated Multiomics Approach

The placenta is essential for a healthy pregnancy, and placental pathology can endanger both maternal and fetal health. Placental function is affected by dynamic, complex, and interconnected molecular, cellular, and environmental events; therefore, we need a systems biology approach to study disease in normal physiological placenta function. We use placental multiomics (short and bulk transcriptomics, untargeted metabolomics, and targeted proteomics) paired with clinical data and placental histopathology reports from 321 placentas across multiple obstetric conditions: fetal growth restriction (FGR), FGR with pregnancy-related hypertension (FGR+HDP), preeclampsia (PE), and spontaneous preterm delivery (PTD). We first performed cellular deconvolution to estimate cell type numbers from bulk transcriptomes: FGR+HDP placentas were the most different from control placentas driven by a higher estimated number of extravillous trophoblast (p<0.0001). Next, we evaluated the impact of fetal sex and gestational age on analyte levels, adjusting for these confounders. We then generated obstetric condition-specific correlation networks and identified communities of related analytes associated with physiology and disease. We demonstrated how network connectivity and its disruption in disease can be used to identify signatures unique to a clinical outcome. We examined a community defined in control placentas for which the most connected node was miR-365a-3p in contrast to the corresponding community in FGR+HDP placentas for which the most connected node was hypoxia-induced miR-210-3p. From this community, we identified a signature containing mRNA transcripts implicated in placental dysfunction (e.g. FLT1, FSTL3, HTRA4, LEP, and PHYHIP). This signature distinguishes between FGR+HDP placentas and placentas of differing clinical outcomes in high-dimensional space. These findings illustrate the power of systems biology-driven interomics network analysis in a single tissue type, laying the groundwork for future multi-tissue studies.

systems biology↗

bioRxiv2024

Multi-omic ADNI CSF and plasma data integration identifies distinct metabolic transitions in disease progression in Alzheimer's Disease

Age and APOE genotype are the strongest known risk factors for late-onset Alzheimers disease (AD), but the mechanisms linking them to neuronal loss remain incompletely defined. Using multiomic data from the Alzheimers Disease Neuroimaging Initiative (ADNI), we propose a unified hypothesis in which two interdependent failure modes--saturation of microglial lipid flux capacity and disruption of the astrocyte-neuron lactate shuttle (ANLS) due to excess astrocytic membrane cholesterol--drive disease progression upstream of amyloid and tau pathology. Stratifying participants by cognitive score quartiles, we find consistent associations linking impaired lipid clearance, metabolic stress, and genetic variants regulating cholesterol handling. These processes appear to reinforce each other, resulting in accelerating neurodegeneration. Our hypothesis reframes AD as a systems-level collapse in metabolic coordination, rather than a purely linear pathological cascade. These insights emerged during the development of digital twin models for personalized interventions, highlighting the power of systems approaches to reveal hidden drivers of neurodegeneration.

neuroscience↗

bioRxiv2023

Generally-healthy individuals with aberrant bowel movement frequencies show enrichment for microbially-derived blood metabolites associated with impaired kidney function.

Bowel movement frequency (BMF) has been linked to changes in the composition of the human gut microbiome and to many chronic conditions, like metabolic disorders, neurodegenerative diseases, chronic kidney disease (CKD), and other intestinal pathologies like irritable bowel syndrome and inflammatory bowel disease. Lower BMF (constipation) can lead to compromised intestinal barrier integrity and a switch from saccharolytic to proteolytic fermentation within the microbiota, giving rise to microbially-derived toxins that may make their way into circulation and cause damage to organ systems. However, the connections between BMF, gut microbial metabolism, and the early-stage development and progression of chronic disease remain underexplored. Here, we examined the phenotypic impact of BMF variation in a cohort of generally-healthy, community dwelling adults with detailed clinical, lifestyle, and multi-omic data. We showed significant differences in microbially-derived blood plasma metabolites, gut bacterial genera, clinical chemistries, and lifestyle factors across BMF groups that have been linked to inflammation, cardiometabolic health, liver function, and CKD severity and progression. We found that the higher plasma levels of 3-indoxyl sulfate (3-IS), a microbially-derived metabolite associated with constipation, was in turn negatively associated with estimated glomerular filtration rate (eGFR), a measure of kidney function. Causal mediation analysis revealed that the effect of BMF on eGFR was significantly mediated by 3-IS. Finally, we identify self-reported diet, lifestyle, and psychological factors associated with BMF variation, which indicate several common-sense strategies for mitigating constipation and diarrhea. Overall, we suggest that aberrant BMF is an underappreciated risk factor in the development of chronic diseases, even in otherwise healthy populations.

systems biology↗

bioRxiv2022

Genome-microbiome interplay provides insight into the determinants of the human blood metabolome

Variation in the blood metabolome is intimately related to human health. Prior work has shown that host genetics and gut microbiome composition, combined, explain sizable, but orthogonal, components of the overall variance in blood metabolomic profiles. However, few details are known about the interplay between genetics and the microbiome in explaining variation on a metabolite-by-metabolite level. Here, we performed analyses of variance for each of the 945 blood metabolites that were robustly detected across a cohort of 2,049 individuals, while controlling for a number of relevant covariates, like sex, age, and genetic ancestry. Over 60% of the detected blood metabolites were significantly associated with either host genetics or the gut microbiome, with more than half of these associations driven solely by the microbiome and around 30% under hybrid genetic-microbiome control. The variances explained by genetics and the microbiome for each metabolite were indeed largely additive, although subtle, but significant, non-additivity was detected. We found that interaction effects, where a metabolitemicrobe association was specific to a particular genetic background, were quite common, albeit with modest effect sizes. The outputs of our integrated genetic-microbiome regression models provide novel biological insights into the processes governing the composition of the blood metabolome. For example, we found that unconjugated secondary bile acids were solely associated with the microbiome, while their conjugated forms were under strong host genetic control. Overall, our results reveal which components of the blood metabolome are under strong genetic control, which are more dependent on gut microbiome composition, and which are dependent upon both. This knowledge will help to guide targeted interventions designed to alter the composition of the blood metabolome.

systems biology↗

bioRxiv2021

Mouse Organ Specific Proteins and Functions

Organ specific proteins (OSPs) possess great medical potentials both in clinics and in biomedical research. Applications of them -- such as alanine transaminase, aspartate transaminase, and troponins -- in clinics have raised certain concerns of their organ specificity. The dynamics and diversity of protein expression in heterogeneous human population are well known, yet their effects on OSPs are less addressed. Here we use mouse as a model and implemented a scheme of breadth study to examine the pan-organ proteome for potential variations of organ specificity in different genetic backgrounds. Using reasonable resources, we generated pan-organ proteomes of four in-bred mouse strains. The results revealed a large diversity that is more profound among OSPs than the overall proteomes. We defined a robustness score to quantify such variation and derived three sets of OSPs with different stringencies. In the meantime, we found that the enriched biological functions of OSPs are also organ specific that are sensitive and useful to assess the quality of OSPs. We hope our breadth study can open doors to explore the molecular diversity and dynamics of organ specificity at the protein level.

genomics↗

bioRxiv2021

Manifestations of genetic risk for Alzheimer's Disease in the blood: a cross-sectional multi-omic analysis in healthy adults aged 18-90+

Deeply phenotyped cohort data can elucidate differences associated with genetic risk for common complex diseases across an age spectrum. Previous work has identified genetic variants associated with Alzheimers disease (AD) risk from large-scale genome-wide association study meta-analyses. To explore effects of known AD-risk variants, we performed a phenome-wide association study on ~2000 clinical, proteomic, and metabolic blood-based analytes obtained from 2,831 cognitively normal adult clients of a consumer-based scientific wellness company. Results uncovered statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE). These effects were detectable from early adulthood. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. Sex-stratified GWAS results from an independent AD case-control meta-analysis supported sexspecific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. These analyses support evidence from previous functional genomics studies in the identification of a causal variant within the PILRA gene. Taken together, this study highlights clues to the earliest effects of AD genetic risk variants in individuals where disease symptoms have not (yet) arisen.

genetics↗

bioRxiv2021

Weight loss response following lifestyle intervention associated with baseline gut metagenomic signature in humans

We report a weight-loss response analysis on a small cohort of individuals (N=25) selected from a larger population (N[~]5,000) enrolled in a commercial scientific wellness program, which included healthy lifestyle coaching. Each individual had baseline data on blood metabolomics, blood proteomics, clinical labs, lifestyle questionnaires, and stool metagenomes. A subset of these participants (N=15) lost at least 10% of their body weight within a 6-12 month period and saw significant improvement in metabolic health markers ( weight loss group), while another subset of individuals (N=10) undergoing the same lifestyle intervention showed no change in BMI over the same timeframe ( no weight loss group). Only a single baseline blood analyte, a metabolite linked to fried food consumption, was (negatively) associated with weight loss, but a large number of baseline stool metagenomic features, including complex polysaccharide and protein degradation genes, stress-response genes, respiration-related genes, cell wall synthesis genes, and gut bacterial replication rates, were significantly associated with weight loss after explicitly controlling for baseline BMI. Together, these results provide a set of baseline gut microbiome functional features that are associated with weight loss outcomes.

microbiology↗

bioRxiv2020

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8+ and CD4+ T cells, and cytotoxic CD4+ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

immunology↗

bioRxiv2020

Gut Microbiome Pattern Reflects Healthy Aging and Predicts Extended Survival in Humans

The gut microbiome has important effects on human health, yet its importance in human aging remains unclear. Using two independent cohorts comprising 4582 individuals across the adult lifespan we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique with age. This uniqueness pattern is strongly associated with gut microbial amino acid derivatives circulating within the bloodstream, many of which have been previously identified as longevity biomarkers. At the latest stages of human life, two distinct patterns emerge wherein individuals in good health show continued microbial drift toward a unique compositional state, while the same drift is absent in individuals who perform worse on a number of validated health measures. The identified healthy aging pattern is characterized by an overall depletion of core genera found across most humans - primarily a depletion in the nearly ubiquitous genus Bacteroides. Consistently, retaining a high Bacteroides dominance into extreme age, or, equivalently, having a low gut microbiome uniqueness score, predicts decreased survival in a four-year follow-up. Our comprehensive analysis identifies the gut microbiome as a novel component of healthy aging, with important implications for the worlds growing older population.

systems biology↗

bioRxiv2019

Measurement of organ-specific and acute-phase blood protein levels in early Lyme disease

Lyme disease results from infection of humans with the spirochete Borrelia burgdorferi. The first and most common clinical manifestation is the circular, inflamed skin lesion referred to as erythema migrans; later manifestations result from infections of other body sites. Laboratory diagnosis of Lyme disease can be challenging in patients with erythema migrans because of the time delay in the development of specific diagnostic antibodies against Borrelia. Reliable blood biomarkers for the early diagnosis of Lyme disease in patients with erythema migrans are needed. Here, we performed selected reaction monitoring, a targeted mass spectrometry-based approach, to measure selected proteins that 1) are known to be predominantly expressed in one organ (i.e., organ-specific blood proteins) and whose blood concentrations may change as a result of Lyme disease, or 2) are involved in acute immune responses. In a longitudinal cohort of 40 Lyme disease patients and 20 healthy controls, we identified 10 proteins with significantly altered serum levels in patients at the time of diagnosis, and we also developed a 10-protein panel identified through multivariate analysis. In an independent cohort of patients with erythema migrans, six of these proteins, APOA4, C9, CRP, CST6, PGLYRP2 and S100A9, were confirmed to show significantly altered serum levels in patients at time of presentation. Nine of the 10 proteins from the multivariate panel were also verified in the second cohort. These proteins, primarily innate immune response proteins or proteins specific to liver, skin or white blood cells, may serve as candidate blood biomarkers requiring further validation to aid in the laboratory diagnosis of early Lyme disease.

systems biology↗

bioRxiv2019

MOBN: an interactive database of multi-omics biological networks

It is essential to reveal the associations between different omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics and metabolomics as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their own data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and diseases research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

bioinformatics↗

bioRxiv2019

Blood metabolome signature predicts gut microbiome α-diversity in health and disease

Defining a healthy gut microbiome has been a challenge in the absence of detailed information on both host health and microbiome composition. Here, we analyzed a multi-omics dataset from hundreds of individuals (discovery n=399, validation n=540) enrolled in a consumer scientific wellness program to identify robust associations between host physiology and gut microbiome structure. We attempted to predict gut microbiome -diversity from nearly 1000 analytes measured from blood, including clinical laboratory tests, proteomics and metabolomics. While a broad panel of 77 standard clinical laboratory tests and a set of 263 proteins from blood could not accurately predict gut microbial -diversity, we found that 45% of the variance in microbial community diversity was explained by a subset of 40 blood metabolites, many of microbial origin. This relationship between the host metabolome and gut microbiome -diversity was very robust, persisting across disease conditions and antibiotics use. Several of these novel metabolic biomarkers of gut microbial diversity were previously associated with host health (e.g. cardiovascular disease risk, diabetes, and kidney function). A subset of 11 metabolites classified participants with potentially problematic low -diversity (ROC AUC=0.88, Precision-Recall AUC=0.76). Relationships between host metabolites and -diversity remained consistent across most of the Body Mass Index (BMI) spectrum, but were modified in extreme obesity (class II/III, but not class I), suggesting a significant metabolic shift. Out-of-sample prediction accuracy of -diversity from the 40 identified blood metabolites in a validation cohort, whose microbiome samples were analyzed by a different vendor, confirmed the robust correspondence between gut microbiome structure and host physiology. Collectively, our results reveal a strong coupling between the human blood metabolome and gut microbial diversity, with implications for human health.

bioinformatics↗

bioRxiv2018

Mechanistic insights on metabolic dysfunction in PTSD: Role of glucocorticoid receptor sensitivity and energy deficit

PTSD is associated with metabolic comorbidities; however it is not clear how the neuroendocrine disturbances affect metabolism. To analyze this we employed a systems biological approach using an integrated mathematical model of metabolism, HPA axis and inflammation. We combined the metabolomics, neuroendocrine, clinical lab and cytokine data from combat-exposed veterans with and without PTSD, to characterize the differences in regulatory effects. We used the pattern of fold change in metabolites representing pathway level differences as reference for metabolic control analysis (MCA) using the model. MCA revealed parameters constituting the HPA axis, inflammation and GPCR pathway that yielded metabolic dysfunction consistent with PTSD. To support this, we performed causal analysis between regulatory components and the significantly different metabolites in our sample. Causal inference revealed that the changes in glucocorticoid receptor sensitivity were mechanistically associated with metabolic dysfunction and the effects were jointly mediated by insulin resistance, inflammation, oxidative stress and energy deficit.

systems biology↗

bioRxiv2018

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data Across 27 Tissue Types

There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting provides a means to predict genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we implemented a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow scans the footprints generated by these algorithms for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We applied our workflow to detect footprints in 192 DNase-seq experiments from ENCODE spanning 27 human tissues. This collection of footprints describes an expansive landscape of potential TF occupancy. At thresholds optimized through machine learning, we report high-quality footprints covering 9.8% of the human genome. These footprints were enriched for true positive TF binding sites as defined by ChIP-seq peaks, as well as for genetic variants associated with changes in gene expression. Integrating our footprint atlas with summary statistics from genome-wide association studies revealed that risk for neuropsychiatric traits was enriched specifically at highly-scoring footprints in human brain, while risk for immune traits was enriched specifically at highly-scoring footprints in human lymphoblasts. Our cloud-based workflow is available at github.com/globusgenomics/genomics-footprint and a database with all footprints and TF binding site predictions are publicly available at http://data.nemoarchive.org/other/grant/sament/sament/footprint_atlas.

bioinformatics↗