Biology subjects

Chan, D. D.

Publications and source records attributed to Chan, D. D..

7 recordsLinked to original sources

bioRxiv2025

Computational Translation of Mouse Models of Osteoarthritis Predicts Human Disease

ObjectiveTranslation of biological insights from preclinical studies to human disease is a pressing challenge in biomedical research, including in osteoarthritis. Translatable Components Regression (TransComp-R) is a computational framework that has previously been used to synthesize preclinical and human OA data to identify biological pathways predictive of human disease conditions. We aimed to evaluate the translatability of two common murine models of post-traumatic osteoarthritis - surgical destabilization of the medial meniscus (DMM) and noninvasive anterior cruciate ligament rupture (ACLR) - to transcriptomics cartilage data from human OA outcomes. DesignTranscriptomics cartilage data of DMM and ACLR mouse and human data was acquired from Gene Expression Omnibus. TransComp-R was used to project human OA data into a mouse model (DMM or ACLR) principal component analysis space. The principal components (PCs) were regressed against human OA conditions using increasing complexity of linear regression models incorporating human demographic covariates of OA, sex, and age. Biological pathways of the mouse PCs that significantly stratified human OA and control groups were then interpreted using Gene Set Enrichment Analysis. ResultsFrom the TransComp-R model, we identified different enriched biological pathways across DMM and ACLR models. While PCs among the DMM models revealed pathways associated with cell signaling and metabolism, ACLR PCs represented immune function and cellular pathways associated with OA condition. The immune pathways presented in the ACLR further highlighted the potential relevance of the OA pathways observed in human conditions. ConclusionsThe ACLR mouse model more successfully predicted human OA conditions, particularly with the human control groups without a history of joint injury or disease. Cross-species translational approaches support the selection of preclinical models intended for therapeutic discovery and pathway analysis in humans.

systems biology↗

bioRxiv2025

Taxonomic Shifts Correlate to Serum Cytokines in an Antibiotics Model to Study the Murine Gut-Joint Axis

The gut microbiome interacts with many systems throughout the human body. Microbiome disruption reduces bone tissue mechanics but paradoxically slows osteoarthritis progression. The microbiome also mediates inflammatory and immune responses, including serum cytokines. Towards our long-term goal of studying how the gut microbiome interacts with synovial joint health and disease, we examined how antibiotics-induced changes to microbial taxa abundance associated to serum cytokine levels. Mice (n = 5+) were provided ad libitum access to water containing antibiotics (1 g/L neomycin, 1 g/L ampicillin, or 1 g/L ampicillin with 0.5 g/L neomycin) or control water from 5- to 16-weeks old, corresponding in skeletal development to [~]10 to [~]25 years in humans. At humane euthanasia, we collected cecum contents for 16S metagenomics and blood for serum cytokine quantification for comparison to control and among antibiotic groups. We used dimensional reduction techniques, multiomic integration, and correlation to discriminate antibiotic groups and identify specific relationships between high-abundance taxa and serum cytokines. Antibiotic treatment significantly lowered diversity, altered phylum relative abundance, and resulted in significant association with specific taxa. Dimensional reduction techniques and multiomic integration revealed distinct antibiotic-associated clusters based on genera relative abundance and cytokine serum concentration. Cytokines IL-6, MIP-1B, and IL-10 significantly contributed to antibiotic discrimination, significantly different among antibiotic treatments, and had significant correlations with specific taxa. Antibiotic treatment resulted in heterogenous response in gut microbiome and serum cytokines, allowing significant microbe-cytokine links to emerge. The relationships identified here will enable further investigation of the gut microbiomes role in modifying joint health and disease.

systems biology↗

bioRxiv2025

Rumenomics: Evaluation of rumen metabolites from healthy sheep identifies differentially produced metabolites across sex, age, and weight

BackgroundThe rumen harbors a diverse and dynamic microbiome vital in digesting vegetation into metabolic byproducts for energy and general biological function. Although previous studies have reported connections between the rumen and the overall health of the sheep, the exact biological process by which this occurs is not well understood. Therefore, our study aimed to quantify sheep rumen metabolites to determine if enriched biological pathways are differentiable across phenotypic features of sex, age, and weight. ResultsWe collected and quantified metabolites of rumen samples from sixteen sheep using liquid chromatography-tandem mass spectrometry. We performed a series of univariate and multivariate statistical analyses to interpret the rumen metabolomics data. To identify metabolic pathways associated with the phenotypic features of sex, weight, and age, we used MetaboAnalyst, which identified amino acid metabolism as a distinguishing factor. Among the pathways, phenylalanine metabolism emerged as a key pathway differentiating sheep based on sex and age. Additionally, phenylalanine, tyrosine, and tryptophan biosynthesis were exclusively associated with age. In univariate linear models, we also discovered that these amino acid and protein pathways were associated with weight by age-corrected effect. Finally, we identified arginine and proline biosynthesis as a pathway linked to metabolites with weight. ConclusionOur study identified differential pathways based on the sex, age, and weight features of sheep. Metabolites produced by the rumen may act as an indicator for sheep health and other ruminants. These findings encourage further investigation of the differentially produced metabolites to assess overall sheep health.

systems biology↗

bioRxiv2024

A familial, telomere-to-telomere reference for human de novo mutation and recombination from a four-generation pedigree

Using five complementary short- and long-read sequencing technologies, we phased and assembled >95% of each diploid human genome in a four-generation, 28-member family (CEPH 1463) allowing us to systematically assess de novo mutations (DNMs) and recombination. From this family, we estimate an average of 192 DNMs per generation, including 75.5 de novo single-nucleotide variants (SNVs), 7.4 non-tandem repeat indels, 79.6 de novo indels or structural variants (SVs) originating from tandem repeats, 7.7 centromeric de novo SVs and SNVs, and 12.4 de novo Y chromosome events per generation. STRs and VNTRs are the most mutable with 32 loci exhibiting recurrent mutation through the generations. We accurately assemble 288 centromeres and six Y chromosomes across the generations, documenting de novo SVs, and demonstrate that the DNM rate varies by an order of magnitude depending on repeat content, length, and sequence identity. We show a strong paternal bias (75-81%) for all forms of germline DNM, yet we estimate that 17% of de novo SNVs are postzygotic in origin with no paternal bias. We place all this variation in the context of a high-resolution recombination map ([~]3.5 kbp breakpoint resolution). We observe a strong maternal recombination bias (1.36 maternal:paternal ratio) with a consistent reduction in the number of crossovers with increasing paternal (r=0.85) and maternal (r=0.65) age. However, we observe no correlation between meiotic crossover locations and de novo SVs, arguing against non-allelic homologous recombination as a predominant mechanism. The use of multiple orthogonal technologies, near-telomere-to-telomere phased genome assemblies, and a multi-generation family to assess transmission has created the most comprehensive, publicly available "truth set" of all classes of genomic variants. The resource can be used to test and benchmark new algorithms and technologies to understand the most fundamental processes underlying human genetic variation.

genomics↗

bioRxiv2024

Multiscale Correlations between Joint and Tissue-Specific Biomechanics and Anatomy in Postmortem Ovine Stifles

The stability of the knee joint is an important indicator of its overall health and function. Joint stability depends on multiple structural and functional properties that include the anatomy of the underlying bones, the geometry and stiffness of the joint capsule and the soft tissues within like tendon, ligaments, cartilage and meniscus. The multiscale biomechanical relationships between the form and function of the whole joint and individual tissues can provide useful insights on the physiological condition of the knee and require further exploration. To better understand these relationships, in this study we compare multiple structural and mechanical parameters in healthy ovine stifles (n = 6). Specifically, we have evaluated joint laxity, joint morphology, individual tissue T2* relaxation and mechanical properties of the anterior and posterior cruciate ligaments (ACL, PCL), medial and lateral collateral ligaments (MCL, LCL), the patellar tendon, menisci, and cartilage from the femoral condyles. Using mechanical testing at two length scales along with anatomical and quantitative magnetic resonance imaging (MRI) scans, we investigate the correlation between individual tissue and whole joint mechanical properties. We also performed statistical tests to measure the strength of correlation coefficients between the measured metrics at multiple levels among whole joint mechanics, joint size, and individual tissue properties. We observe positive correlations between the joint laxity forces and the epicondyle-to-epicondyle distance measured as an anatomical marker. We also find that the viscoelastic properties of the tendons and ligaments correlate positively with joint laxity forces. No such correlations were observed between the cartilage and meniscus properties and the joint laxity forces. Further, we found a weak inverse correlation between the tissue viscoelastic properties and T2* for MCL; strong and moderately positive correlations for cartilage samples from both femoral condyles and the menisci, LCL and PCL respectively. These results provide useful insights into the differential role of individual tissue properties that can be used to predict the whole joint responses that are key indicators of knee health and performance.

bioengineering↗

bioRxiv2024

Characterization of Composite Agarose-Collagen Hydrogels for Chondrocyte Culture

To elucidate the mechanisms of cellular mechanotransduction, it is necessary to employ biomaterials that effectively merge biofunctionality with appropriate mechanical characteristics. Agarose and collagen separately are common biopolymers used in cartilage mechanobiology and mechanotransduction studies but lack features that make them ideal for functional engineered cartilage. In this study, agarose (8% w/v and 4% w/v) is blended with collagen type I (4mg/mL) to create composites. We hypothesized that a higher stiffness, composite hydrogel would promote native cartilage-like conditions. To address these questions, acellular and cell-laden studies were completed to assess rheologic and compressive properties, contraction, and structural homogeneity in addition to matrix mechanics, cell proliferation, and glycosaminoglycan production. Over 21 days in culture, cellular 4% agarose - 2mg/mL collagen I hydrogels displayed good structural and bulk mechanical properties, cell proliferation, and continual glycosaminoglycan production, indicating promise towards the development of an effective hydrogel for chondrocyte mechanotransduction and mechanobiology studies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/583023v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@133f6aborg.highwire.dtl.DTLVardef@876594org.highwire.dtl.DTLVardef@11e05faorg.highwire.dtl.DTLVardef@1a9914_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Intervertebral Disc Elastography to Relate Shear Modulus and Relaxometry in Compression and Bending

Intervertebral disc degeneration is the most recognized cause of low back pain, characterized by the decline of tissue structure and mechanics. Image-based mechanical parameters (e.g., strain, stiffness) may provide an ideal assessment of disc function that is lost with degeneration but unfortunately remains underdeveloped. Moreover, it is unknown whether strain or stiffness of the disc may be predicted by MRI relaxometry (e.g. T1 or T2), an increasingly accepted quantitative measure of disc structure. In this study, we quantified T1 and T2 relaxation times and in-plane strains using displacement-encoded MRI within the disc under physiological levels of compression and bending. We then estimated shear modulus in orthogonal image planes and compared these values to relaxation times and strains within regions of the disc. Intratissue strain depended on the loading mode, and shear modulus in the nucleus pulposus was typically an order of magnitude lower than the annulus fibrosis, except in bending, where the apparent stiffness depended on the loading. Relative shear moduli estimated from strain data derived under compression generally did not correspond with those from bending experiments, with no correlations in the sagittal plane and only 4 of 15 regions correlated in the coronal plane, suggesting that future inverse models should incorporate multiple loading conditions. Strain imaging and strain-based estimation of material properties may serve as imaging biomarkers to distinguish healthy and diseased discs. Additionally, image-based elastography and relaxometry may be viewed as complementary measures of disc structure and function to assess degeneration in longitudinal studies.

bioengineering↗