Biology subjects

Larson, N.

Publications and source records attributed to Larson, N..

6 recordsLinked to original sources

bioRxiv2026

nanoASM: Long-Read Allele-Specific DNA Methylation Profiling Enables Functional Annotation of Regulatory Noncoding Variants in Human Prostate Tissues

Long-read nanopore sequencing enables simultaneous detection of germline variation and native DNA base modifications on individual DNA molecules, providing a unique opportunity to investigate allele-specific epigenetic regulation. Here, we performed whole-genome nanopore sequencing on normal and tumor prostate tissues to characterize differential methylation, methylation entropy, and allele-specific methylation (ASM) associated with noncoding genetic variants. Genome-wide analysis identified extensive cancer-associated differentially methylated regions (DMRs), with hypermethylated DMRs significantly enriched near transcription start sites and transcriptional regulatory regions. Integration with transcriptomic datasets revealed strong inverse relationships between promoter methylation and gene expression, while 5-hydroxymethylcytosine (5hmC) levels positively correlated with transcriptional activity across gene bodies. Using fragment-level methylation patterns enabled by long-read sequencing, we further quantified methylation entropy incorporating both 5mCG and 5hmCG states. Cancer-hypermethylated DMRs exhibited markedly reduced entropy, consistent with clonal fixation of methylation states during tumor progression. Entropy profiling across chromatin annotations demonstrated maximal epigenetic heterogeneity at partially modified enhancer-associated regions. To investigate cis-regulatory genetic effects, we developed a simple ASM framework (nanoASM) that can partition sequencing reads by allelic state and identifies allele-specific DMRs directly from long-read data. Compared with conventional population-level mQTL analysis, ASM demonstrated substantially improved statistical efficiency by leveraging within-individual contrasts and reducing sample-level heterogeneity. Although germline single nucleotide polymorphisms (SNPs) were largely shared between normal and tumor tissues, ASM patterns differed substantially, with tumor-associated ASM regions displaying significantly larger genomic span and stronger allelic methylation differences. Comparative analysis with TCGA prostate mQTL and GTEx prostate eQTL datasets demonstrated substantial concordance between ASM directionality and downstream transcriptional effects, particularly for variants located within DMRs and near transcription start sites. At the IRX4 prostate cancer risk locus, ASM identified an androgen-responsive regulatory domain overlapping AR ChIP-seq and H3K27ac peaks, nominating rs6885084 as a candidate functional variant. At the PSCA locus, ASM anchored by rs4736369 was associated with allele-specific methylation, chromatin activation, transcript abundance, and isoform usage. Together, these findings establish nanopore-based ASM analysis as a powerful approach for resolving functional noncoding variants and their regulatory domains they control in prostate cancer.

genetics↗

bioRxiv2026

Biophysical Constraints Dictate the Stability of Social Traits in Pseudomonas aeruginosa Aggregates

The maintenance of cooperative behaviors within microbial populations remains an evolutionary puzzle, particularly in spatially structured environments like those found in chronic infection. In Pseudomonas aeruginosa, quorum sensing (QS) directs the production of costly public goods that are vulnerable to exploitation by non-producing cheaters. However, the biophysical mechanisms that stabilize this cooperation in complex environments remain poorly understood. Here, we elucidate how the biophysical properties of the bacterial cell surface govern the micron-scale architecture of bacterial aggregates to promote or constrain the stability of cooperation. Using a polymer-structured growth environment, we demonstrate that cell surface hydrophobicity acts as a primary determinant of spatial organization, where hydrophilic wild-type cells assemble into "stacked" aggregates, whereas hydrophobic O-specific antigen-deficient cells form dense "clumps". In mixed populations, distinct surface properties exhibit phase-separation-like immiscibility, segregating the cells at the micron scale. We show that this segregation directly suppresses cheater fitness by physically sequestering cooperative clusters. Specifically, hydrophobic QS-deficient cells were effectively excluded by hydrophilic QS cooperators, limiting their access to public goods. Furthermore, invasion experiments revealed that hydrophilic cells are inherently fitter, capable of unidirectionally invading populations with a hydrophobic cell surface and independent of social dynamics. Conversely, the need to gain access to nutrient resources can fine-tune these barriers, enabling hydrophilic QS-deficient cells to cluster proximally to hydrophobic cooperators. These findings establish that bacterial cell surface traits impact strongly on microbial social interactions and offer new insights into the maintenance and loss of cooperative traits in chronic infections such as those found in chronic lung infections. Significance StatementCooperative behaviors are essential for the survival of pathogenic bacteria like Pseudomonas aeruginosa, yet they are continually threatened by "cheater" cells that exploit shared public good resources. We demonstrate that the physical properties of the cell surface, specifically hydrophobicity, act as a key architect of microbial social structure. In polymer-rich environments, differences in surface hydrophobicity drive a distinct "oil-and-water" segregation between cell populations. This micron-scale phase separation physically sequesters cooperative cells, insulating them from exploitation by cheaters. Consequently, spatial structure serves as a biophysical stabilizer of cooperation, limiting the metabolic advantages typically held by cheaters. Our findings demonstrate that microbial social evolution is governed not only by genetic strategies, but by fundamental biophysical constraints that dictate the spatial limits of exploitation.

microbiology↗

bioRxiv2024

Fine Mapping Regulatory Variants by Characterizing Native CpG Methylation with Nanopore Long Read Sequencing

5-methylcytosine (5mC) is the most common chemical modification occurring on the CpG sites across the human genome. Bisulfite conversion combined with short-read whole genome sequencing can capture and quantify the modification at single nucleotide resolution. However, the PCR amplification process could lead to duplicative methylation patterns and introduce 5mC detection bias. Additionally, the limited read length also restricts co-methylation analysis between distant CpG sites. The bisulfite conversion process presents a significant challenge for detecting variant-specific methylation due to the destruction of allele information in the sequencing reads. To address these issues, we sought to characterize the human methylation profiling with the nanopore long-read sequencing, aiming to demonstrate its potential for long-range co-methylation analysis with native modification call and intact allele information retained. In this regard, we first analyzed the nanopore demo data in the adaptive sampling sequencing run targeting all human CpG islands. We applied the linkage disequilibrium (LD) R2 to calculate the co-methylation in nanopore data, and further identified 27,875, 50,481, 26,542 and 51,189 methylation haplotype blocks (MHB) in COLO829, COLO829BL, HCC1395 and HCC1395BL cell lines, respectively. Interestingly, while we found that majority of the co-methylation were in a short range ([≤]200bp), a small portion (1[~]3%) showed long distance ([≥]1,000bp), suggesting potential remote regulatory mechanisms across the genome. To further characterize the epigenetic changes related to transcription factor binding, we profiled the 5mC percentage changes surrounding various motif sites in JASPAR collection and found that CTCF and KLF5 binding sites showed reduced methylation, while FOXE1 and ZNF354A sites showed increased methylation. To further investigate the allele-specific 5mCG in the prostate genome, we designed a target region covering methylation quantitative trait loci (mQTL) and genome-wide association study (GWAS) risk germline variants and generated long reads with adaptive sampling run in the 22Rv1 cell line. To identify the allele-specific methylation in the 22Rv1 cell line, we performed long-read based phasing and compared the 5mCG signals between the two haplotypes. As a result, we identified 6,390 haplotype-specific methylated regions in the 22Rv1 cell line (p-MWU [≤] 1e-5 and delta [≥] 50%). By examining haplotype-specific methylated regions near the phasing variants, we identified examples of allele-specific methylated regions that showed allele-specific accessibility in the ATAC-seq data. By further integrating the ATAC-seq data of 22Rv1, we found that methylation levels were negatively correlated with chromatin accessibility at the genome-wide scale. Our study has revealed native methylome profiling while preserving haplotype information, offering a novel approach to uncovering the regulatory mechanisms of the human prostate genome.

genetics↗

bioRxiv2023

Leveraging genetic correlation structure to target discrete signaling mechanisms across metabolic tissues

Abstract/IntroductionInter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Beginning with the discovery of insulin over a century ago, characterization of molecules responsible for signal between tissues has required careful and elegant experimentation where these observations have been integral to deciphering physiology and disease. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. For example, physiologic dissection of the actions of soluble proteins such as proprotein convertase subtilisin/kexin type 9 (PCSK9) and glucagon-like peptide 1 (GLP1) have yielded among the most promising therapeutics to treat cardiovascular disease and obesity, respectively1-4. A major obstacle in the characterization of such soluble factors is that defining their tissues and pathways of action requires extensive experimental testing in cells and animal models. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by "brute-force" surveys of all genes within RNA-sequencing measures across tissues within a population5-9. Expanding on this intuition, we reasoned that parallel strategies could be used to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of gene variation between individuals. Thus, comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling. Here, we surveyed gene-gene correlation structure across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or HFHS diet. Variation of genes such as FGF21, ADIPOQ, GCG and IL6 showed enrichments which recapitulate experimental observations. Further, similar analyses were applied to explore both within-tissue signaling mechanisms (liver PCSK9) as well as genes encoding enzymes producing metabolites (adipose PNPLA2), where inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference of tissue-specific variation in relationships with metabolic traits. We refer to this resource as Gene-Derived Correlations Across Tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables querying of any gene in any tissue to find correlated patterns of genes, cell types, pathways and network architectures across metabolic organs.

physiology↗

bioRxiv2023

Rapid high-resolution size distribution analysis for adeno-associated virus using high speed SV-AUC

When optimized, sedimentation velocity analytical ultracentrifugation (SV-AUC) provides the most-accurate, broadest-range, and highest-resolution size distribution analysis of any method. Generating simulated data for an adeno-associated virus (AAV) sample consisting of four species differing only in their DNA content and having closely spaced sedimentation coefficients, allows manipulation of the SV-AUC experimental protocol to optimize the size distribution resolution. In developing this high speed SV-AUC (hs-SV-AUC) protocol several experimental challenges must be overcome: 1) the need for rapid data acquisition, 2) avoiding optical artifacts from steep boundaries and 3) overcoming the increased potential for convection. A protocol, hs-SV-AUC, has been developed that uses high rotor speeds, interference detection and low temperatures to overcome these challenges. By confining data analysis to a limited radial-time window and using a very short run time (< 20 min after temperature equilibration), the need to match the sample and reference solvent composition and meniscus positions is relaxed, making interference detection is as simple to employ as absorbance detection. Experimental size distributions from the same AAV sample by hs-SV-AUC at 45K rpm and 10 {degrees}C versus low-speed SV-AUC at 15K rpm, and 10 {degrees}C illustrates the improved size distribution resolution offered by the hs-SV-AUC protocol.

biophysics↗

bioRxiv2019

Robust Hierarchical Density Estimation and Regression for Re-stained Histological Whole Slide Image Co-registration

For many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be properly overlaid (i.e. registered) to identify co-localized features. Traditional image registration methods sometimes fail due to the high variation of cell density and insufficient texture information in WSIs - particularly at high magnifications. In this paper, we proposed a robust image registration strategy to align re-stained WSIs precisely and efficiently. This method is applied to 30 pairs of immunohistochemical (IHC) stains and their hematoxylin and eosin (H&E) counterparts. Our approach advances the existing methods in three key ways. First, we introduce refinements to existing image registration methods. Second, we present an effective weighting strategy using kernel density estimation to mitigate registration errors. Third, we account for the linear relationship across WSI levels to improve accuracy. Our experiments show significant decreases in registration errors when on matching IHC and H&E pairs, enabling subcellular-level analysis on stained and re-stained histological images. We also provide a tool to allow users to develop their own registration benchmarking experiments.

bioinformatics↗