Biology subjects

Goodrich, J. A.

Publications and source records attributed to Goodrich, J. A..

6 recordsLinked to original sources

bioRxiv2026

Circulating Microbial DNA as a Potential Cancer Biomarker: Technical Challenges and Controlled Evaluation

Abstract Background. Circulating microbial DNA (cmDNA) has been proposed as a non-invasive cancer biomarker, but most evidence comes from cancer-sequencing datasets not designed for microbial analysis and lacking contamination controls. Whether reported signatures reflect biology or artifact is unclear in low-biomass specimens, where standard taxonomic pipelines are prone to systematic error. Methods. In a tightly controlled pilot study of metastatic castration-resistant prostate cancer, we profiled plasma cell-free DNA (cfDNA) and buffy-coat genomic DNA (gDNA) from two patients and two healthy volunteers alongside mock blood-draw and reagent controls, each with and without host-DNA depletion. Reads were classified with Kraken2/Bracken and, independently, with the marker-gene classifier MetaPhlAn. As informatics controls, reads were per-base shuffled to randomize nucleotide order while preserving read length and guanine-cytosine (GC) content, and purely synthetic reads were generated from a four-base process matched only to an aggregate GC target; both were classified identically. Genus abundances were regressed against Kraken2 database k-mer representation and against GC content. Results. Across 40 samples, Kraken2 reported several thousand genera, samples clustered by specimen type in principal-coordinate analysis (PCoA), and pooled genus counts correlated strongly with a published cancer-microbiome catalog (The Cancer Genome Atlas lung adenocarcinoma, TCGA-LUAD; Spearman {rho} = 0.81 over 282 shared genera), a pattern readily interpreted as biological signal. However, these observations were also made in per-base shuffling, which preserves GC content and length but destroys all biological sequence: shuffled reads were still abundantly classified, still clustered by specimen type, and still correlated with the catalog ({rho} {approx} 0.7), as did every sample group, including pure reagent controls. Genus counts scaled tightly with each genus's k-mer representation in the Kraken2 database on real (r 2 = 0.74) and shuffled (r2 = 0.85) reads, and the same dependence appeared in the independent published cohort. Purely synthetic reads carrying no information beyond an aggregate GC target reproduced much of the cross-cohort agreement (synthetic TCGA-LUAD {rho} = 0.61 versus 0.81 for real reads; significant in 27 of 33 TCGA cancers), and replicate shuffles of a low-GC versus a high-GC plasma sample, for which the true difference is zero, produced spurious significant differences in about 46% of genera. Regressing observed counts against the shuffled baseline left 23 genera above the artifact floor at 5% false discovery rate (FDR), nearly all known kit contaminants, control-enriched viruses, or very-low-abundance taxa; a four-criterion validity filter reduced thousands of Kraken2 genera to a single defensible candidate, Klebsiella. Conclusions. Much of the apparent cmDNA structure, including its agreement with a published cancer-microbiome catalog, is explained by base composition and reference-database architecture rather than authentic biology, and short-read k-mer pipelines cannot separate the two on their own. We find little positive evidence of an authentic circulating microbial signal, though our small sample cannot prove its absence. To limit false discovery in low-biomass metagenomics, we recommend specimen-matched negative controls, corroboration with a conservative second classifier, per-base shuffling (with GC-matched synthetic reads as a stricter floor), and GC-aware analysis.

bioinformatics↗

bioRxiv2026

Annotation-Based Gene-Peak Links Improve Regulatory Network Prediction of Gene Expression in Human Kidney Multi-Omics

BackgroundLinking distal regulatory elements to their target genes is a central problem for interpreting chromatin accessibility and other non-coding genomic data. Proximity-based mapping is convenient but ignores three-dimensional enhancer-promoter architecture and can misassign long-range regulatory effects. Correlation-based approaches can also miss regulatory links because of limited statistical power and restrictive distance or significance thresholds. Single-cell and single-nucleus multi-omic datasets, such as 10x Multiome profiles that jointly measure chromatin accessibility and gene expression in the same cells or nuclei, now provide a way to evaluate gene-peak linkage strategies by testing how well linked accessibility features predict gene expression. Existing methods often focus on scoring individual enhancer-gene pairs. In this study, we proposed and constructed a fast annotation-based candidate gene-peak network and tested whether it improves downstream prediction of gene expression. MethodsWe first built a unified gene-peak regulatory network by integrating enhancer-based, promoter-based, and proximity-based linkage strategies. We then used single-cell multiome data from the Kidney Precision Medicine Project (KPMP) 10x Multiome cohort to evaluate whether the proposed links captured regulatory signals. We aggregated RNA expression and ATAC accessibility at the cell-type cluster level and trained predictive models to evaluate how well different linkage strategies could explain gene expression based on accessibility. Model performance was compared between annotation-based (including both enhancer- and promoter-based links) and proximity-based gene-peak links using testing R{superscript 2} and mean squared error (MSE) in a strict set of 1,704 genes and an adaptive set of 7,973 genes with more relaxed requirements. ResultsIn the strict regime (1,704 genes with [≥]20 peaks assigned by the nearest-TSS rule and [≥]10 annotated enhancer-based peaks), the annotation-based model consistently achieved higher testing R{superscript 2} and lower testing MSE than the proximity-based model. In the larger adaptive regime (7,973 genes with [≥]5 proximity-based and [≥]2 enhancer-based peaks), we defined for each gene a balanced number of selected peaks based on its available closest and enhancer links; the annotation-based model again showed globally higher testing R{superscript 2} and lower testing MSE. These improvements were observed over a broad range of candidate-linked peak numbers. ConclusionsUsing human kidney 10x Multiome data, we show that a fast annotation-based gene-peak linkage framework can improve prediction of gene expression from chromatin accessibility compared with conventional approaches. These results support the use of biologically informed enhancer and promoter annotations when constructing candidate gene regulatory networks. Our framework also showed concordance with the correlation-based Signac LinkPeaks method while providing broader coverage and greater computational efficiency. We have implemented these annotation-based linkage methods in the GPlinksR R package, providing a fast and scalable tool for constructing regulatory networks.

genomics↗

bioRxiv2025

Clustering of Omic Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient Variational Inference: Method and Applications to Bulk and Single-Cell Transcriptomics

MotivationHigh-dimensional omics data are typically measured on limited sample sizes, which challenges model-based clustering methods such as Gaussian mixture models, often leading to instability and poor generalization under complex mixture structures. To address these limitations, we developed Praxis-BGM, a natural-gradient variational inference framework for Gaussian mixture models that enables semi-supervised transfer learning by incorporating an informative prior Gaussian mixture model derived from large-scale reference data with robust cluster structures. This prior can encode cluster-specific means, covariance structures, and structural connectivity patterns, and is updated using the target data with variational inference to improve clustering in small-sample settings. ResultsWe derived natural-gradient updates for standard parameters and assess feature-level contributions to posterior clustering via Bayes Factors. Implemented in Python library JAX for accelerator-oriented computation, Praxis-BGM is computationally efficient and scalable. Across extensive simulations and two real-world applications--breast cancer bulk transcriptomics for subtype recovery and single-cell transcriptomics for cross-platform label transfer--Praxis-BGM improves posterior clustering performance, stability, and biological interpretability, even when priors are partially mismatched. Availability and ImplementationPraxis-BGM is freely available at https://github.com/ContiLab-usc/Praxis-BGM, and an archival version is available on Zenodo at https://doi.org/10.5281/zenodo.19657680. Contactqiranjia@usc.edu Supplementary InformationSupplementary materials are available with the manuscript submission.

bioinformatics↗

bioRxiv2025

Single-molecule microscopy reveals that TFIIE subunits dynamically interact with preinitiation complexes in a manner controlled by TFIIH

Transcription by RNA polymerase II (Pol II) requires general transcription factors that bind with Pol II at the promoters of protein-coding genes to form preinitiation complexes (PICs). Among these is TFIIE, which recruits TFIIH to the PIC and stimulates the kinase and translocase activities of TFIIH, thereby regulating the fate of formed PICs. In this study, we used a purified reconstituted human Pol II transcription system and single molecule total internal reflection fluorescence (smTIRF) microscopy to monitor TFIIE binding dynamics in PICs under different conditions in real time. We observed highly dynamic interactions of the two subunits of TFIIE (TFIIE and TFIIE{beta}) with PICs. Measurement of rate constants for on/off binding of each subunit suggest they behave asynchronously. TFIIH exclusion increased the rates of association and dissociation for both subunits, with the strongest effect on TFIIE. Despite stabilization of TFIIE by TFIIH the TFIIE subunits remain dynamic in PICs. Additionally, two disease-related TFIIE{beta} point mutations destabilized TFIIE{beta} and altered its kinetic behaviors within PICs. Our results contribute to an emerging model that PICs are not static assemblies and highlight important connections between the structural arrangement and kinetic behaviors of GTFs in PICs.

biochemistry↗

bioRxiv2022

RNA- and DNA-binding proteins generally exhibit direct transfer of polynucleotides: Implications for target site search

We previously demonstrated that the PRC2 chromatin-modifying enzyme exhibits the ability to directly transfer between RNA and DNA without a free-enzyme intermediate state. Simulations suggested that such a direct transfer mechanism may be generally necessary for RNA to recruit proteins to chromatin, but the prevalence of direct transfer capability is unknown. Herein, we used fluorescence polarization assays and observed direct transfer for several well-characterized nucleic acid-binding proteins: three-prime repair exonuclease 1 (TREX1), heterogeneous nuclear ribonucleoprotein U, Fem-3-binding factor 2, and MS2 bacteriophage coat protein. For TREX1, the direct transfer mechanism was additionally interrogated by single molecule assays, and the data suggest that direct transfer occurs through an unstable ternary intermediate with partially associated ligands. Generally, direct transfer could allow many DNA- and RNA-binding proteins to conduct a one-dimensional search for their target sites. Furthermore, presumably long-lived protein-polynucleotide complexes might instead be readily replaced by other protein-polynucleotide complexes in vivo. SignificanceClassically, the lifetime of a protein-ligand complex is presumed to be an intrinsic property, unaffected by competitor molecules in free solution. By contrast, a few oligomeric nucleic acid binding proteins have been observed to exchange competing ligands in their binding sites, and consequently their lifetimes decrease with competitor concentration. Our findings indicate that this "direct transfer" is a more general property of nucleic acid binding proteins. This suggests that many DNA- and RNA-binding proteins can reduce the dimensionality of their search for their target sites by intramolecular direct transfer to nucleosome DNA, instead of relying entirely on three-dimensional diffusion, and it suggests that their mean complex lifetimes in vivo can be regulated by the concentration of free ligand molecules.

biophysics↗

bioRxiv2020

miR-206 knockout shows it is critical for myogenesis and directly regulates newly identified target mRNAs

The muscle specific miRNA, miR-206, is important for the process of myogenesis; however, studying the function of miR-206 in muscle development and differentiation still proves challenging because the complement of mRNA targets it regulates remains undefined. In addition, miR-206 shares close sequence similarity to miR-1, another muscle specific miRNA, making it hard to study the impact of miR-206 alone in cell culture models. Here we used CRISPR/Cas9 technology to knockout miR-206 in C2C12 muscle cells. We show that knocking out miR-206 significantly impairs and delays differentiation and myotube formation, revealing that miR-206 alone is important for myogenesis. In addition, we use an experimental affinity purification technique to identify new mRNA targets of miR-206 in C2C12 cells. We identified over one hundred mRNAs as putative miR-206 targets. Functional experiments on six putative targets indicate that Adam19, Bgn, Cbx5, Smarce1, and Spg20 are direct miR-206 targets in C2C12 cells. Our data show a unique and important role for miR-206 in myogenesis.

molecular biology↗