Biology subjects

Anderson, Z.

Publications and source records attributed to Anderson, Z..

5 recordsLinked to original sources

bioRxiv2026

Determinants of haplotype phasing accuracy in long-read human genome sequencing

Accurate haplotype phasing is critical for interpreting human genetic variation. Long-read whole-genome sequencing has emerged as a powerful approach for read-based phasing, particularly where parental DNA is absent, yet the determinants of phasing accuracy remain incompletely defined. Here, we evaluate haplotype phasing performance across sequencing technology, reference genome, read length, and coverage depth using Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) data from two Genome in a Bottle reference samples (HG002 and HG005). In clinically relevant genes, alignment to the T2T-CHM13 (T2T) reference genome improves phasing performance relative to GRCh38, reducing mean gene-level phasing error rates by 3-9-fold. T2T alignment increases phase set NG50 and yields 1.5-2-fold more phased variant pairs. At similar read N50 values, ONT has a higher phasing error rate than PacBio in certain genes. Downsampling demonstrates that phasing error rates plateau at [~]20x coverage. Longer ONT read lengths reduce phasing error rates and extend phase set contiguity. Haplotype-resolved assemblies produce substantially higher phasing error rates than alignment-based phasing, demonstrating the advantage of an alignment-based approach. To enable per-variant-pair confidence assessment, we introduce PhaseQuality, a technology-specific stratification method that assigns confidence tiers to phased variants based solely on sequencing data. PhaseQuality accurately assigns 82-99% of known phasing errors to lower-confidence tiers, reducing error rates among high-confidence pairs to <0.5%. Together, these results demonstrate the primary technical determinants of long-read haplotype phasing accuracy and provide practical benchmarks for optimizing reference genome selection, coverage targets, and read length for long-read sequencing studies.

genomics↗

bioRxiv2026

Origin-1: a generative AI platform for de novo antibody design against novel epitopes

0Generative artificial intelligence has advanced antibody discovery, yet de novo design of therapeutic antibodies against targets with "zero-prior" epitopes remains a fundamental challenge. We define "zero-prior" epitopes as target sites lacking structural data from any reported antibody-antigen or protein-protein complex involving the target. Here we present Origin-1, a generative AI platform that overcomes this by integrating epitope-conditioned all-atom structure generation, paired complementarity determining region sequence design, and a specialized co-folding-based scoring protocol to select antibody designs predicted to be high-confidence, specific binders with favorable developability. We evaluated Origin-1 on a panel of ten targets selected to have no available protein-protein complex structures and minimal homology ([≤]60% sequence identity) to proteins with known complexes, creating stringent design conditions. In fewer than one hundred design attempts per target, we identified developable, specific antibodies, validated across multiple biophysical and developability assays, for four targets: COL6A3, AZGP1, CHI3L2, and IL36RA, with functional inhibition demonstrated for IL36RA. Cryogenic electron microscopy confirmed the atomic accuracy of our designs, revealing complexes that closely matched the computational models with high structural fidelity (3.0-3.3 [A] resolution; 0.83-0.91 DockQ). Furthermore, we employed AI-guided affinity maturation to optimize a de novo antibody binder against IL36RA, producing functional antagonists with sub-nanomolar affinities and a top EC50 of 12.3 nM. These results demonstrate a framework for targeting epitopes without structural precedent, expanding the programmable therapeutic antibody landscape.

molecular biology↗

bioRxiv2025

High-Throughput Machine Learning-Aided Antibody Discovery for Cell Surface Antigens

Machine learning (ML) has the potential to revolutionize antibody design and selection, but its success depends on access to extensive, well-curated datasets of antibody-antigen interactions. To address this need, we developed a synthetic Fab yeast display library optimized for seamless ML integration, focusing on sequence diversity within the CDRH3 loop. The library incorporates key sequence features derived from human B cell repertoires essential for efficient antibody generation captured in a compact antigen recognition module (ARM) format. Built using the VH1-69 heavy chain and four light chains, the library was evaluated against ten human and murine cell surface antigens, including PD-L1, TIGIT, and ROBO1. This approach yielded hundreds of antibodies with robust biophysical properties, validated for functional performance in flow cytometry and immunohistochemistry. Furthermore, ML analysis identified additional antibodies for ROBO2 and PD-L2 from the aggregate sequencing data, demonstrating utility for hybrid in silico and experimental workflows. We provide a publicly accessible dataset comprising more than 68,000 Fab sequences and 486 characterized antibodies. This study establishes an ML-compatible framework designed to accelerate and streamline antibody discovery and development.

biophysics↗

bioRxiv2024

A Randomized, Controlled Clinical Trial Demonstrates Improved Cognitive Function in Senior Dogs Supplemented with a Senolytic and NAD+ Precursor Combination.

Age-related decline in mobility and cognition are associated with cellular senescence and NAD+ depletion in dogs and people. A combination of a novel NAD+ precursor and senolytic, LY-D6/2 was examined in this randomized controlled trial. Seventy dogs were enrolled and allocated into placebo, low or full dose groups. Primary outcomes were change in cognitive impairment measured with the owner-reported Canine Cognitive Dysfunction Rating (CCDR) scale and change in activity measured with physical activity monitors. Fifty-nine dogs completed evaluations at the three-month primary endpoint, and 51 reached the six-month secondary endpoint. There was a significant difference in CCDR score across treatment groups from baseline to the primary endpoint (p=0.02) with the largest decrease in the full dose group. There were no significant differences between groups in changes in measured activity. However, the proportion of dogs that improved in frailty and owner-reported activity levels and happiness was higher in the full dose group than other groups. Adverse events occurred equally across groups. All groups showed improvement in cognition, frailty, and activity suggesting placebo effect and benefits of trial participation. We conclude that LY-D6/2 significantly improves owner-assessed cognitive function and may have broader effects on frailty, activity and happiness as reported by owners.

animal behavior and cognition↗

bioRxiv2023

Broadening the scope: Multiple functional connectivity networks underlying threat and safety signaling

IntroductionThreat learning and extinction processes are thought to be foundational to anxiety and fear-related disorders. However, the study of these processes in the human brain has largely focused on a priori regions of interest, owing partly to the ease of translating between these regions in human and non-human animals. Moving beyond analyzing focal regions of interest to whole-brain dynamics during threat learning is essential for understanding the neuropathology of fear-related disorders in humans. Methods223 participants completed a 2-day Pavlovian threat conditioning paradigm while undergoing fMRI. Participants completed threat acquisition and extinction. Extinction recall was assessed 48 hours later. Using a data-driven group independent component analysis (ICA), we examined large-scale functional connectivity networks during each phase of threat conditioning. Connectivity networks were tested to see how they responded to conditional stimuli during early and late phases of threat acquisition and extinction and during early trials of extinction recall. ResultsA network overlapping with the default mode network involving hippocampus, vmPFC, and posterior cingulate was implicated in threat acquisition and extinction. Another network overlapping with the salience network involving dACC, mPFC, and inferior frontal gyrus was implicated in threat acquisition and extinction recall. Other networks overlapping with parts of the salience, somatomotor, visual, and fronto-parietal networks were involved in the acquisition or extinction of learned threat responses. ConclusionsThese findings help confirm previous investigations of specific brain regions in a model-free fashion and introduce new findings of spatially independent networks during threat and safety learning. Rather than being a single process in a core network of regions, threat learning involves multiple brain networks operating in parallel coordinating different functions at different timescales. Understanding the nature and interplay of these dynamics will be critical for comprehensive understanding of the multiple processes that may be at play in the neuropathology of anxiety and fear-related disorders.

neuroscience↗