Biology subjects

Rand, L.

Publications and source records attributed to Rand, L..

6 recordsLinked to original sources

bioRxiv2026

Predicting VHH-Fc Developability from Large-Scale IgG Data

The VHH-Fc antibody scaffold is an emerging therapeutic modality. No public large-scale, standardized developability VHH-Fc dataset exists. Filling that gap, we introduce GDPa5, a 160-member VHH-Fc library profiled across 10 biophysical assays on the PROPHET-Ab platform. Cross format models trained on the developability properties of 559 IgGs outperformed intra-format models trained on GDPa5 alone, which is an advantage driven by the larger scale of standardized IgG data rather than by format. The most accurately predicted properties were heparin binding (HAC, Spearman {rho}=0.82), hydrophobicity (HIC, {rho}=0.63), and self-association (AC-SINS, {rho}=0.62), all of which are largely governed by antibody surface properties. Tabular neural networks (TabICLv2, TabPFN v2.5), applied here for the first time to antibody developability prediction, outperformed conventional modeling approaches. Adding experimental HIC and HAC measurements as model inputs improved prediction of the more complex polyreactivity liability (PR-CHO, {Delta}{rho} = +0.10), supporting a tiered assay strategy that extends predictive performance while limiting experimental burden. We demonstrate through this work that IgG-trained models are a practical, data-efficient starting point for VHH-Fc developability prediction.

biophysics↗

bioRxiv2026

Decoding Bispecific Antibody Developability: Design Rules and Predictive Models from a 160-Member Library

Bispecific antibodies deliver functional outcomes that monospecific antibodies cannot, yet emergent self-association, polyreactivity, and aggregation often degrade their developability relative to their parental arms. Whether bispecific developability inherits from the parents or is driven by the format has not been tested at scale. We characterized 160 bispecific antibodies and their 65 parental arms on a uniform knobs-into-holes CrossMab IgG1 scaffold across 10 assays on the PROPHET-Ab high-throughput platform. Bispecific developability separates into three classes of inheritance. Hydrophobicity and surface charge inherit cleanly from the parents (Spearman {rho} {approx} 0.85 to 0.95), so parental-level screening predicts bispecific fate. Self-association and polyreactivity inherit partially ({rho} {approx} 0.60 to 0.88), with mechanistically interpretable emergent outliers driven in part by Fv-Fv charge complementarity and a parental biophysical ceiling on the hydrophobicity (HIC) by surface-charge (HAC) plane. Thermostability is poorly predicted from parental antibodies ({rho} < 0.4), so it requires bispecific-level testing. The class framework yields actionable selection rules: triage hydrophobicity and charge at the parental level, avoid pairing two high-HIC x high-HAC arms, pair opposite-sign Fv charges to suppress self-association but re-validate at the formulation buffer, and measure thermostability on the bispecific itself. This work charts a tractable path from monospecific sequence to bispecific developability prediction. SignificanceBispecific antibodies are a fast-growing therapeutic class, yet the rational design of well-behaving bispecific antibodies from validated monospecific antibody building blocks remains challenging. A key bottleneck is the lack of comprehensive, high-quality public datasets linking parental antibody developability properties to corresponding bispecific antibody developability properties. We address this gap by releasing a dataset comprising 160 bispecific antibodies and the 65 parental monospecific antibodies profiled in 10 developability assays. The data show that bispecific antibody developability is complex. Some properties are easily predictable from the parents, whereas others emerge in the bispecific format or from the bispecific format itself. The factors that govern each property can be identified empirically and used to make practical selection decisions. The mechanistic explanations and predictive models reported here establish a compact set of actionable rules. Together, they define a framework for using computational pipelines to convert monospecific antibodies into bispecific antibodies with drug-like developability properties, enabling faster and more effective generation of high-quality bispecific antibodies for diverse therapeutic applications.

biophysics↗

bioRxiv2025

Mapping the Transcriptional Landscape of Drug Responses in Primary Human Cells Using High-Throughput DRUG-seq

To advance our understanding of drug action in physiologically-relevant systems, we developed a high-throughput transcriptomic atlas of compound responses in primary human cell types. Leveraging the scalable and cost-effective Digital RNA with the pertUrbation of Genes (DRUG-seq) assay, we profiled gene expression responses to 89 pharmacologically-active compounds across six concentrations in four distinct primary cell types: aortic smooth muscle cells (AoSMCs), skeletal muscle myoblasts (SkMMs), dermal fibroblasts, and melanocytes. Through rigorous quality control and normalization, we generated reproducible and cell type-resolved transcriptomic signatures, enabling the discovery of both shared and divergent regulatory programs. This dataset revealed core cellular responses, such as brefeldin A-mediated ER stress across all cell types, as well as lineage-specific effects, including dexamethasone-induced hypoxia signaling in AoSMCs, complex inflammatory responses linked to epithelial-to-mesenchymal transition pathways in SkMMs, TGF-{beta}-modulated states in fibroblasts, and dabrafenib-driven transcriptional shifts towards quiescence in melanocytes. By integrating systematic perturbations with primary models, this dataset serves as a resource for building systems-level models of drug response and mechanism. Ultimately, we aim to accelerate predictive pharmacology by enabling high-throughput data generation grounded in human biology and readily usable by artificial intelligence models.

systems biology↗

bioRxiv2023

Population genetic analyses of longitudinal vaginal microbiome reveal racioethnic evolutionary dynamics and prevailing positive selection of Lactobacillus adhesins

The vaginal microbiome is important for reproductive health and should constantly evolve in response to dynamic host-microbe interactions. The composition of the vaginal microbiome is associated with ethnicity; however, the evolutionary landscape of the vaginal microbiome, especially in the multi-ethnic context, remains under-studied. In this study, we performed a longitudinal evolutionary vaginal microbiome analysis of 351 samples from 35 pregnant women with diverse ethnic backgrounds and validated the main findings in two cohorts totaling 462 samples from 90 multi-ethnic women. Microbiome alpha diversity and community states showed strong ethnic signatures. Lactobacillaceae species had a significantly higher nonsynonymous/synonymous mutation ratio (pN/pS) than non-Lactobacillaceae species in all ethnicities. In addition, non-Lactobacillaceae anaerobic bacteria were enriched in Black and Latino women, with significantly elevated nucleotide diversity and lower pN/pS in Black women. Intriguingly, the Lactobacillaceae species had a large repertoire of positively selected genes, including the human mucin-binding and bacterial cell wall anchor genes, which showed independent, recurrent signatures of positive selection across multiple strains, indicating that the host-microbiome interactions directly drive microbial evolution at the molecular interface. Finally, we propose that the evolutionary metrics reflect the environmental niches of adapting microbes. Our study revealed the extensive ethnic signatures in vaginal microbial diversity, composition, community state, and evolutionary dynamics at species and gene levels, highlighting the importance of studying the host-microbiome ecosystem from an evolutionary perspective. HighlightsO_LIExtensive ethnic signatures in the vaginal microbial diversity, composition, community state, and evolutionary dynamics were demonstrated. C_LIO_LIHealthy Lactobacillaceae species showed lower nucleotide diversity but much more relaxed or even positive selection of genes than non-Lactobacillaceae species. C_LIO_LIIn Black women, non-Lactobacillaceae species displayed higher nucleotide diversity and more stringent negative selection. C_LIO_LILactobacillus mucin-binding and cell wall anchor genes showed convergent signatures of positive selection across vaginal microbiomes. C_LI

microbiology↗

bioRxiv2022

Longitudinal Urine Metabolic Profiling and Gestational Age Prediction in Pregnancy

Pregnancy is a critical time that has long-term impacts on both maternal and fetal health. During pregnancy, the maternal metabolome undergoes dramatic systemic changes, although correlating longitudinal changes in maternal urine remain largely unexplored. We applied an LCMS-based untargeted metabolomics profiling approach to analyze 346 longitudinal maternal urine samples collected throughout pregnancy for 36 women from diverse ethnic backgrounds with differing clinical characteristics. We detected 20,314 metabolic peaks and annotated 875 metabolites. Altered metabolites include a broad panel of glucocorticoids, lipids, and amino acid derivatives, which revealed systematic pathway alterations during pregnancy. We also developed a machine-learning model to precisely predict gestational age (GA) at time of sampling using urine metabolites that provides a non-invasive method for pregnancy dating. This longitudinal maternal urine study demonstrates the clinical utility of using untargeted metabolomics in obstetric settings. One Sentence SummaryMachine-learning based gestational age and due date using longitudinal urine samples of pregnancy.

systems biology↗

bioRxiv2021

Glia-neuron signaling mediated by two different BMP ligands impacts synaptic growth

The nervous system is a complex network of cells whose interactions provide circuitry necessary for an organism to perceive and move through its environment. Revealing the molecular basis of how neurons and non-neuronal glia communicate is essential for understanding neural development, behavior, and abnormalities of the nervous system. BMP signaling in motor neurons, activated in part by retrograde signals from muscle expressed Gbb (BMP5/6/7) has been implicated in synaptic growth, function and plasticity in Drosophila melanogaster. Through loss-of-function studies, we establish Gbb as a critical mediator of glia to neuron signaling important for proper synaptic growth. Furthermore, the BMP2/4 ortholog, Dpp, expressed in a subset of motor neurons, acts by autocrine signaling to also facilitate neuromuscular junction (NMJ) growth at specific muscle innervation sites. In addition to signaling from glia to motor neurons, autocrine Gbb induces signaling in larval VNC glia which strongly express the BMP type II receptor, Wit. In addition to Dpps autocrine motor neuron signaling, Dpp also engages in paracrine signaling to adjacent glia but not to neighboring motor neurons. In one type of dorsal midline motor neuron, RP2, dpp transcription is under tight regulation, as its expression is under autoregulatory control in RP2 but not aCC neurons. Taken together our findings indicate that bi-directional BMP signaling, mediated by two different ligands, facilitates communication between glia and neurons. Gbb, prominently expressed in glia, and Dpp acting from a discrete set of neurons induce active Smad-dependent BMP signaling to influence bouton number during neuromuscular junction growth.

neuroscience↗