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Ross, D.

Publications and source records attributed to Ross, D..

At least 19 recordsLinked to original sources
bioRxiv2026

Developing a behavioral measure of social memory from narrative video

Understanding other people relies on long-term memory: we use learned information about others' personalities, mental states, and personal histories to make inferences about the causes of their behavior. However, little is known about individual differences in memory for high-level social information. Here, we develop and psychometrically evaluate a behavioral measure of social memory from narrative video content. Participants (N = 103) watched the pilot episode of the TV show Friday Night Lights, and answered multiple choice questions about characters' actions, statements, inferred mental states, relationships, and personalities immediately after viewing and following a 21-day delay. The social memory task showed moderate split-half reliability, demonstrating feasibility as an individual difference measure. Memory for information about specific events in the narrative showed a decline across the delay, while memory for abstract information remained stable. Social memory performance was correlated with separate measures of face-name association and verbal episodic memory, but not with face image recognition memory. These results provide a proof of concept for a novel behavioral measure of social memory and suggest that the measure relates to episodic and relational memory but not image recognition.

neuroscience↗
bioRxiv2026

QuadCleave: Enzymatic cleavage and analysis of DNA G-quadruplexes.

Prediction of G-quadruplexes (G4), noncanonical DNA secondary structures containing guanine tetrads, can have profound relevance across cancer, neurodegenerative and other genetic diseases, but require experimental validation by instrument intensive methods including CD spectroscopy, fluorescence, NMR, or crystallography. We report a novel, inexpensive, efficient and scalable 2-enzyme system for confirmation of G4 formation and copy number repeat count. First, QuadCleave, in which Mismatch Endonuclease I was used to repeatably, site-specifically and selectively cleave oligodeoxynucleotides containing exclusively G4-forming sequences. Second, HaeIII enzyme identified G4-forming sequences that can also generate GG/CC base pairs, and a sequences cleavage by both established it in C9ORF72, the most mutated gene in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). These findings establish ubiquitously accessible experimental validation of G4 forming sequences and their copy number repeat assessment, with specificity to C9ORF72 oligodeoxynucleotides whose repeat expansion has critical clinical implications in a subpopulation of ALS and FTD patients.

molecular biology↗
bioRxiv2026

Unified sampling framework and experimental benchmarking of sequence- and structure-based protein models

Generative models are increasingly used for protein design, but the lack of standardized evaluation frameworks limits comparison across model classes and hinders translation to experimental success. Here, we introduce a unified sampling and benchmarking framework that enables controlled sequence generation across alignment, protein language, and structure-based models, and apply it to Tobacco etch virus (TEV) protease. Across hundreds of thousands of designed sequences, different models explore distinct regions of sequence space with no clear computational selection metrics to assess enzymatic function. Experimental evaluation reveals large differences in functional outcomes, ranging from non-functional variants to sequences with 9-fold higher activity than wildtype. Machine learning-designed libraries achieve a 39.32% hit rate (percentage of variants matching or exceeding wildtype activity) compared to 6.06% for an error-prone PCR baseline. Structure-based models perform best overall, with hit rates of 74.4% and 66.8% for ESM-IF1 and ProteinMPNN, respectively. Commonly used selection metrics do not strongly correlate with experimental activity, highlighting a gap between in silico evaluation and enzyme function. Together, these results establish a generalizable framework for benchmarking generative protein models and demonstrate the necessity of experimental validation for guiding model development and sequence prioritization.

bioinformatics↗
bioRxiv2026

Functional Profiling of Thousands of Sequence-Diverse Protease Homologs with GROQ-seq

High-quality datasets that span broad sequence diversity are essential for understanding protein sequence-function relationships beyond local mutational landscapes. Here, we applied Growth-based Quantitative Sequencing (GROQ-seq) to measure function across an 11,722 member protease library, comprised of natural homologs and AI-shrunken variants. This library spans vast sequence diversity, with Levenshtein distances of up to 245 and a mean pairwise sequence identity of 41% to TEV protease S219V. We identified sequence-divergent TEV protease homologs that preserve function against the native TEV protease substrate. These findings reveal the robustness of protease activity across highly diverse sequences. Here, we demonstrate the aptitude of the GROQ-seq assay for screening large, diverse protein libraries for function, enabling efficient data generation at scale for training machine learning models across broad sequence landscapes.

synthetic biology↗
bioRxiv2026

GROQ-seq Datasets Across Transcription Factors (LacI, RamR, VanR), T7 RNA Polymerase and TEV Protease

Predicting any proteins function from its sequence alone would be a significant breakthrough in molecular biology. Although machine learning approaches have sought to tackle this, their limited generalizability reflects the absence of sufficiently large, open, diverse, and unified datasets. To address this data gap, we developed a high-throughput experimental platform called GROQ-seq (Growth-based Quantitative Sequencing). In GROQ-seq, a proteins function can be linked to a sequencing-based readout that enables scalable characterization of large variant libraries in Escherichia coli. Here, we present pilot datasets demonstrating its performance across three distinct protein function classes: transcription factors, polymerases, and proteases. The objective of this report is to present the datasets and to provide users with a clear and transparent characterization of their properties, including both the strengths and limitations.

bioengineering↗
bioRxiv2026

GROQ-seq Enables Cross-site Reproducibility for High-Throughput Measurement of Protein Function

High-throughput functional assays are increasingly used to generate large-scale protein function datasets for protein engineering and machine learning applications. However, the utility of such datasets depends on the reproducibility of the underlying measurements. Here we report reproducible, quantitative measurements of protein sequence-to-function data at scale across two facilities. We analyze GROQ-seq (Growth-based Quantitative Sequencing) measurements of three bacterial transcription factors. Independent barcode measurements of the same sequence produce highly consistent functional estimates, demonstrating strong biological reproducibility (across all transcription factors the mean Root Mean Square Deviation [RMSD] {approx} 0.53 and mean Spearman {approx} 0.63). We also compared experiments performed at two facilities using a shared protocol, but with differing levels of automation and system integration. We observe strong agreement between measurements taken at the two sites (mean RMSD {approx} 0.41 and mean Spearman {approx} 0.730). Orthogonal tests further support this agreement: a classifier trained to distinguish data by site performs near random (AUC = 0.559), and top-ranking variants show strong statistical overlap between experiments. Together, these results demonstrate that GROQ-seq enables reproducible, scalable measurement of protein function suitable for large aggregated datasets.

bioengineering↗
bioRxiv2026

evedesign: accessible biosequence design with a unified framework

Machine learning methods for protein engineering are rarely interoperable, require bespoke workflows, and remain inaccessible to non-experts. Yet the design problems that matter most - conditional design subject to real-world constraints, multi-objective optimization, and iterative lab-in-the-loop workflows where experimental data continuously refines successive design rounds - demand exactly the kind of flexible, composable infrastructure that no single tool provides. We present evedesign, a unified open-source framework that formalizes conditional biosequence design in a method-agnostic way, enabling complex multiobjective workflows combining supervised and unsupervised models from standardized specifications, and built from the outset to support iterative experimental integration. An interactive web interface facilitates end-to-end design for a broad scientific audience at https://evedesign.bio. We demonstrate evedesigns utility in antibody engineering, enzyme design, and natural enzyme discovery, and invite open-source community contributions.

bioinformatics↗
bioRxiv2025

Mapping the phenotypic landscape of a transcriptional repressor using Deep Mutational Scanning and Growth-based Quantitative Sequencing

CymR is a TetR-family transcriptional repressor that recognizes a well-defined operator sequence in the promoter PcymRC. The native ligand cumate and several structurally related aromatic acids bind at an allosteric site and induce a conformational change in CymR, resulting in release from the DNA operator and de-repression of the promoter. The amino acid residues that contribute to these core functions have not been mapped, nor has the protein been subjected to extensive mutagenesis to modify its function. Here, for the first time, we integrate Deep Mutational Scanning (DMS) with Growth-based Quantitative Sequencing (GROQ-Seq) to evaluate a comprehensive phenotypic landscape of CymR variants, including single amino acid insertions and deletions. We measure this library across a concentration gradient of small molecule inducers to construct an induction curve for all library members. From this analysis, we identify amino acids throughout the protein that are essential for repressor function and discover several mutations that improve the sensitivity of CymR to the ligand perillic acid. In addition, rarely investigated insertion mutants are revealed to be a key driver of novel phenotypes, including several regions of CymR where insertions result in an inverted phenotype and the isolation of variants exhibiting an unusual band-stop phenotype. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/693801v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@121b16org.highwire.dtl.DTLVardef@b0478aorg.highwire.dtl.DTLVardef@128f2ecorg.highwire.dtl.DTLVardef@164a426_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗
bioRxiv2025

Single cell long-read genotyping of transcriptomes reveals discrete mechanisms of clonal evolution in post-myeloproliferative neoplasm acute myeloid leukemia.

Myeloproliferative neoplasms (MPNs) are caused by acquired mutations in hematopoietic stem and progenitor cells (HSPCs). The acquisition of additional mutations like TP53 and the overall mutational burden influence a patients risk of disease progression toward lethal post-MPN acute myeloid leukemia (AML). Recent technological advancements in linking single-cell gene expression with genotype have improved our understanding of tumor heterogeneity. However, current methodologies have limitations in simultaneously genotyping low-expression genes (such as JAK2) alongside other pathogenic loci. To address this, we developed a novel long read genotyping pipeline of cDNA transcripts called LOTR-Seq, which can genotype the full length of expressed transcripts of 30 genes at once. Using LOTR-Seq, we genotyped HSPCs at the JAK2V617 locus in 9,075 single cells from eight patients with chronic phase MPN (CP-MPN) and in 5,016 cells from four patients with post-MPN AML. We then linked the mutations to the single cell transcriptome of 29,712 JAK2V617F-driven CP-MPN cells and 16,895 post-MPN AML cells. In our analysis of post-MPN AMLs, we identified nine mutated loci across six genes (JAK2, IDH1/2, TP53, SRSF2, U2AF1) and linked these mutations to specific transcriptional phenotypes. Overall, LOTR-Seq provides novel insights into the evolution of post-MPN AML.

cancer biology↗
bioRxiv2025

A phylogenomic and metagenomic meta-analysis of bacterial diversity in the phyllosphere lifts a veil on Hyphomicrobiales dark matter

The phyllosphere, or above-ground part of plants, hosts diverse bacterial communities that play critical ecological roles and provide beneficial functions for the plant. The Hyphomicrobiales (Alphaproteobacteria) are a highly diverse and ecologically important clade known to be key members of the plant microbiome, in particular in association with plant roots, but their diversity remain largely uncharacterized in the phyllosphere. Using a meta-analysis combining metabarcoding, metagenomics and phylogenomics, we explored the worldwide diversity of leaf-associated Hyphomicrobiales. We confirmed Methylobacterium was ubiquitous in the phyllosphere and revealed the dominance of two under-characterized Hyphomicrobiales taxa: Lichenibacterium, a lichen-associated genus previously identified as "1174-901-12" in taxonomic databases, and RH-AL1, an undescribed lineage of bacteria related to Beijerinckiaceae, previously isolated from coal slag. Despite their abundance in the phyllosphere, Lichenibacterium and RH_AL1 could not be properly detected by 16S rRNA gene barcoding, due in part to limitations of taxonomic resolution of the 16S rRNA gene and of representativeness in existing taxonomic databases, underlining limitations of this approach for their accurate identification in the phyllosphere. As for Methylobacterium, a significant proportion of Lichenibacterium and RH-AL1 were also detected in association with lichens and in environments with harsh conditions like exposed surfaces, air and snow, suggesting airborne or waterborne dispersal and high resilience in harsh environments. Overall, our study stresses the need to move toward metagenomics and culturomics to increase the representativeness of leaf-associated bacterial taxa in reference databases, and to improve our understanding of the evolutionary and functional mechanisms underpinning bacteria adaptations to living on plants. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/671110v4_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@938d34org.highwire.dtl.DTLVardef@11b6df0org.highwire.dtl.DTLVardef@1ced858org.highwire.dtl.DTLVardef@dd7a55_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIGlobal meta-analysis reveals core Hyphomicrobiales in the phyllosphere C_LIO_LILichenibacterium and Methylobacterium dominate the phyllosphere and exposed surfaces C_LIO_LINovel phyllosphere clades RH-AL1 and JAJXWB01 were identified by phylogenomics C_LIO_LI16S rRNA gene limits taxonomic resolution in the phyllosphere; metagenomics refines taxonomy C_LI

microbiology↗
bioRxiv2025

Thermally activated irreversible homogenization of G-quadruplexes in an ALS/FTD-associated nucleotide expansion

A significant proportion of familial amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) cases exhibit a substantial copy number expansion of the hexanucleotide GGGGCC/GGCCCC sequence in the C9ORF72 gene. The GGGGCC sequence forms a non-canonical DNA structure called a G-quadruplex (G4) which has been associated with the disease states and with nucleic acid condensate formation. G4s can fold into various topologies, which can differentially impact fidelity of DNA synthesis. However, how G4 conformational heterogeneity and its regulation impact hexanucleotide repeat expansion is unclear, and important clues may lie in the thermodynamic properties of different G4 topologies. Here, we use temperature-swept CD spectroscopy to observe configurational homogenization of an initially heterogeneous population of G4s over a small range of temperatures, demonstrating thermally activated behavior. We further show that this reaction is irreversible, since subsequent temperature sweeps do not show CD shifts from non-parallel to parallel G4 topologies. Finally, we provide an analytical theory based on a two-state thermodynamic model which is compatible with experimental evidence, and we discuss alternate mechanisms for the homogenization transition. These findings suggest that kinetic regulation of non-canonical DNA structures may play a role in cellular homeostasis or disease pathogenesis. SIGNIFICANCEThe GGGGCC repeats in the C9ORF72 gene expand in copy number in certain neurodegenerative diseases, forming non-canonical DNA structures called G-quadruplexes (G4) which are associated with the pathological state. However, why the repeat expansion occurs is not known, and a key may lie in the thermodynamic stability of certain G4 conformations. Here, we use CD spectroscopy to experimentally report thermally activated heat-induced G4 conformation homogenization, from a heterogeneous population to the parallel configuration. We derive an analytical biophysical theory which is compatible with this experimental observation, which is shown to be irreversible. Our in vitro tuning of the free energy landscape that modulates G4 conformational fidelity motivates a search for possible in vivo enzymatic regulators.

biophysics↗
bioRxiv2025

Epistasis in Allosteric Proteins: Can Biophysical Models Provide a Better Framework for Prediction and Understanding?

The prediction of epistasis, or the interaction between mutations, is a complex challenge impacting protein science, healthcare, and biotechnology. For allosteric proteins, the prediction of epistatic effects is further complicated by the intricate networks of conformational states and binding interactions inherent to their function. Here, we explore these issues by systematically comparing biophysical and phenomenological models to analyze mutational effects and epistasis for the lac repressor protein, LacI. Using an extensive dataset consisting of dose-response measurements for 164 LacI variants, we find that while the phenomenological Hill model provides slightly better predictive accuracy, the biophysical model fits the data more parsimoniously, with significantly less epistasis in its parameters. Our results highlight the importance of the multi-state, multi-dimensional nature of allosteric function and the potential benefits of using biophysical models for the analysis of mutational effects and epistasis.

biophysics↗
bioRxiv2024

A novel in-vitro model of the bone marrow microenvironment in AML identifies CD44 and Focal Adhesion Kinase as therapeutic targets to reverse cell adhesion-mediated drug resistance

Acute myeloid leukemia (AML) is an aggressive neoplasm. Although most patients respond to induction therapy, they commonly relapse due to recurrent disease in the bone marrow microenvironment (BMME). So, disruption of the BMME, releasing tumour cells into the peripheral circulation, has therapeutic potential. Using both primary donor AML cells and cell lines, we developed an in-vitro co-culture model of the AML BMME. We used this model to identify the most effective agent(s) to block AML cell adherence and reverse adhesion-mediated treatment resistanc E. We identified anti-CD44 treatment significantly increased the efficacy of cytarabine. However, some AML cells remained adhered, and transcriptional analysis identified focal adhesion kinase (FAK) signalling as a contributing factor; adhered cells showed elevated FAK phosphorylation that was reduced by the FAK inhibitor, defactinib. Importantly, we demonstrated that anti-CD44 and defactinib were highly synergistic at diminishing adhesion of the most primitive CD34high AML cells in primary autologous co-cultures. Taken together, we identified anti-CD44 and defactinib as a promising therapeutic combination to release AML cells from the chemoprotective AML BMME. As anti-CD44 is already available as a recombinant humanised monoclonal antibody, the combination of this agent with defactinib could be rapidly tested in AML clinical trials.

cancer biology↗
bioRxiv2024

Multiple factors regulate i-motif and G-quadruplex structures in vitro: analysis of repeated and non-repeated polyG/polyC clusters by circular dichroism

The B-form of DNA in the genome contains thousands of sequences that can form various noncanonical structures. Of particular interest are two structures namely G-quadruplex (G4), formed by two or more stacks of four guanine residues in a plane, and intercalating-motif (i-motif, iM) formed by alternately arranged C-C+ pairs. Circular dichroism (CD) spectroscopy is a fast biophysical technique to analyze G4s and iMs. We conducted a CD analysis of two types of DNA sequences, one containing tandem repeats and one without, for the generation of G4s and iMs under various environmental conditions, which include pH, buffer composition, boiling, with flanking sequences, complimentary DNA strands, and single-stranded DNA binding protein (SSB). Changes in pH and boiling caused drastic variations in the CD spectra of DNA containing tandem repeats of GGGGCC and GGCCCC from the C9ORF72 gene, although some changes in G4/iM-forming DNA from promoter-proximal regions of several oncogenes also occur. An increase in the number of hexanucleotide repeats generated complex CD patterns at specific pH due to the presence of both G and C bases. The presence of flanking sequences affects CD pattern of a mixture of G4- and iM-forming sequences of the c-MYC promoter-proximal region. SSB disassembled G4 and iMs of all sequences suggesting an in vivo role for SSBs in disassembly of G4s and iMs during various DNA transactions.

biophysics↗
bioRxiv2024

Ligify: Automated genome mining for ligand-inducible transcription factors

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/581298v1_ufig1.gif" ALT="Figure 1"> View larger version (8K): org.highwire.dtl.DTLVardef@15eef04org.highwire.dtl.DTLVardef@19e1851org.highwire.dtl.DTLVardef@4e9fe8org.highwire.dtl.DTLVardef@1e4628f_HPS_FORMAT_FIGEXP M_FIG C_FIG Prokaryotic transcription factors can be repurposed into biosensors for the ligand-inducible control of gene expression, but the landscape of chemical ligands for which biosensors exist is extremely limited. To expand this landscape, we developed Ligify, a web application that leverages information in enzyme reaction databases to predict transcription factors that may be responsive to user-defined chemicals. Candidate transcription factors are then incorporated into automatically generated plasmid sequences that are designed to express GFP in response to the target chemical. Our benchmarking analyses demonstrated that Ligify correctly predicted 31/100 previously validated biosensors, and highlighted strategies for further improvement. We then used Ligify to build a panel of genetic circuits that could induce a 47-fold, 5-fold, 9-fold, and 27-fold change in fluorescence in response to D-ribose, L-sorbose, isoeugenol, and 4-vinylphenol, respectively. Ligify should enhance the ability of researchers to quickly develop biosensors for an expanded range of chemicals, and is publicly available at https://ligify.streamlit.app.

synthetic biology↗
bioRxiv2023

ExoChew: An exonuclease technique to generate single-stranded DNA libraries

Although DNA in the genome is double-stranded, single-stranded DNA is generated during various processes including DNA replication and repair. Some single-stranded DNAs can form noncanonical structures. Various proteins bind to the single-stranded DNAs site-specifically and/or structure-specifically to regulate various DNA transactions. Because of the transient nature of single-stranded DNAs in the cell, current in vivo techniques may not reveal all such sequences, structures, and protein-DNA complexes. To explore such sequences and structures genome-wide, it is necessary to generate single-stranded DNA libraries. Current in vitro methods involve heat denaturation of libraries of double-stranded DNA fragments followed by cooling to prevent reannealing; however, a significant amount of DNA can reanneal to regenerate double-stranded DNAs. In ExoChew method, double-stranded DNA fragment libraries are enzymatically converted to single-stranded DNA libraries. Genomic DNA is sonicated to generate pools of double-stranded DNA fragments of required size. Each pool of double-stranded DNA fragments is then treated with either T7 exonuclease or E. coli exonuclease III. which recognize and cleave double-stranded DNA from 5 ends or 3 ends, generating single-stranded DNA pools, respectively. The enzymatically generated single-stranded DNA pools can be used for genome-wide studies of protein-DNA interactions and structural studies of DNA.

molecular biology↗
bioRxiv2023

Multielectrode Network Stimulation (ME-NETS) demonstrated by concurrent tDCS and fMRI

Non-invasive transcranial direct current stimulation (tDCS) can modulate activity of targeted brain regions. Whether tDCS can reliably and repeatedly modulate intrinsic connectivity of entire brain networks is unclear. We used concurrent tDCS-MRI to investigate the effect of high dose anodal tDCS on resting state connectivity within the Arcuate Fasciculus (AF) network, which spans the temporal, parietal, and frontal lobes and is connected via a structural backbone, the Arcuate Fasciculus (AF) white matter tract. Effects of high-dose tDCS (4mA) delivered via a single electrode placed over one of the AF nodes (single electrode stimulation, SE-S) was compared to the same dose split between multiple electrodes placed over AF-network nodes (multielectrode network stimulation, ME-NETS). While both SE-S and ME-NETS significantly modulated connectivity between AF network nodes (increasing connectivity during stimulation epochs), ME-NETS had a significantly larger and more reliable effect than SE-S. Moreover, comparison with a control network, the Inferior Longitudinal Fasciculus (ILF) network suggested that the effect of ME-NETS on connectivity was specific to the targeted AF-network. This finding was further supported by the results of a seed-to-voxel analysis wherein we found ME-NETS primarily modulated connectivity between AF-network nodes. Finally, an exploratory analysis looking at dynamic connectivity using sliding window correlation found strong and immediate modulation of connectivity during three stimulation epochs within the same imaging session.

neuroscience↗
bioRxiv2023

Spatial molecular imaging of the human type 2 diabetic islet

The islets of Langerhans are complex micro-organs comprised of multiple cell types essential for the maintenance of glucose homeostasis. The endocrine cell types of the islets engage in intimate, intercellular communication that is necessary for normal secretory activity. Disruption of this intercellular communication, which is at least partially dependent on the spatial organization of individual islets, leads to secretory dysfunction and exacerbation of the symptomology of disease states such as type 2 diabetes. However, the molecular determinants of the mechanisms underlying disrupted intercellular communication remain incompletely understood. Herein we describe the utilization of CosMx Spatial Molecular Imaging (SMI) to interrogate transcriptomic changes associated with the transition from the obese, prediabetic state to overt type 2 diabetes. Using SMI, we verified previously reported findings regarding islet composition in the obese and type 2 diabetic states, including loss of beta cells and expansion of alpha cell mass. In addition, we identified changes in the islet neighborhood that have implications for the function of islet endocrine cells. In particular, we identified a subset of alpha cells oriented in the periphery of the islets that appear to exhibit a transcriptomic profile suggestive of de-differentiation toward a beta cell-like transcriptome-type. To our knowledge, this is the first study utilizing spatial molecular imaging to investigate single-cell transcriptomes of individual islets. Further exploration of the intersection of islet architecture and gene expression using spatial technologies is expected to yield novel insights into the mechanisms underlying the development and progression of metabolic diseases like type 2 diabetes.

molecular biology↗