Biology subjects

Harris, B.

Publications and source records attributed to Harris, B..

11 recordsLinked to original sources

bioRxiv2026

Distinct lifespan trajectories of cortical intrinsic timescales and neural oscillations

The brain integrates information over time through the persistence of neural activity, yet how this persistence changes across the human lifespan and the mechanisms underlying these changes remain unclear. Using resting-state magnetoencephalography from three independent cohorts (n = 1,156; ages 6-88 years), we systematically examined lifespan changes in neural temporal dynamics, focusing on cortical intrinsic timescales and alpha oscillatory frequency. We found that cortical intrinsic timescales shortened from approximately 29 ms in childhood to 9 ms in late adulthood, with the steepest decline occurring before age 30. This compression was reproducible across cohorts and observed in all cortical regions, while the sensory-to-association hierarchy of timescales remained preserved throughout life. By contrast, alpha frequency followed a distinct trajectory, peaking in early adulthood before declining after midlife. Individualized corticothalamic models linked timescale compression to stronger intrathalamic inhibition, whereas increased conduction delays partly accounted for alpha slowing. Further network simulations indicated that intrinsic timescales primarily reflect collective network dynamics rather than single-neuron kinetics. Together, these findings reveal distinct lifespan trajectories of neural persistence and oscillatory rhythms, implicating collective corticothalamic dynamics in the temporal organization of the human brain across development and ageing.

neuroscience↗

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↗

bioRxiv2026

An integrated multimodal pan-organ atlas of the female reproductive system across the lifespan contextualises gynaecological pathologies

Single cell transcriptomics has transformed our knowledge of reproductive tissues, yet studies remain largely organ-specific and temporally limited, leaving an incomplete picture of how cell types are distributed across the reproductive system over a lifetime. Gynaecological conditions affect more than one in four females and frequently span multiple organs and life stages. To advance our understanding and treatment of these conditions, an integrated cellular reference is essential. Here we present the Human Female Reproductive System Cell Atlas v1: a single-cell transcriptomic resource integrating more than 2M cells across the ovary, fallopian tube, uterus, cervix and vagina over the lifespan and menstrual cycle, further integrated with spatial transcriptomics and chromatin accessibility profiling to define 210 cell types through community-based annotation. Cross-organ integration resolves shared and organ-specific cellular states, identifying uterine-specific perivascular populations lining uterine spiral arteries, hypoxia-sensing type 3 innate lymphoid cells (ILC3s) enriched in the uterus, and lipid-associated macrophages with distinct subsets in each reproductive organ, including a previously undescribed population shared between the uterus and fallopian tube. Cross-organ integration enables detection of ectopic epithelial populations in otherwise healthy donors, including endometrial-like cells within a paediatric ovary consistent with early endometriosis. Integration with genome-wide association studies (GWAS) reveals that risk variants for major gynecological conditions act in mesenchymal cell states defined by specific transcriptional programmes and spatial or temporal context - for instance, heavy menstrual bleeding risk is enriched in basal fibroblasts (SFRP5) of the regenerative endometrial compartment. An integrated chromatin accessibility atlas provides peak-to-gene maps across reproductive cell types, enabling nomination of disease effector genes and providing the first regulatory evidence linking a Polyendocrine Metabolic Ovarian Syndrome (PMOS) risk locus to INHBB in granulosa cells. Together, this resource establishes a cellular and molecular framework for reproductive biology and the pathogenesis of neglected gynaecological conditions.

Cell Biology↗

bioRxiv2024

Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI

The interdisciplinary time-series analysis literature encompasses thousands of statistical features for quantifying interpretable properties of dynamical data. But for any given application, it is likely that just a small subset of informative time-series features is required to capture the dynamical quantities of interest. So, while comprehensive libraries of time-series features have been developed, it is useful to construct reduced and computationally efficient subsets for specific applications. In this work, we demonstrate a systematic process to deduce such a reduced set, focused on the problem of distinguishing changes to functional Magnetic Resonance Imaging (fMRI) time series caused by a range of experimental manipulations of excitatory and inhibitory neural activity in mouse cortical circuits. We reduce a comprehensive library of over 7000 candidate time-series features down to a subset of 16 features, which we call catchaMouse16, that aims to both: (i) accurately characterize biologically relevant properties of fMRI time series; and (ii) minimize inter-feature redundancy. The catchaMouse16 feature set accurately classifies experimental perturbations of neuronal activity from fMRI recordings, and also shows strong generalization performance on an unseen mouse and human resting-state fMRI data where it tracks spatial variations in excitatory and inhibitory cortical cell densities, often with greater statistical power than the full hctsa feature set. We provide an efficient, open-source implementation of the catchaMouse16 feature set in C (achieving an approximately 60 times speed-up relative to the native Matlab code of the same features), with wrappers for Python and Matlab. This work demonstrates a procedure to reduce a large candidate time-series feature set down to the key statistical properties of mouse fMRI dynamics that can be used to efficiently quantify and interpret informative dynamical patterns in neural time series.

neuroscience↗

bioRxiv2024

Large-scale single-cell profiling of stem cells uncovers redundant regulators of shoot development and yield trait variation

Stem cells in plant shoots are a rare population of cells that produce leaves, fruits and seeds, vital sources for food and bioethanol. Uncovering regulators expressed in these stem cells will inform crop engineering to boost productivity. Single-cell analysis is a powerful tool for identifying regulators expressed in specific groups of cells. However, accessing plant shoot stem cells is challenging. Recent single-cell analyses of plant shoots have not captured these cells, and failed to detect stem cell regulators like CLAVATA3 and WUSCHEL. In this study, we finely dissected stem cell-enriched shoot tissues from both maize and arabidopsis for single-cell RNA-seq profiling. We optimized protocols to efficiently recover thousands of CLAVATA3 and WUSCHEL expressed cells. A cross-species comparison identified conserved stem cell regulators between maize and arabidopsis. We also performed single-cell RNA-seq on maize stem cell overproliferation mutants to find additional candidate regulators. Expression of candidate stem cell genes was validated using spatial transcriptomics, and we functionally confirmed roles in shoot development. These candidates include a family of ribosome-associated RNA-binding proteins, and two families of sugar kinase genes related to hypoxia signaling and cytokinin hormone homeostasis. These large-scale single-cell profiling of stem cells provide a resource for mining stem cell regulators, which show significant association with yield traits. Overall, our discoveries advance the understanding of shoot development and open avenues for manipulating diverse crops to enhance food and energy security.

plant biology↗

bioRxiv2023

Ribosomal protein control of hematopoietic stem cell transformation through direct, non-canonical regulation of metabolism

We report here that expression of the ribosomal protein, RPL22, is frequently reduced in human myelodysplastic syndrome (MDS) and acute myelogenous leukemia (AML); and, reduced RPL22 expression is associated with worse outcomes. Mice null for Rpl22 display characteristics of an MDS-like syndrome and develop leukemia at an accelerated rate. Rpl22-deficient mice also display enhanced hematopoietic stem cell (HSC) self-renewal and obstructed differentiation potential, which arises not from reduced protein synthesis but from altered metabolism including increased fatty acid oxidation (FAO) and a striking induction of the stemness factor Lin28b in the resulting leukemia. Lin28b promotes a substantial increase in lipid content, upon which the survival of Rpl22-deficient leukemias depends. Altogether, these findings reveal that Rpl22 insufficiency enhances the leukemia potential of HSC through regulation of FAO and promotes leukemogenesis through Lin28b promotion of lipid synthesis. HighlightsO_LIRPL22 insufficiency is observed in MDS/AML and is associated with reduced survival C_LIO_LIRpl22-deficiency produces an MDS-like syndrome and facilitates leukemogenesis C_LIO_LIRpl22-deficiency does not impair global protein synthesis by HSC C_LIO_LIRpl22 controls leukemia survival through control of lipid synthesis C_LI eTOC: Rpl22 controls the function and transformation potential of hematopoietic stem cells through regulation of lipid metabolism.

cancer biology↗

bioRxiv2022

RTEL-1 and DNA polymerase theta promote subtelomeric DNA synthesis and telomere fusion in C. elegans

Interstitial telomere sequences (ITS) are degenerate telomere tracts scattered along chromosome arms whose functions are not well understood. We found that critically shortened telomeres of C. elegans telomerase mutants initiate DNA synthesis within ITS tracts that were close to or far from a telomere. Some ITS tracts were targeted recurrently. RTEL-1 dismantles T-loops and recombination intermediates, and DNA polymerase theta (POLQ-1) promotes end-joining using short segments of microhomology. In telomerase mutants, RTEL-1 and POLQ-1 promoted telomere fusion and DNA synthesis at subtelomeric ITS tracts. RTEL-1 is known to suppress homologous recombination, and we found that RTEL-1 similarly suppressed POLQ-1-mediated double-strand break repair. Mutation signatures characteristic of repair by POLQ-1 occurred during initiation of subtelomeric DNA synthesis and at subsequent template shifting events. We propose that RTEL-1 and POLQ-1 play distinct essential roles in subtelomeric DNA synthesis, a process that may contribute significantly to telomere fusion and tumor genome evolution.

genetics↗

bioRxiv2021

An allelic series of spontaneous mutations in Rorb cause a gait phenotype, retinal abnormalities, and transcriptomic changes relevant to human neurodevelopmental conditions

Rorb encodes the Retinoic Acid Receptor-related orphan receptor beta. Mutations in either of the two transcripts of Rorb cause defects in multiple systems, including abnormal photoreceptor abundance and morphology in the retina and a characteristic "high-stepper" or "duck-like" gait arising from dysfunction of interneurons in the spinal cord. Rorb is also important for cortical development and cell fate specification in mice. Rorb variants segregate with epilepsy and comorbidities such as intellectual disability in numerous clinical cases. Here we describe five mouse strains with spontaneous mutations in Rorb identified by their gait phenotype. These mutations affect different domains and isoforms of Rorb, which correspond to the spectrum of anatomical and physiological phenotypes exhibited by these mice. Gene set analysis in Rorb mutants implicates pathways associated with development and nervous system function, and differential gene expression analysis indicates changes in numerous genes related to epilepsy, bipolar disorder, and autism spectrum disorder (ASD). Many of these genes and their protein products are known to interact during synapse formation and neuronal activity. These findings further illuminate the role of Rorb in nervous system development, provide further evidence for an association between Rorb and several neurological conditions, and describe an allelic series of Rorb mutant mice that will be useful for dissecting thalamocortical afferent (TCA) development, neural cell fate determination, and as animal models exhibiting transcriptomic shifts in neurological conditions such as epilepsy, bipolar disorder, and ASD. {blacksquare}Five mutant mice with a characteristic high-stepper gait phenotype have mutations in Rorb {blacksquare}These allelic series mutations manifest in a spectrum of anatomical and physiological abnormalities {blacksquare}Gene expression data suggest involvement of pathways related to neurodevelopmental disorders

genetics↗

bioRxiv2021

Non-additive QTL mapping of lactation traits in 124,000 sequence-imputed cattle reveals novel recessive loci

Deleterious recessive conditions have primarily been studied in a Mendelian disease context. Recently, several large effect, deleterious recessive mutations were discovered via non-additive GWAS of quantitative growth and developmental traits in cattle. This showed quantitative traits can be used as proxies of genetic disorders if they are indicative of whole animal health status and susceptible to underlying genetic conditions. Lactation traits might also reflect genetic disorders in cattle, given the increased energy demands of lactation and the substantial stresses imposed on the animal. Here, we report a screen of over 124,000 cows for recessive effects based on lactation traits. We discovered novel loci associated with five large recessive impacts on milk yield traits represented by missense variants (DOCK8, IL4R, KIAA0556, and SLC25A4) or premature stop variants (ITGAL, LRCH4, and RBM34) as candidate causal mutations. On milk composition traits, we identified several small effect dominance contributions to previously reported additive QTL. In contrasting analyses of milk yield and milk composition phenotypes, we note differing genetic architectures. Milk yield phenotypes presented lower heritabilities and fewer additive QTL, but higher non-additive genetic variance and a higher proportion of loci exhibiting dominance compared to milk composition phenotypes. Large-effect recessive QTL are segregating at surprisingly high frequencies in cattle. We speculate that the differences in genetic architecture between milk yield and milk composition phenotypes derive from underlying dissimilarities in the cellular and molecular representation of these traits. Lactation yields may act as a better proxy than milk composition traits for a wide range of underlying biological disorders affecting animal fitness

genetics↗

bioRxiv2019

The orphan nuclear receptor estrogen-related receptor beta (ERRβ) in triple-negative breast cancer

PurposeTriple negative breast cancer (TNBC)/ basal-like breast cancer (BLBC) is a highly aggressive form of breast cancer prevalent in African-American (AA) women. We previously reported that a small molecule agonist ligand for the orphan nuclear receptor estrogen-related receptor beta (ERR{beta} or ESRRB) has growth inhibitory and anti-mitotic activity in TNBC cell lines. In this study, we evaluate the association of ESRRB mRNA, copy number levels, and protein expression with demographic, clinicopathological, and gene expression features in breast tumor clinical specimens.\n\nMethodsESRRB mRNA level expression and clinical associations were analyzed using RNAseq data. Array-based comparative genomic hybridization determined ESRRB copy number in AA and Caucasian women. Transcription factor activity was measured using promoter-reporter luciferase assays in TNBC cell lines. Semi-automatic quantification of immunohistochemistry measured ERR{beta} protein expression on a 150-patient tissue microarray series.\n\nResultsESRRB mRNA expression is significantly lower in TNBC/BLBC vs. other breast cancer subtypes. There is no evidence of ESRRB copy number loss. ESRRB mRNA expression is correlated with the expression of genes associated with neuroactive ligand-receptor interaction, metabolic pathways, and deafness. These genes contain G/C-rich transcription factor binding motifs. The ESRRB message is alternatively spliced into three isoforms, which we show have different transcription factor activity in basal-like vs. other TNBC cell lines. We further show that the ERR{beta}2 and ERR{beta}sf isoforms are broadly expressed in breast tumors at the protein level.\n\nConclusionsDecreased ESRRB mRNA expression, and distinct patterns of ERR{beta} isoform subcellular localization and transcription factor activity are key features in TNBC/BLBC.

cancer biology↗