Biology subjects

Johnson, B.

Publications and source records attributed to Johnson, B..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Quantification of collagen and associated features from H&E-stained whole slide pathology images across cancer types using a physics-based deep learning model

BackgroundCollagen is the major component of the extracellular matrix (ECM). Collagen structural organization undergoes significant transformation during tumorigenesis. The visualization of collagen in histological tissue sections would aid in the study of tumor growth, encapsulation, and invasion. However, such visualization requires the use of special stains such as Picrosirius Red (PSR) or Massons Trichrome (MT), or more recently, second-harmonic generation imaging (SHG) in unstained tissue sections. However, PSR and MT both suffer from significant inter- (and intra-) lab stain variabilities, and SHG, while considered a ground truth by many, suffers from issues of system complexity/reliability, cost, and speed/throughput. These technical hurdles limit more widespread assessment of collagen in tissue samples. MethodsUsing high-contrast, high-throughput polarization imaging on PSR-stained slides to generate ground truth training polarization images, we developed a deep learning model (iQMAI) to infer the presence of collagen directly from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with high specificity. After iQMAI inference across WSIs, individual collagen fibers were extracted, and features describing overall collagen intensity and fiber morphology were computed. iQMAI pixel-and feature-wise outputs were compared to ground truth polarization imaging to assess model performance. The trained iQMAI model was deployed on H&E-stained WSI from the TCGA LUAD, LUSC, LIHC, and PAAD datasets for evaluation. iQMAI-derived collagen features were compared to tissue composition, gene expression, and overall survival. ResultsThe iQMAI model shows significant generalization across multiple indications. iQMAI collagen predictions were similar to polarization imaging measurements of the same sample, with a mean structural similarity index (SSIM) of 0.84 (95% CI 0.69-0.93), a mean patch-wise RMSE of 0.04 (95% CI 0.02-0.08), and a linear correlation (R2=0.93). Comparing features of the collagen fibers extracted from iQMAI vs. polarization images yielded similar linear correlations between computed fiber tortuosity, length, width, and relative angle. The relationship between collagen fiber density and fibroblast density was distinct in non-small cell lung cancer (LUAD and LUSC), hepatocellular carcinoma (LIHC), and pancreatic ductal adenocarcinoma (PAAD). In PAAD, fiber density and fiber width were both negatively associated with the LRRC-15 gene expression signature, and increased fiber width was associated with longer overall survival. ConclusionsiQMAI is a deep learning model that accurately predicts collagen from an H&E-stained WSI, allowing for spatially resolved quantification of collagen morphology and enabling investigation of the interplay between collagen and other TME components. We demonstrate an example of the utility of iQMAI-based collagen assessment in PAAD, where collagen features are associated with immunosuppressive cancer-associated fibroblasts and overall survival. Understanding the relationship between collagen, the tumor microenvironment composition, and disease progression may aid the development of effective immunotherapies in PAAD and other cancer types.

pathology↗

bioRxiv2025

Neoplastic immune mimicry is a generalizable phenomenon in breast cancer and epithelial CD69 enables early tumor progression

Dedifferentiation programs are commonly enacted during breast cancer progression to enhance tumor cell fitness. Increased cellular plasticity within the neoplastic compartment of tumors correlates with disease aggressiveness, often culminating in greater resistance to cytotoxic therapies or augmented metastatic potential. Here we report that subpopulations of dedifferentiated neoplastic breast epithelial cells express canonical leukocyte cell surface receptor proteins and have thus named this cellular program "immune mimicry." We document neoplastic cells engaging in immune mimicry within public human breast tumor single-cell RNA-seq datasets, histopathological breast tumor specimens, breast cancer cell lines, as well as in murine transgenic and cell line-derived mammary cancer models. Immune-mimicked neoplastic cells harbor hallmarks of dedifferentiation and are enriched in treatment-resistant and high-grade breast tumors. We corroborated these observations in aggressive breast cancer cell lines where anti-proliferative cytotoxic chemotherapies drove epithelial cells toward immune mimicry. Moreover, in subsequent proof-of-concept studies, we demonstrate that expression of the CD69 leukocyte activation protein by neoplastic cells confers a proliferative advantage that facilitates early tumor growth and therefore conclude that neoplastic breast epithelial cells upregulating leukocyte surface receptors potentiate malignancy. Moving forward, neoplastic immune mimicry should be evaluated for prognostic utility in additional breast cancer cohorts to determine its potential for patient stratification. Future research should evaluate correlates with distal metastases, progression-free survival, overall survival, and therapeutic response/resistance. Statement of SignificanceNeoplastic breast epithelial cells express surface receptors canonically attributed to leukocytes and are associated with therapy resistance and aggressive tumor behavior.

cancer biology↗

bioRxiv2024

Advancing Plant Metabolic Research By Using Large Language Models To Expand Databases And Extract Labelled Data

Premise: Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, revealed that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been revealed with the advent of accessible large language models, which, even in their early stages, can distill structured data from literature. This brings us closer to creating specialized databases that consolidate virtually all published knowledge on a topic. Methods: Here, we first test different prompt engineering technique / language model combinations in the identification of validated enzyme-product pairs. Next, we evaluate automated prompt engineering and retrieval augmented generation applied to identifying compound-species associations. Finally, we build and determine the accuracy of a multimodal language model-based pipeline that transcribes images of tables into machine-readable formats. Results: When tuned for each specific task, these methods perform with high accuracies (80-90 percent for enzyme-product pair identification and table image transcription), or with modest accuracies (50 percent) but lower false-negative rates than previous methods (down to 40 percent from 55 percent) for compound-species pair identification. Discussion: We enumerate several suggestions for working with language models as researchers, among which is the importance of the users domain-specific expertise and knowledge. Significance StatementScientific databases have played a major role in advancing metabolic research. However, even todays advanced databases are incomplete and/or are not built to best suit certain research tasks. Here, we explored and evaluated the use of large language models and various prompt engineering techniques to expand and subset existing databases in task-specific ways. Our results illustrate the potential for high-accuracy additions and restructurings of existing databases using language models, assuming the specific methods by which the models are used are tuned and validated for the specific task. These findings are important because they outline a method by which we could greatly expand existing databases and rapidly tailor them to specific research efforts, leading to greater research productivity and effective utilization of past research findings. All authors collected data, analyzed data, prepared the manuscript, and approved its final version. The authors declare that they have no competing interests.

plant biology↗

bioRxiv2024

Allele-specific silencing of a dominant SETX mutation in familial amyotrophic lateral sclerosis type 4

Amyotrophic lateral sclerosis 4 (ALS4) is an autosomal dominant motor neuron disease that is molecularly characterized by reduced R-loop levels and caused by pathogenic variants in senataxin (SETX). SETX encodes an RNA/DNA helicase that resolves three-stranded nucleic acid structures called R-loops. Currently, there are no disease-modifying therapies available for ALS4. Given that SETX is haplosufficient, removing the product of the mutated allele presents a potential therapeutic strategy. We designed a series of siRNAs to selectively target the RNA transcript from the ALS4 allele containing the c.1166T>C mutation (p.Leu389Ser). Transfection of HEK293 cells with siRNA and plasmids encoding either wild-type or mutant (Leu389Ser) epitope tagged SETX revealed that three siRNAs specifically reduced mutant SETX protein levels without affecting the wild-type SETX protein. In ALS4 primary fibroblasts, siRNA treatment silenced the endogenous mutant SETX allele, while sparing the wild-type allele, and restored R-loop levels in patient cells. Our findings demonstrate that mutant SETX, differing from wild-type by a single nucleotide, can be effectively and specifically silenced by RNA interference, highlighting the potential of allele-specific siRNA as a therapeutic approach for ALS4.

genetics↗

bioRxiv2024

Gut and oral microbial community characterization from women with breast cancer, women with ductal carcinoma in situ, and healthy women reveals differences in gut but not oral microbiota

This study characterized and compared the fecal and oral microbiota from women with early-stage breast cancer (BC), women with ductal carcinoma in situ (DCIS), and healthy women. Fecal and oral samples were collected from newly diagnosed patients prior to any therapy and characterized using 16S rRNA sequencing. Measures of gut microbial alpha diversity were significantly lower in the BC versus healthy cohort. Beta diversity differed significantly between the BC or DCIS and healthy groups and several differentially abundant taxa were identified. Clustering (non-negative matrix factorization) of the gut microbiota identified 5 bacterial guilds dominated by either Prevotella, Enterobacteriaceae, Akkermansia, Clostridiales, or Bacteroides. The Bacteroides and Enterobacteriaceae guilds were significantly more abundant in the BC cohort compared to healthy controls whereas the Clostridiales guild was more abundant in the healthy group. Finally, prediction of functional pathways identified 23 pathways that differed between the BC and healthy gut microbiota including lipopolysaccharide biosynthesis, glycan biosynthesis and metabolism, lipid metabolism, and sphingolipid metabolism. In contrast to the gut microbiomes, there were no significant differences in alpha or beta diversity in the oral microbiomes and very few differentially abundant taxa were observed. NMF analysis of the oral microbiota samples identified 7 guilds dominated by Veillonella, Prevotella, Gemellaceae, Haemophilus, Neisseria, Propionibacterium, and Streptococcus, however, none of these guilds were differentially associated with the different cohorts. Our results suggest that alterations in the gut microbiota, but not oral microbiota, may provide the basis for interventions targeting the gut microbiome to improve treatment outcomes and long-term prognosis. IMPORTANCEEmerging evidence suggests that the gut microbiota may play a role in breast cancer. Few studies have evaluated both the gut and oral microbiomes in women with breast cancer (BC) and none have characterized these microbiomes in women with ductal carcinoma in situ (DCIS). We surveyed the gut and oral microbiomes from women with BC or DCIS and healthy women and identified compositional and functional features of the gut microbiota that differed between these cohorts. In contrast, very few differential features were identified in the oral microbiota. These findings suggest that the oral microbiome is unlikely to be an effective risk marker for DCIS or breast cancer compared to the gut microbiome. Understanding the role of gut bacteria in BC and DCIS may open up new opportunities for the development of novel markers for early detection (or markers of susceptibility) as well as new strategies for prevention and/or treatment.

microbiology↗

bioRxiv2024

Biophysics-based protein language models for protein engineering

Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure, and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose Mutational Effect Transfer Learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure, and energetics. We finetune METL on experimental sequence-function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity, and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METLs ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.

bioinformatics↗

bioRxiv2024

Phylochemical mapping of natural products onto the plant tree of life using text mining and large language models.

Plants produce a staggering array of chemicals that are the basis for organismal function and diversity and also provide essential human nutrients and medicine. However, it is poorly defined how these compounds have evolved and are distributed across the diverse lineages of the plant kingdom, hindering a systematic view and understanding of plant chemical diversity. Recent advances in plant genome/transcriptome sequencing have provided a well-defined molecular phylogeny of plants, on which the presence of diverse natural products can be mapped to systematically determine their phylogenetic distribution. Here, we built a proof-of-concept workflow via which previously reported diverse tyrosine-derived plant natural products were mapped on to the plant tree of life. Plant chemical-species associations were mined from literature, filtered, evaluated through manual inspection of over 2,500 scientific articles, and mapped onto the plant phylogeny. The resulting phylochemical map confirmed several highly lineage-specific compound class distributions, such as betalain pigments and Amaryllidaceae alkaloids. The map also highlighted several lineages enriched in dopamine-derived compounds, including the orders Caryophyllales, Liliales, and Fabales. Additionally, the application of large language models using our manually curated data as a ground truth set showed that post-mining manual processing steps can largely be automated with a low false positive rate. Our study demonstrates that a workflow combining text mining with language model-based processing can generate broader phylochemical maps, which will serve as a critical community resource to uncover key evolutionary events that underlie plant chemical diversity and enable system-level views of natures millions of years of chemical experimentation. Significance StatementThis study introduces a novel workflow to map the distribution of natural products onto a phylogeny. It also demonstrates the effectiveness of combining text mining of the literature with large language model evaluation for constructing a phylochemical database that can organize vast portions of the chemical diversity present across the plant tree of life.

plant biology↗

bioRxiv2024

A missense variant effect map for the human tumour suppressor protein CHK2

The tumour suppressor CHEK2 encodes the serine/threonine protein kinase CHK2 which, upon DNA damage, is important for pausing the cell cycle, initiating DNA repair and inducing apoptosis. CHK2 phosphorylation of the tumour suppressor BRCA1 is also important for mitotic spindle assembly and chromosomal stability. Consistent with its cell cycle checkpoint role, both germline and somatic variants in CHEK2 have been linked to breast and multiple other cancer types. Over 90% of clinical germline CHEK2 missense variants are classified as variants of uncertain significance, complicating diagnosis of CHK2-dependent cancer. We therefore sought to test the functional impact of all possible missense variants in CHK2. Using a scalable multiplexed assay based on the ability of human CHK2 to complement DNA sensitivity of a S. cerevisiae lacking its ortholog RAD53, we generated a systematic missense variant effect map for CHEK2 missense variation. Map scores reflect known biochemical features of CHK2 and exhibit good performance in separating pathogenic from benign clinical missense variants. Thus, the missense variant effect map for CHK2 offers value in understanding both known and yet-to-be-observed CHK2 variants.

genetics↗

bioRxiv2023

Organ specific microenvironmental MR1 expression in cutaneous melanoma

The microenvironment is an important regulator of intertumoral trafficking and activity of immune cells. Understanding how the immune system can be tailored to maintain anti-tumor killing responses in metastatic disease remains an important goal. Thus, immune mediated eradication of metastasis requires the consideration of organ specific microenvironmental cues. Using a xenograft model of melanoma metastasis in adult zebrafish, we perturbed the dynamic balance between the infiltrating immune cells in the metastatic setting using a suite of different transgenic zebrafish. We employed intravital imaging coupled with metabolism imaging (FLIM) to visualize and map the organ specific metabolism with near simultaneity in multiple metastatic lesions. Of all the MHC complexes examined for brain and skeletal metastases, we determined that there is an organ specific expression of mhc1uba (human ortholog, MR1) for both the melanoma cells and the resident and infiltrating immune cells. Specifically, immune clusters did not express mhc1uba in brain metastatic lesions in immune competent fish. Finally, the differential immune response drove organ specific metabolism where tumor glycolysis was increased in brain metastases compared to skeletal and parental lines as measured using fluorescence lifetime imaging microscopy (FLIM). As MR1 belongs to the MHC class I molecules and is a target of immunotherapeutic drugs, we believe that our data presents an opportunity to understand the relationship between organ specific tumor metabolism and drug efficacy in the metastatic setting.

cancer biology↗

bioRxiv2023

Optical manipulation reveals macrophage mediated mesoscale brain mechanical homeostasis in vivo

Tissues are active materials where epithelial turnover, immune surveillance, and remodeling of stromal cells such as macrophages all regulate form and function. Scattering modalities such as Brillouin microscopy (BM) can non-invasively access mechanical signatures at GHz. However, our traditional understanding of tissue material properties is derived mainly from modalities which probe mechanical properties at different frequencies. Thus, reconciling measurements amongst these modalities remains an active area. Here, we compare optical tweezer active microrheology (OT-AMR) and Brillouin microscopy (BM) to longitudinally map brain development in the larval zebrafish. We determine that each measurement is able to detect a mechanical signature linked to functional units of the brain. We demonstrate that the corrected BM-Longitudinal modulus using a density factor correlates well with OT-AMR storage modulus at lower frequencies. We also show that the brain tissue mechanical properties are dependent on both the neuronal architecture and the presence of macrophages. Moreover, the BM technique is able to delineate the contributions to mechanical properties of the macrophage from that due to colony stimulating factor 1 receptor (CSF1R) mediated stromal remodeling. Here, our data suggest that macrophage remodeling is instrumental in the maintenance of tissue mechanical homeostasis during development. Moreover, the strong agreement between the OT-AM and BM further demonstrates that scattering-based technique is sensitive to both large and minute structural modification in vivo.

biophysics↗

bioRxiv2023

A complex of the lipid transport ER proteins TMEM24 and C2CD2 with band 4.1 at cell-cell contacts

Junctions between the ER and the plasma membrane (ER/PM junctions) are implicated in calcium homeostasis, non-vesicular lipid transfer and other cellular functions. Two ER proteins that function both as membrane tethers to the PM via a polybasic motif in their C-terminus and as phospholipid transporters are brain-enriched TMEM24 (C2CD2L) and its paralog C2CD2. Based on an unbiased proximity ligation analysis, we found that both proteins can also form a complex with band 4.1 family members, which in turn can bind a variety of plasma membrane proteins including cell adhesion molecules such as SynCAM 1. This complex results in the enrichment of TMEM24 and C2CD2 containing ER/PM junctions at sites of cell contacts. Dynamic properties of TMEM24-dependent ER/PM contacts are impacted when in complex as TMEM24 present at cell adjacent junctions is not shed by calcium rise, unlike TMEM24 at non-cell adjacent junctions. These findings suggest that cell-contact interactions control ER/PM junctions via TMEM24 complexes involving band 4.1 proteins. SUMMARYThe ER-anchored lipid transfer proteins TMEM24/C2CD2L and its paralog C2CD2 mediate formation of ER-plasma membrane junctions at sites of cell-cell contacts by interacting with band 4.1 family members and indirectly with cell adhesion proteins.

cell biology↗

bioRxiv2023

A novel protocol for magnetoencephalographic mapping of speech motor control regions during speech production.

Neuroimaging protocols for mapping of expressive speech centres employ several standard speech tasks including object naming, rhyming, and covert word production (Agarwal et al., 2019). These tasks reliably elicit activation of distributed speech centres in prefrontal, precentral and cingulate motor cortices and are widely used for presurgical mapping and in research studies of language production. In the present study we used an alternative speech protocol employing reiterated productions of simple disyllabic nonwords (VCV; Anastasopoulou et al., 2022; van Lieshout et al., 2007). Here we show that this task elicits highly focal and highly lateralised activations of speech motor control areas centred on the precentral gyrus and adjacent portions of the middle frontal gyrus. 10 healthy adults, 19 typically developing children and 7 children with CAS participated in the study. MEG scans were carried out with a whole-head MEG system consisting of 160 first-order axial gradiometers with a 50 mm baseline (Model PQ1160R-N2, KIT, Kanazawa, Japan). MEG data were acquired with analog filter settings of 0.03 Hz high-pass, 1,000 Hz low-pass, 4,000 Hz sampling rate. Measurements were carried out with participants in supine position in a magnetically shielded room (Fujihara Co. Ltd., Tokyo, Japan). Time-aligned speech acoustics were recorded in an auxiliary channel of the MEG setup at the same sample rate as the MEG recordings. Brain activity was recorded while participants produced reiterated utterances of /ipa/ and /api/, at normal and speeded rates in addition to a button press task (right index finger) to elicit activity in the hand region of sensorimotor cortex (e.g. Johnson et al., 2020). MEG data were co-registered with individual structural MRI scans obtained in a separate scanning session. Source reconstruction was performed with synthetic aperture magnetometry (SAM) beamformer implemented in Matlab (Jobst et al., 2018) and group statistics performed with permutation tests (p < 0.05). Button press map shows clusters encompassing dorsal precentral and postcentral gyri (Brodmann areas 4 and 6), corresponding to hand sensorimotor cortices. Speech map shows clusters encompassing precentral gyrus immediately ventral to hand motor cortex (BA6), and an immediately adjacent portion of the posterior middle frontal gyrus. Both button press and speech result in a robust desynchronisation restricted within a frequency band of about 13-30 Hz (beta band). Our results show that the reiterated speech task results in robust beta-band desynchronisation in a highly focal region of the precentral gyrus, located immediately ventral to the hand motor region of the precentral gyrus. In adults, speech motor -related brain activity was predominantly observed in the left hemisphere. Typically developing children, on the other hand, exhibited bilateral activation and in the case of individuals with CAS exhibited only right-hemisphere activation. Taken together the present findings provide a non-invasive and highly selective window on a crucial node of the expressive speech network that has previously been accessed only with invasive electrophysiological means and lesion studies.

neuroscience↗

bioRxiv2023

SARS-CoV-2 hijacks fragile X mental retardation proteins for efficient infection

Viruses interact with numerous host factors to facilitate viral replication and to dampen antiviral defense mechanisms. We currently have a limited mechanistic understanding of how SARS-CoV-2 binds host factors and the functional role of these interactions. Here, we uncover a novel interaction between the viral NSP3 protein and the fragile X mental retardation proteins (FMRPs: FMR1 and FXR1-2). SARS-CoV-2 NSP3 mutant viruses preventing FMRP binding have attenuated replication in vitro and have delayed disease onset in vivo. We show that a unique peptide motif in NSP3 binds directly to the two central KH domains of FMRPs and that this interaction is disrupted by the I304N mutation found in a patient with fragile X syndrome. NSP3 binding to FMRPs disrupts their interaction with the stress granule component UBAP2L through direct competition with a peptide motif in UBAP2L to prevent FMRP incorporation into stress granules. Collectively, our results provide novel insight into how SARS-CoV-2 hijacks host cell proteins for efficient infection and provides molecular insight to the possible underlying molecular defects in fragile X syndrome.

microbiology↗

bioRxiv2023

Membrane Topology of UbiA Prenyltransferase Domain-Containing Protein-1 (UBIAD1), a Novel Regulator of Cholesterol Homeostasis

UbiA prenyltransferase domain containing protein-1 (UBIAD1) is a polytopic membrane-bound enzyme that synthesizes the vitamin K2 subtype menaquinone-4 (MK-4) by conjugating the prenyl group of geranylgeranyl pyrophosphate (GGpp) to the aromatic acceptor menadione. The enzyme moonlights as a regulator endoplasmic reticulum (ER)-localized 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), the rate limiting enzyme in the synthesis of sterol and nonsterol isoprenoids. When cells are replete with isoprenoids, UBIAD1 constitutively cycles between membranes of the medial-trans Golgi and the ER. When ER membranes become depleted of GGpp, UBIAD1 becomes trapped in the organelle where it binds to and inhibits the ER-associated degradation (ERAD) of HMGCR. This inhibition permits continued synthesis of nonsterol isoprenoids, even when sterols are abundant. The resultant accumulation of GGpp in the ER causes dissociation of the HMGCR-UBIAD1 complex, which allows maximal ERAD of HMGCR and translocation of UBIAD1 to the Golgi. These findings disclose a novel GGpp sensing mechanism that allows for metabolically-regulated, intracellular trafficking of UBIAD1. However, the mechanism for this GGpp-induced transport remains to be determined. In the current study, we use cysteine derivatization and protease protection assays to determine the membrane topology of UBIAD1. These findings are key to the determination of mechanisms through which GGpp modulates the intracellular trafficking of UBIAD1 and ultimately, the ERAD of HMGCR.

biochemistry↗

bioRxiv2023

Estimating single cell clonal dynamics in human blood using coalescent theory

While evolutionary approaches to medicine show promise, measuring evolution itself is difficult due to experimental constraints and the dynamic nature of body systems. In cancer evolution, continuous observation of clonal architecture is impossible, and longitudinal samples from multiple timepoints are rare. Increasingly available DNA sequencing datasets at single cell resolution enable the reconstruction of past evolution using mutational history, allowing for a better understanding of dynamics prior to detectable disease. We derive methods based on coalescent theory for estimating the net growth rate of clones from either reconstructed phylogenies or the number of shared mutations. Using single-cell datasets from blood, we apply and validate our analytical methods for estimating the net growth rate of hematopoietic clones, eliminating the need for complex simulations. We show that our estimates may have broad applications to improve mechanistic understanding and prognostic ability. Compared to clones with a single or unknown driver mutation, clones with multiple drivers have significantly increased growth rates (median 0.94 vs. 0.25 per year; p = 1.6 x 10-6). Further, stratifying patients with a myeloproliferative neoplasm (MPN) by the growth rate of their fittest clone shows that higher growth rates are associated with shorter time from clone initiation to MPN diagnosis (median 13.9 vs. 26.4 months; p = 0.0026).

evolutionary biology↗

bioRxiv2023

Identification of two β-cell subtypes by 7 independent criteria

Despite the recent explosion in surveys of cell-type heterogeneity, the mechanisms that specify and stabilize highly related cell subtypes remain poorly understood. Here, focusing initially on exploring quantitative histone mark heterogeneity, we identify two major sub-types of pancreatic {beta}-cells ({beta}HI and {beta}LO). {beta}HI and {beta}LO cells differ in their size, morphology, cytosolic and nuclear ultrastructure, transcriptional output, epigenomes, cell surface marker, and function. Importantly, {beta}HI and {beta}LO cells can be FACS separated live into CD24+ ({beta}HI) and CD24- ({beta}LO) fractions. From an epigenetic viewpoint, {beta}HI-cells exhibit [~]4-fold higher levels of H3K27me3, more compacted chromatin, and distinct chromatin organization that associates with a specific pattern of transcriptional output. Functionally, {beta}HI cells have increased mitochondrial mass, activity, and insulin secretion both in vivo and ex vivo. Critically, Eed and Jmjd3 loss-of-function studies demonstrate that H3K27me3 dosage is a significant regulator of {beta}HI / {beta}LO cell ratio in vivo, yielding some of the first-ever specific models of {beta}-cell sub-type distortion. {beta}HI and {beta}LO sub-types are conserved in humans with {beta}HI-cells enriched in human Type-2 diabetes. These data identify two novel and fundamentally distinct {beta}-cell subtypes and identify epigenetic dosage as a novel regulator of {beta}-cell subtype specification and heterogeneity. HighlightsO_LIQuantitative H3K27me3 heterogeneity reveals 2 common {beta}-cell subtypes C_LIO_LI{beta}HI and {beta}LO cells are stably distinct by 7 independent sets of parameters C_LIO_LIH3K27me3 dosage controls {beta}HI / {beta}LO ratio in vivo C_LIO_LI{beta}HI and {beta}LO cells are conserved in humans and enriched in Type-2 diabetes C_LI

cell biology↗

bioRxiv2023

Improvements to the Gulf Pipefish Syngnathus scovelli Genome

The Gulf pipefish Syngnathus scovelli has emerged as an important species in the study of sexual selection, development, and physiology, among other topics. The fish family Syngnathidae, which includes pipefishes, seahorses, and seadragons, has become an increasingly attractive target for comparative research in ecological and evolutionary genomics. These endeavors depend on having a high-quality genome assembly and annotation. However, the first version of the S. scovelli genome assembly was generated by short-read sequencing and annotated using a small set of RNA-sequence data, resulting in limited contiguity and a relatively poor annotation. Here, we present an improved genome assembly and an enhanced annotation, resulting in a new official gene set for S. scovelli. By using PacBio long-read high-fidelity (Hi-Fi) sequences and a proximity ligation (Hi-C) library, we fill small gaps and join the contigs to obtain 22 chromosome-level scaffolds. Compared to the previously published genome, the gaps in our novel genome assembly are smaller, the N75 is much larger (13.3 Mb), and this new genome is around 95% BUSCO complete. The precision of the gene models in the NCBIs eukaryotic annotation pipeline was enhanced by using a large body of RNA-Seq reads from different tissue types, leading to the discovery of 28,162 genes, of which 8,061 were non-coding genes. This new genome assembly and the annotation are tagged as a RefSeq genome by NCBI and thus provide substantially enhanced genomic resources for future research involving S. scovelli.

genomics↗

bioRxiv2022

Leptospermum extract (QV0) suppresses pleural mesothelioma tumour growth in vitro and in vivo by mitochondrial dysfunction associated apoptosis

Pleural mesothelioma (PM) is a highly aggressive, fast-growing asbestos-induced cancer with limited effective treatments. There has been an interest in using naturally occurring anticancer agents derived from plant materials for the treatment of PM. However, it is unclear if aqueous extract from the Leptospermum polygalifolium (QV0) has activity against PM. Here we investigated the anti-cancer property of QV0 in vitro and in vivo. Animals treated with Defender(R) (QV0 dietary supply) exhibited a reduced tumour size over 30 days, which was associated with an average extended of seven days mouse life. There was no liver toxicity, nor increased blood glucose post-treatment in animals treated with Defender(R). Moreover, QV0 suppressed the growth of 13 cancer cell lines in a dose-dependent manner, effective at concentrations as low as 0.02% w/v. This response was found to be associated with inhibited cell migration, proliferation, and colony formation, but without evident cell cycle alteration. We observed mitochondrial dysfunction post QV0 treatment, as evidenced by significantly decreased basal and maximal oxygen consumption rates. Significantly enhanced tumour apoptosis was observed in the Defender(R)-treated animals, correlating with mitochondrial dysfunction. To the best of our knowledge, this study constitutes the first demonstration of an improved host survival (without adverse effects) response in a QV0-treated PM mouse model, associated with an evident inhibition of PM cell growth and mitochondrial dysfunction-related enhancement of tumour apoptosis. ImportanceA major problem with cancer chemotherapy or immunotherapy is the severe adverse effects associated with normal tissue damage. PM is known to be treatment resistant and has poor a prognosis, therefore new therapeutic treatment options are urgently needed. In the present study, we explored the potential utility of a Leptospermum extract (QV0) as a treatment option for mesothelioma. We demonstrated for the first time that QV0 exhibits an anti-tumour response in mesothelioma, without any associated adverse effects observed in the PM mouse model. These findings provide a rationale for early-stage clinical trials. We anticipate that prospective translational research will lead to the clinical implementation of a novel QV0-based treatment strategy that will ultimately benefit PM patients.

cancer biology↗