Biology subjects

Rahman, R.

Publications and source records attributed to Rahman, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Conformational Basis of Functionally Selective Allosteric Modulation of the Angiotensin II type 1 Receptor by Small Molecules

Blockade of signaling through the angiotensin II type 1 receptor (AT1R), a prototypical G protein-coupled receptor (GPCR), by angiotensin receptor blockers (ARBs) is a major therapeutic approach to treating a wide variety of cardiovascular and renal diseases1. Like most GPCRs, the AT1R signals through two transducers, G proteins and {beta}-arrestins2,3. Previous reports have described {beta}-arrestin-biased peptide orthosteric agonists for the AT1R with potential therapeutic advantages over currently available unbiased ARBs4-6. Here we report the DNA- encoded library screening-guided isolation and pharmacological characterization of the first small molecule AT1R allosteric ligands. We use cryo-electron microscopy, double electron- electron resonance spectroscopy, molecular dynamics simulations, and targeted mutagenesis to determine their binding sites, binding modes and conformational mechanisms driving their unique and divergent modulatory effects on G protein and {beta}-arrestin pathways. Our findings uncover new mechanisms for precisely controlling the dynamic behavior of the AT1R with implications for drug development targeting this pathophysiologically important receptor family.

biochemistry↗

bioRxiv2026

FluoVolt Staining Induces Photodamage During Live-Cell Voltage Imaging

Fluorescent voltage-sensitive dyes (VSDs) enable non-invasive, high-throughput optical measurement of membrane potential in living cells, but the analytical reliability of such measurements depends critically on whether the dye and associated imaging conditions perturb the system under study. Here, we systematically characterise the photophysical performance and cell-perturbing effects of FluoVolt, a widely adopted VSD, across cancer cell lines (GIN31 glioblastoma and SK-MEL-30 melanoma) and primary human macrophages. Photobleaching kinetics were strongly cell-type-dependent, with SK-MEL-30 cells exhibiting complete fluorescence loss within 400 seconds under standard widefield conditions. FluoVolt staining combined with laser excitation caused an approximately 2.5-fold increase in cell detachment relative to unstained controls, and dual-wavelength excitation (488 + 405 nm) reduced GIN31 cell viability by approximately 17.5%. Critically, morphological changes, a transition from elongated to amoeboid-like phenotypes, were detected under staining conditions alone, prior to any laser exposure, indicating baseline dye-induced perturbation independent of phototoxicity. Halving dye concentration and loading time significantly attenuated these effects while preserving measurable fluorescence signal. These findings identify FluoVolt staining and excitation as previously uncharacterised sources of systematic measurement artefact and provide practical, actionable guidance for protocol design, control selection, and data interpretation in optical membrane potential imaging.

biophysics↗

bioRxiv2026

Spatially confined niches support hypoxia-associated transcriptional plasticity contributing to malignant progression in IDH-mutant gliomas

BackgroundWhile hypoxia is a well-established driver of glioblastoma progression, its role in IDH-mutant gliomas, characterized by localized hypoxic microenvironments rather than overt necrosis, remains poorly understood. Here, we investigate how hypoxia and microenvironmental-adaptations shape cellular heterogeneity and transcriptional plasticity in these tumors. MethodsWe integrated bulk, single-cell, and spatial-transcriptomics datasets from IDH-mutant glioma patients (Astrocytomas and Oligodendrogliomas) to characterize cellular-states and map the localization of hypoxic niches. To uncover tumor microenvironment effects, we established co-culture models using primary IDH-mutant glioma cells with human microglia and astrocytes, maintained under hypoxic and normoxic conditions, followed by bulk RNA-sequencing. ResultsWe identified a hypoxia-associated astrocyte-like (AC-like) program that defines a quiescent, non-cycling population with a distinct transcription factor profile indicative of functional plasticity in IDH-mutant gliomas. These cells harbor glioma stem cell (GSC)-like features and are poised for a quiescent-to-activated (Q-to-A) transition that drives tumor progression. Mechanistically, co-culture models reveal that microglia promote this Q-to-A transition by enhancing HBEGF/EGFR paracrine signaling. Spatial transcriptomics uncovers the co-localization of hypoxic niches within quiescent AC-like cells, whereby the activated subpopulation forms discrete niches defined by localized HBEGF/EGFR communication gradients. Notably, tumors exhibiting elevated EGFR-driven activation signatures correlate with higher histological grade and poorer patient survival, implicating the Q-to-A transition as a critical driver of malignant progression. ConclusionQ-to-A transition within the hypoxic niche represents a critical driver of malignant progression in IDH-mutant gliomas, providing a microenvironment-driven mechanism for the transition to higher-grade disease and identifying targetable-vulnerabilities for therapeutic intervention. Key pointsO_LIHypoxic niches spatially confine quiescent, astrocyte-like cellular state characterized by glioma stem cell features in IDH-mutant gliomas. C_LIO_LIMicroglia triggers the quiescent-to-activated (Q-to-A) transition via paracrine EGFR signaling crosstalk. C_LIO_LIEGFR-driven Q-to-A plasticity serves as a microenvironmentally-regulated driver of malignant progression and adverse patient survival. C_LI Importance of the studyWhile high-grade glioblastomas feature well-defined hypoxic and necrotic regions that drive tumor progression and therapy resistance, IDH-mutant gliomas lack these distinct hallmarks, instead exhibiting disorganized hypoxic niches. Consequently, the functional contribution of these niches to tumor progression has remained poorly understood. Although genetic and epigenetic alterations are established drivers of progression in IDH-mutant gliomas, we provide an additional, essential layer of complexity: microenvironment-driven transcriptional plasticity. By uncovering the relationship between tumor hypoxia and heterogeneous glioma cell states, we uncover that these niches are essential for such plasticity. We also show that the tumor microenvironment provides critical molecular cues necessary to initiate a transcriptional shift enabling glioma cells to transition from a quiescent reservoir into an activated state primed for rapid proliferation. By identifying this paracrine mechanism, our work uncovers a targetable vulnerability that dictates how dormant cell reservoirs are mobilized to fuel malignant transformation.

cancer biology↗

bioRxiv2026

Foundation cell segmentation models performance on live microscopy and spatial-omics data

Accurate cell segmentation is an essential step for quantitative analysis of biological imaging data. Recent advances in deep learning have led to the development of generalist segmentation models that perform robustly across multiple imaging modalities, including label-free phase contrast, fluorescence cell culture, and multiplexed fluorescence tissue imaging. However, systematic comparisons of these models at the level of downstream biological analysis remain limited. To address this gap, we evaluated several recent segmentation models, including Cellpose cyto3, Cellpose-SAM, {micro}SAM, and CellSAM, on phase contrast and fluorescence cell culture images. In addition, Mesmer and InstanSeg were included for benchmarking on multiplexed fluorescence tissue images generated using CO-Detection by IndEXing (CODEX). We found that Cellpose-SAM achieved strong performance on phase contrast images, while SAM-based models consistently performed well on fluorescence cell culture data. In contrast, no single model consistently outperformed others on CODEX datasets. Instead, each model exhibited distinct strengths and limitations, which led to differences in downstream analyses, including clustering and cell type identification. Together, our study emphasizes the importance of selecting segmentation models based on dataset characteristics and analytical goals, rather than relying on a single universal approach.

bioinformatics↗

bioRxiv2026

Ribociclib as a Potential Multi-Target Inhibitor of Pro-Inflammatory Cytokines: An In Silico Investigation

Ribociclib, a selective cyclin-dependent kinase (CDK) 4/6 inhibitor, is approved as a first-line therapy for HR-positive/HER2-negative advanced breast cancer. Emerging evidence suggests that Ribociclib may exert immunomodulatory effects. However, its role in cytokine regulation remains largely unexplored. This study presents a comprehensive in silico investigation of Ribociclibs interactions with eight key pro-inflammatory cytokines--IL-6, TNF-, IL-17A, IL-17F, IL-17A/F, IL-1{beta}, MCP-1, and IFN-{gamma}. Computational assessments included molecular docking, molecular dynamics (MD) simulations, MM-GBSA binding free energy calculations, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analyses. Molecular docking and MD simulations indicated strong and stable complex formation with TNF-, IL-6, MCP-1, IL-1{beta}, and IL-17A/F. MM-GBSA results further showed that Ribociclib formed the most stable complexes with IL-17A/F ({Delta}Gbind = -25.94 kcal/mol) and MCP-1 ({Delta}Gbind = -25.88 kcal/mol), comparable to binding with the CDK-6 ({Delta}Gbind = -36.23 kcal/mol) control protein. PCA and DCCM analyses further supported the stabilizing influence of Ribociclib on these cytokine conformations. Moderate interactions were observed with TNF-, IL-6, and IFN-{gamma}. Collectively, these findings suggest that Ribociclib may function as a multi-target inhibitor capable of modulating diverse inflammatory pathways, providing a computational foundation for its repurposing as a cost-effective anti-inflammatory therapeutic candidate.

pharmacology and toxicology↗

bioRxiv2026

Single-Cell Metabolic Profiling in a Glioblastoma Co-culture Model Using AP-MALDI-based Mass Spectrometry Imaging

Mass spectrometry imaging enables spatially resolved, label-free detection of metabolites in tissue and culture systems, providing insight into their metabolic landscape and spatial distribution. However, conventional approaches often lack the spatial resolution and specificity needed to investigate metabolic heterogeneity at the single-cell level, particularly in physiologically relevant models. Here, we present a single-cell ambient mass spectrometry imaging platform, enabling direct chemical mapping of metabolites at 10 m resolution. This method integrates cell labelling, high resolution microscopy and AP-MALDI Orbitrap mass spectrometry imaging to achieve cell-type-specific metabolite profiling. To demonstrate its application, we applied this approach to glioblastoma (GBM), an aggressive adult brain tumour characterised by cellular heterogeneity, metabolic adaptation, and infiltrative growth within the tumour microenvironment. A co-culture model combining patient-derived glioblastoma invasive-margin cells with human cortical astrocytes was used to recapitulate the invasive niche. Distinct metabolic signatures emerged upon glioblastoma-astrocyte interaction, involving pathways related to nucleotide metabolism, phospholipid and sphingolipid turnover, and tryptophan and tyrosine metabolism. These findings suggest cell-type-specific metabolic activity and potential intercellular metabolic interplay. Overall, this workflow offers a broadly accessible and robust approach for investigating metabolic heterogeneity at cellular resolution, enabling insights into metabolic interactions of heterogenous cell types in both disease and non-disease settings.

cancer biology↗

bioRxiv2025

Functional Characterization of Arabidopsis α-Amylase3 (AMY3): Amylose Specificity and Structural Insights into Its Duplex Carbohydrate-Binding Module

Flowering plants contain a plastid-localized [a]-amylase (AMY3) with several recognized domains: two N-terminal carbohydrate-binding modules, an alpha-alpha hairpin that binds the catalytically inactive activator {beta}-amylase9, and a C-terminal catalytic domain. Despite considerable research on leaf starch metabolism in Arabidopsis, little is known about the function of AMY3. Starch is composed of two glucose polymers: highly branched amylopectin (70-90%) and largely unbranched amylose (10-30%). Because [a]-amylases are endo-amylases, we predicted that AMY3 might prefer to hydrolyze amylose, in contrast to the exo-amylase {beta}-amylase1 (BAM1), which we predicted would prefer amylopectin. Using iodine spectroscopy, we observed that AMY3 caused the percent amylose of corn starch to decrease, whereas BAM1 caused the percent amylose to increase before a gradual decrease. Enzyme assays revealed that AMY3 has a higher affinity for amylose compared to amylopectin, whereas BAM1 has a higher affinity for amylopectin. Furthermore, we found that the starch in amy3 leaves had twice as much amylose as WT leaves at the end of the day which suggests potential health and industrial applications. We purified the AMY3 CBM region and studied its structure with small-angle X-ray scattering, seeking to understand its role in substrate specificity. The AMY3 CBMs form a unique duplex where two interlocking {beta}-strands connect CBM1 and CBM2. The duplex CBM forms a dimer in solution and can bind amylose and amylopectin. However, AMY3 without the CBMs still prefers amylose, indicating the duplex CBM is not essential for amylose specificity in vitro. However, the duplex CBM is important for AMY3 dimerization.

biochemistry↗

bioRxiv2025

Structural and enzymatic divergence between human MDH isoforms underlies their specialized regulatory roles in metabolism

Malate dehydrogenase (MDH: EC:1.1.1.37) catalyzes a key NAD+-dependent redox reaction integral to cellular metabolism. In humans, the cytosolic (hMDH1) and mitochondrial (hMDH2) isoforms operate in distinct compartments, suggesting potential differences in regulation. Here, we present a comparative analysis of hMDH1 and hMDH2 under physiologically relevant conditions, integrating enzymatic assays, ligand binding studies, small-angle X-ray scattering (SAXS), and molecular modeling. Our findings reveal that hMDH2 activity is inhibited by -ketoglutarate, glutamate, NAD+, ATP, and citrate at concentrations consistent with mitochondrial metabolic states characterized by elevated amino acid catabolism or redox stress. Conversely, hMDH1 exhibits minimal impact by these metabolites, with only modest inhibition observed in the presence of ATP and ADP. SAXS analyses confirm that both isoforms maintain stable dimeric structures upon ligand binding, indicating that regulation is not mediated by global conformational changes. Structural modeling and normal mode analyses identify increased flexibility in hMDH1, particularly within the active site loop, thumb loop, and a partially disordered C-terminal helix. In contrast, hMDH2 displays a more rigid architecture and a more electropositive active site environment, correlating with its heightened sensitivity to anionic metabolites. Fluorescence quenching experiments further support these distinctions, demonstrating stronger binding affinities for nucleotide-based ligands in hMDH2 compared to hMDH1. Collectively, these results suggest that isoform-specific regulation of human MDH arises from differences in local structural dynamics and electrostatics, rather than large-scale structural rearrangements. hMDH2 appears adapted to integrate mitochondrial metabolic signals, modulating malate oxidation in response to cellular conditions, while hMDH1 maintains consistent cytosolic function across diverse metabolic states.

biochemistry↗

bioRxiv2025

Dyr726, a brain-penetrant inhibitor of PI3Kα, Type III receptor tyrosine kinases, and WNT signalling

The vast majority of clinical small molecule multi-kinase inhibitors (mKI) report abject failures in targeting cancers with high stem cell contents like high-grade glioma and colorectal cancers. The FDA-approved mKIs to date ablate receptor tyrosine kinase signaling but do not target the paradoxical WNT signaling which is a key survival driver for the self-renewing cancer stem cells. The WNT pathway enhances cancer plasticity and triggers relapse of highly heterogenous tumours. Using de novo synthesis and structure-activity-relationship (SAR) studies with blood-brain-barrier (BBB) penetrant mKI scaffolds, we designed a highly potent and selective small molecule inhibitor of PI3K, PDGFR/KIT, and the WNT pathway denoted Dyr726. Dyr726 is superior to clinical mKIs and inhibits PI3K-AKT-mTOR and WNT-pathway signaling at multiple nodes thereby impeding proliferation, invasion, and tumour growth. Phospho-proteomic, structural, and target engagement analyses, combined with in vitro, in vivo efficacy, and pharmacokinetic studies reveal that Dyr726 is a brain-penetrant small molecule which effectively reduces tumour volume and extends survival of murine orthotopic models. Our current work establishes a first-in-class brain penetrant small molecule mKI which simultaneously antagonize the PI3K-AKT-mTOR and WNT pathways in preclinical cancer stem cell cultures, adult and pediatric primary organoids, and orthotopic murine models with positive efficacy in combination with clinical standard of care.

cancer biology↗

bioRxiv2025

Single-cell transcriptomics reveals the impact of sex and age in the healthy human liver

Background & AimsThe liver is a vital organ composed of parenchymal, nonparenchymal, and immune cell populations. Single-cell sequencing approaches now provide the opportunity to understand how sex and age influence gene expression and cellular function across cell types within the liver. MethodsWe analyzed the cellular composition and interactions for the human liver through single-nucleus RNA sequencing (snRNA-seq), incorporating insights from 37 healthy liver samples. The dataset contains cells from female and male donors spanning more than seven decades of life, and analysis was performed to evaluate the impact of sex and age on differential gene expression, pathway enrichment, and predicted ligand-receptor and protein-protein interactions. ResultsExcluding the X and Y chromosomes, we identified 374 genes uniquely enriched in cells of the female liver and 520 genes enriched in cells of the male liver. Differential expression analysis defined unique circuitries enriched within each cell type between females and males and their impact on cell-cell communication and response to external signals, including enrichment of cholesterol/lipid metabolism, transforming growth factor beta (TGF-beta) signaling, and fibronectin (FN1) production in female cells and bone morphogenic protein (BMP) signaling in male cells. With increased age, we observe a greater diversity in gene expression, including enrichment of genes that regulate neuregulin (NGR) signaling at older ages, while genes regulating insulin growth factor (IGF) signaling are enriched at younger ages. Senescence signatures were also identified for each cell type within the liver. ConclusionsThese results define the activities of healthy cell types within the liver across sex and age and provide a foundation for studies to examine how ancestry, geography, and disease states influence liver function within these contexts. Impact and ImplicationsOur study analyzes 37 human liver samples at the single-cell level to understand how sex and age influence gene expression, cell interactions, and response to signals across liver cell types and sub-types. These findings are of particular significance for researchers who need to understand how sex and age may influence the response of individual cell types to injury or treatment of injury. This dataset will also provide a healthy reference for future studies to understand how ancestry, geography, and disease states shape liver biology across age and sex.

cell biology↗

bioRxiv2024

CCGM: a Compound Coarse Grain Model representation for enhanced chemotype exploration, annotation and screening

Structurally similar compounds often exhibit similar bioactivity, making similarity estimation an essential step in many cheminformatics workflows. Traditionally, compound similarity has been evaluated using diverse molecular representations, such as molecular fingerprints, compound 3D structural features, and physicochemical properties. These methods have proven effective, particularly during the early stages of drug discovery, where the primary goal is to identify initial hits from large compound libraries. However, these representation and methods often fall short during the hit-to-lead development phase, where modifications to the core scaffold or chemotype are performed and evaluated. To address this limitation, we developed the Compound-Coarse-Grain-Model (CCGM), a framework that represents structural features of a compound as nodes and edges within a simplified graph. This approach augments the pharmacophore and chemotype features of the compound within the graph, enabling the identification of compounds with similar chemotype and pharmacophore features more effectively than conventional methods. CCGM is particularly useful for when screening large libraries to identify compounds with similar chemotypes and for filtering generative designs to retain designs with similar pharmacophore features.

bioinformatics↗

bioRxiv2024

The impact of wild-boar derived microbiota transplantation on piglet microbiota, metabolite profile and gut proinflammatory cytokine production differs from sow-derived microbiota

Colonization of co-evolved, species-specific microbes in early life plays a crucial role in gastrointestinal development and immune function. This study hypothesized modern pig production practices have resulted in the loss of co-evolved species and critical symbiotic host- microbe interactions. To test this, we reintroduced microbes from wild boars (WB) into conventional piglets to explore their colonization dynamics and effects on gut microbial communities, metabolite profiles, and immune responses. At postnatal day (PND) 21, 48 piglets were assigned to four treatment groups: 1) WB-derived mixed microbial community (MMC), 2) sow-derived MMC, 3) a combination of WB and sow MMC (Mix), or 4) Control (PBS). Post- transplantation analyses at PND 48 revealed distinct microbial communities in WB-inoculated piglets compared to Controls, with trends toward differentiation from Sow but not Mix groups. WB-derived microbes were more successful in colonizing piglets, particularly in the Mix group, where they competed with sow-derived microbes. WB group cecal digesta enriched with Lactobacillus helveticus, Lactobacillus mucosae, and Lactobacillus pontis. Cecal metabolite analysis showed that WB piglets were enriched in histamine, acetyl-ornithine, ornithine, citrulline, and other metabolites, with higher histamine levels linked to Lactobacillus abundance. WB piglets exhibited lower cecal IL-1{beta} and IL-6 levels compared to Controls and Sow groups, while the Mix group showed reduced IFN-{gamma}, IL-2, and IL-6 compared to the Sow group. No differences in weight gain, fecal scores, or plasma cytokines were observed, indicating no adverse effects. These findings support that missing WB microbes effectively colonize domestic piglets and may positively impact metabolite production and immune responses. ImportanceThis study addresses the growing concern over losing co-evolved, species-specific microbes in modern agricultural practices, particularly in pig production. The implementation of strict biosecurity measures and widespread antibiotic use in conventional farming systems may disrupt crucial host-microbe interactions that are essential for gastrointestinal development and immune function. Our research demonstrates that by reintroducing wild boar-derived microbes into domestic piglets, these co-evolved microbes can successfully colonize the gut, influence microbial community composition, and alter metabolite profiles and immune responses without causing adverse effects. These findings also suggest that these co-evolved microbes can fill an intestinal niche, positively impacting immune activation. This research lays the groundwork for future strategies to enhance livestock health and performance by restoring natural microbial populations that produce immune-modulating metabolites. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/623678v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@6ec5c2org.highwire.dtl.DTLVardef@79804corg.highwire.dtl.DTLVardef@d63016org.highwire.dtl.DTLVardef@1ef0010_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Leveraging Machine Learning and AlphaFold2 Steering to Discover State-Specific Inhibitors Across the Kinome

Protein kinases are structurally dynamic proteins that control downstream signaling cascades by phosphorylating their substrates. Protein kinases regulate their function by adopting several conformational states in their active site determined by the movements of several motifs such as the C-Helix, DFG residues and the activation loop. Each conformational state represents a distinct physicochemical environment that accepts or precludes ligand binding. However, most of the kinome have not been crystalized across these possible conformational states. It has been shown that shallow Multiple Sequence Alignments (MSA) can enable AlphaFold2 (AF2) to model kinases in alternative conformations. However, it is unclear if these models can be leveraged for structure-based drug discovery. Additionally, there are several machine learning tools to predict protein-ligand interactions based on ligand chemotype and binding pocket properties, but these models cannot be used to identify ligands with clear state specificity. Here, we first present an approach called AlphaFold2 Steering (AF2-Steering), a systematic methodology to direct AF2 to sample kinases in the active and inactive conformations. We use our approach to model the protein kinome in precise conformational states. We demonstrate the utility of these AF2-steered kinase models by employing them in a prospective virtual screening study that integrates machine learning with docking to find state specific inhibitors for well-studied and dark kinases that lack structures in the active conformational state. We then experimentally validate the hits, an essential step often overlooked, and later experimentally confirm the conformation-specificity of the ligands identified for FLT3, a protein kinase that currently lacks an active state crystal structure. Against a strict binding criterion of at least 1M Kd, our modelled structures achieved an overall hit rate of 53%. We also confirm the conformation-specificity of 4/7 FLT3 ligands, thus demonstrating the value of MSA-steered AF2 modelled kinase structures combined with machine learning and docking to guide conformation-specific kinase drug discovery.

bioinformatics↗

bioRxiv2024

Eco-Microbiology: Discovering Biochemical Enhancers of PET Biodegradation by Piscinibacter sakaiensis

The scale of plastic pollution boggles the mind. Nearly 400 megatons of virgin plastics are produced annually, with an environmental release rate of 80 percent; and plastic waste including micro- and nanoplastics are associated with a plethora of problems. The naturally evolved abilities of plastic-degrading and consuming microbes offer a starting point for generating sustainable and eco-centric solutions to plastic pollution. Here we developed an iterative discovery procedure coupling faster quasi-high-throughput polyethylene terephthalate (PET) dependent bioactivity screens with longer-term PET biodegradation assays to find small molecule and ionic boosters of PET consumption by the bacterium Piscinibacter sakaiensis. We discovered multiple hits supporting greater than 2-fold enhancement of PET biodegradation - with hits belonging to a small but heterogeneous set of compounds and mixtures, suggesting upregulation of PET consumption via multiple paths. This work has the potential to advance the creation of a fermentation-based process for solving PET plastic pollution. ImportancePlastic pollution is an urgent environmental issue. In addition, micro- and nanoplastics (MNPs) have become an acute source of worry with discoveries of the global distribution and transport of MNPs, their presence within a diversity of organisms including common foodstuffs and human tissues, and their potential association with declining fertility and various disease states. Solutions are needed and the microbial world offers abundant help via naturally evolved biodegraders of plastic waste. We created a non-genetic method to accelerate polyethylene terephthalate (PET) plastic biodegradation by Piscinibacter sakaiensis, a bacterium that evolved to slowly but completely consume PET. Our method entails a combination of plastic-dependent bioactivity screens and slower biodegradation tests to find extrinsic biochemical stimulators of PET biodegradation. The conditions we found boost PET biodegradation by over two-fold and provide a foundation for further studies to realize a fermentation-based process needed to solve PET plastic pollution.

microbiology↗

bioRxiv2024

A comparison of wild boar and domestic pig microbiota does not reveal a loss of microbial species but an increase in alpha diversity and opportunistic genera in domestic pigs

The microbiome of wild animals is believed to be co-evolved with host species, which may play an important role in host physiology. It has been hypothesized that the rigorous hygienic practice in combination with antibiotics and diets with simplified formulas used in the modern swine industry may negatively affect the establishment and development of the gut microbiome. In this study, we evaluated the fecal microbiome of 90 domestic pigs sampled from 9 farms in Canada and 39 wild pigs sampled from three different locations on two continents (North America and Europe) using 16S rRNA gene amplicon sequencing. Surprisingly, the gut microbiome in domestic pigs exhibited higher alpha-diversity indices than wild pigs (P<0.0001). The wild pig microbiome showed a lower Firmicutes to Bacteroidetes ratio and a higher presence of bacterial phyla Elusimicrobiota, Verrucomicrobiota, Cyanobacteria, and Fibrobacterota compared to their domestic counterparts. At the genus level, wild pig microbiome had enriched genera that were known for fibre degradation and short-chained fatty acids production. Interestingly, the phylum Fusobacteriota was only observed in domestic pigs. We identified 31 ASVs that were commonly found in the pig gut microbiome regardless of host sources, which could be recognized as members of the core gut microbiome. Interestingly, we found a few ASVs missing in domestic pigs that were prevalent in wild ones, whereas domestic pigs harbored 59 ASVs that were completely absent in wild pigs. The present study sheds light on the impact of domestication on the pig gut microbiome, including the gain of new genera. ImportanceThe microbiome of pigs plays a crucial role in shaping host physiology and health. This study looked to identify if domestication and current rearing practices have resulted in a loss of co-evolved bacterial species by comparing the microbiome of wild boar and conventionally raised pigs. It represents a comparison of domestic and wild pigs with the largest sample sizes, and is the first to examine wild boars from multiple sites and continents. We were able to identify core microbiome members that were shared between wild and domestic populations, and counter to expectation, few microbes were identified to be lost from wild boar. Nevertheless, the microbiome of wild boars was distinct from domestic pigs, with notably lower abundance of important pathogenic genera. The differences in microbial composition may identify an opportunity to shift the microbial community of domestic pigs towards that of wild boar with the intent to reduce pathogen load.

microbiology↗

bioRxiv2024

Leveraging multiple data types for improved compound-kinase bioactivity prediction

Machine learning methods offer time- and cost-effective means for identifying novel chemical matter as well as guiding experimental efforts to map enormous compound-kinase interaction spaces. However, considerable challenges for compound-kinase interaction modeling arise from the heterogeneity of available bioactivity readouts, including single-dose compound profiling results, such as percentage inhibition, and multi-dose-response results, such as IC50 . Standard activity prediction approaches utilize only dose-response data in the model training, disregarding a substantial portion of available information contained in single-dose measurements. Here, we propose a novel machine learning methodology for compound-kinase activity prediction that leverages both single-dose and dose-response data. Our two-stage model first learns a mapping between single-dose and dose-response bioactivity readouts, and then generates proxy dose-response activity labels for compounds that have only been tested in single-dose assays. The predictions from the first-stage model are then integrated with experimentally measured dose-response activities to model compound-kinase binding based on chemical structures and kinase features. We demonstrate that our two-stage approach yields accurate activity predictions and significantly improves model performance compared to training solely on dose-response labels, particularly in the most practical and challenging scenarios of predicting activities for new compounds and new compound scaffolds. This superior performance is consistent across five evaluated machine learning methods, including traditional models such as random forest and kernel learning, as well as deep learning-based approaches. Using the best performing model, we carried out extensive experimental profiling on a total of 347 selected compound-kinase pairs, achieving a high hit rate of 40% and a negative predictive value of 78%. We show that these rates can be improved further by incorporating model uncertainty estimates into the compound selection process. By integrating multiple activity data types, we demonstrate that our approach holds promise for facilitating the development of training activity datasets in a more efficient and cost-effective way.

bioinformatics↗

bioRxiv2024

Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cell types and their heterogeneity within the human liver, but the spatial organization at single-cell resolution has not yet been described. Here we apply multiplexed error robust fluorescent in situ hybridization (MERFISH) to map the zonal distribution of hepatocytes, resolve subsets of macrophage and mesenchymal populations, and investigate the relationship between hepatocyte ploidy and gene expression within the healthy human liver. We next integrated spatial information from MERFISH with the more complete transcriptome produced by single-nucleus RNA sequencing (snRNA-seq), revealing zonally enriched receptor-ligand interactions. Finally, analysis of fibrotic liver samples identified two hepatocyte populations that are not restricted to zonal distribution and expand with injury. Together these spatial maps of the healthy and fibrotic liver provide a deeper understanding of the cellular and spatial remodeling that drives disease which, in turn, could provide new avenues for intervention and further study.

genomics↗

bioRxiv2023

Wireless Electrical-Molecular Quantum Signalling for Cancer Cell Induced Death

Quantum biological tunnelling for electron transfer (QBET) is involved in controlling cellular behaviour. Control of electrical-molecular communication could revolutionise the development of disruptive technologies for understanding and modulating electrically induced molecular signalling. Current communication technology is not appropriate for interfacing with cells at a spatial/temporal level equivalent to the native biological signalling. This limits our ability to tune cell function by controlling single molecular events. Here, we merge wireless nano-electrochemical tools with cancer cells. Gold-bipolar nanoelectrodes functionalised with redox active species were developed as electric field stimulated bio-actuators, that we term bio-nanoantennae. We show that a remote electrical input regulates electron transport between the redox molecules on the bio-nanoantennae in a selective manner. The wireless modulation of electron transport results in QBET triggering apoptosis in patient-derived cancer cells, representing electrical-induced induced controlled molecular signalling. Transcriptomics data highlight the electric field-induced nanoantenna targets the cancer cells in a unique manner. The insight concerning action and functional nanomaterials opens a plethora of applications in healthcare. This approach may lead to new quantum-based medical diagnostics and treatments, as well as a fundamental understanding of biological physics.

biophysics↗