Biology subjects

An, L.

Publications and source records attributed to An, L..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

RAS/PI3K pathway mutations sensitise epithelial ovarian cancer cells to a PARP/NAMPT inhibitor combination

The combination of PARP and NAMPT inhibitors (PARPi/NAMPTi) has been explored for the treatment of TNBC, Ewing Sarcoma and high grade serous carcinoma (HGSC). However, dose limiting toxicity has hampered NAMPTi in clinical trials. To maximise the therapeutic window, we set out to identify predictive genomic biomarkers. Bioinformatic analysis and screening of a panel of epithelial ovarian cancer (EOC) cell lines revealed that cells with RAS/PI3K pathway mutations were sensitive to the NAMPTi FK866. Activity of olaparib and FK866 was associated with a reduction in nicotinamide mononucleotide (NMN) and the PARP substrate nicotinamide adenine dinucleotide (NAD+), with coincident increases in ROS production, DNA damage and apoptosis induction. Caspase 3/7 activity was upregulated to a greater extent in RAS/PI3K mutant cell lines. Finally, the combination significantly reduced omental tumour weight and increased overall survival in mice injected with ID8 Trp53-/-;Pten-/- cells. This study highlights the potential of the PARPi/NAMPTi combination in RAS/PI3K pathway mutant EOC.

cancer biology↗

bioRxiv2024

Modeling protein-small molecule conformational ensembles with ChemNet

Modeling the conformational heterogeneity of protein-small molecule interactions is important for understanding natural systems and evaluating designed systems, but remains an outstanding challenge. We reasoned that while residue level descriptions of biomolecules are efficient for de novo structure prediction, for probing heterogeneity of interactions with small molecules in the folded state an entirely atomic level description could have advantages in speed and generality. We developed a graph neural network called PLACER (Protein-Ligand Atomistic Conformational Ensemble Resolver) trained to recapitulate correct atomic positions from partially corrupted input structures from the Cambridge Structural Database and the Protein Data Bank; the nodes of the graph are the atoms in the system. PLACER accurately generates structures of diverse organic small molecules given knowledge of their atom composition and bonding, and given a description of the larger protein context, builds up structures of small molecules and protein side chains for protein-small molecule docking. Because PLACER is rapid and stochastic, ensembles of predictions can be readily generated to map conformational heterogeneity. In enzyme design efforts described here and elsewhere, we find that using PLACER to assess the accuracy and pre-organization of the designed active sites results in higher success rates and higher activities; we obtain a preorganized retroaldolase with a kcat/KM of 11000 M-1min-1, considerably higher than any pre-deep learning design for this reaction. We anticipate that PLACER will be widely useful for rapidly generating conformational ensembles of small molecule and small molecule-protein systems, and for designing higher activity preorganized enzymes.

biochemistry↗

bioRxiv2024

Protective role of parenthood on age-related brain function in mid- to late-life

The experience of parenthood can profoundly alter ones body, mind, and environment, yet we know little about the long-term associations between parenthood and brain function and aging in adulthood. Here, we investigate the link between number of children parented (parity) and age on brain function in 19,964 females and 17,607 males from the UK Biobank. In both females and males, increased parity was positively associated with functional connectivity, particularly within the somato/motor network. Critically, the spatial topography of parity-linked effects was inversely correlated with the impact of age on functional connectivity across the brain for both females and males, suggesting that a higher number of children is associated with patterns of brain function in the opposite direction to age-related alterations. These results indicate that the changes accompanying parenthood may confer benefits to brain health across the lifespan, highlighting the importance of future work to understand the associated mechanisms.

neuroscience↗

bioRxiv2024

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

neuroscience↗

bioRxiv2024

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a "meta-matching" framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants ("meta-matching finetune" and "meta-matching stacking") from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = -0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.

neuroscience↗

bioRxiv2023

Atomic context-conditioned protein sequence design using LigandMPNN

Protein sequence design in the context of small molecules, nucleotides, and metals is critical to enzyme and small molecule binder and sensor design, but current state-of-the-art deep learning-based sequence design methods are unable to model non-protein atoms and molecules. Here, we describe a deep learning-based protein sequence design method called LigandMPNN that explicitly models all non-protein components of biomolecular systems. LigandMPNN significantly outperforms Rosetta and ProteinMPNN on native backbone sequence recovery for residues interacting with small molecules (63.3% vs. 50.4% & 50.5%), nucleotides (50.5% vs. 35.2% & 34.0%), and metals (77.5% vs. 36.0% & 40.6%). LigandMPNN generates not only sequences but also sidechain conformations to allow detailed evaluation of binding interactions. Experimental characterization demonstrates that LigandMPNN can generate small molecule and DNA-binding proteins with high affinity and specificity. One-sentence summaryWe present a deep learning-based protein sequence design method that allows explicit modeling of small molecule, nucleotide, metal, and other atomic contexts.

biochemistry↗

bioRxiv2023

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles

A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition. One Sentence SummaryWe use a pseuodocycle-based shape complementarity optimizing approach to design nanomolar binders to diverse ligands, including the flexible and polar methotrexate and thyroxine, that can be directly converted into ligand-gated nanopores and chemically induced dimerization systems.

biochemistry↗

bioRxiv2023

Molecular heterogeneity of quiescent melanocyte stem cells revealed by single-cell RNA-sequencing.

Melanocyte stem cells (McSCs) of the hair follicle are a rare cell population within the skin and are notably underrepresented in whole-skin, single-cell RNA sequencing (scRNA-seq) datasets. Using a cell enrichment strategy to isolate KIT+/CD45-cells from the telogen skin of adult female C57BL/6J mice, we evaluated the transcriptional landscape of quiescent McSCs (qMcSCs) at high resolution. Through this evaluation, we confirmed existing molecular signatures for qMcCS subpopulations (e.g., Kit+, Cd34+/-, Plp1+, Cd274+/-, Thy1+, Cdh3+/-) and identified novel qMcSC subpopulations, including two that differentially regulate their immune privilege status. Within qMcSC subpopulations, we also predicted melanocyte differentiation potential, neural crest potential, and quiescence depth. Taken together, the results demonstrate that the qMcSC population is heterogenous and future studies focused on investigating changes in qMcSCs should consider changes in subpopulation composition. SignificanceSingle cell transcriptomics has revolutionized our ability to interrogate the dynamic nature of tissues. Here we provide a high-resolution map of the melanocyte stem cell population during quiescence. This map provides one of few examples highlighting broad heterogeneity in stem cells during the quiescent cell state. The map also unifies previous observations using other cell, molecular and functional analyses to define the unique features of the quiescent melanocyte stem cell population. This data provides a valuable resource to individuals interested in further evaluating aspects of cellular quiescence in stem cells broadly or melanocyte stem cells specifically.

cell biology↗

bioRxiv2023

Serine hydroxymethyl transferase is a binding target of caprylic acid: Uncovering a novel molecular target for a herbicide and for producing caprylic acid-tolerant crops

Identification of new binding targets is essential for the development of herbicides and phytotoxin-tolerant crops. Caprylic acid (CAP) is a safe and non-selective bio-herbicide in uncultivated areas. However, the herbicidal action of CAP remains unclear. Herein, metabolomic and proteomic profiling indicated that a serine hydroxymethyl transferase in Conyza canadensis (CcSHMT1) is a promising candidate binding targeted for CAP. The protein abundance and activity of CcSHMT1 were decreased in a time- and dosage-dependent manners after CAP treatment. CAP competes with phenyl-serine at the binding sites, decreasing the enzymatic activity of CcSHMT1. Overexpression of CcSHMT1, AtSHMT1 and OsSHMT1 in Arabidopsis or rice endowed plants with high tolerance to CAP treatment, whereas the knockout of osshmt1 led to death of plants under normal atmospheric conditions. Furthermore, T194A, T194A/ M195V and D209N Osshmt1 mutant plants derived from base editing exhibited tolerance to CAP. CAP bound to CcSHMT1 with high affinity, and Ala191 in the key domains of N-terminus was identified to be critical for the binding site of CAP. Collectively, our findings demonstrate that CcSHMT1 is a binding target for herbicidal activity of CAP. This study marks a key step in the druggability of SHMT inhibitors and represents an attractive target for phytotoxin-tolerant crops.

plant biology↗

bioRxiv2023

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.

neuroscience↗

bioRxiv2023

A wave of minor de novo DNA methylation initiates in mouse 8-cell embryos and co-regulates imprinted X- chromosome inactivation

DNA methylation is extensively reprogrammed during early stage of mammalian development and is essential for normal embryogenesis. It is well established that mouse embryos acquire genome-wide DNA methylation during implantation, referred to as de novo DNA methylation, from globally hypomethylated blastocysts. However, the fact that the main de novo DNA methyltransferase 3B (DNMT3B) is initially expressed as early as the 8-cell stage, contradicts the current knowledge about timing of initiation of de novo DNA methylation. Here, we reported that a previously overlooked minor wave of de novo DNA methylation initially occurs during the transition from the 8-cell to blastocyst stage, before the well-known large-scale de novo DNA methylation during implantation. Functional analyses indicated that minor de novo DNA methylation regulates proliferation, lineage differentiation and metabolic homeostasis of preimplantation embryos, and is critical for embryonic developmental potential and pregnancy outcomes. Furthermore, bioinformatic and functional analyses indicated that minor de novo DNA methylation preferentially occurs on the X chromosome and co-regulates imprinted X-chromosome inactivation via the interaction between DNMT3B and polycomb repressive complexes 2 core components during blastocyst formation. Thus, our study updates the current knowledge of embryonic de novo DNA methylation, thereby providing a novel insight of early embryonic epigenetic reprogramming. Summary statementA minor wave of de novo DNA methylation has been initiated prior to blastocyst formation, but not during the implantation period, and co-regulates imprinted X-chromosome inactivation.

developmental biology↗

bioRxiv2023

Diversity and distribution of viruses inhabiting the oil reservoirs

Oil reservoirs, being one of the most significant subsurface repositories of energy and carbon, have long hosted diverse microorganisms affecting energy production and carbon emissions. Viruses play crucial roles in the ecology of microbiomes, however, their distribution and ecological significance in oil reservoirs remain undetermined. Here, we assembled an extensive catalogue encompassing viral and prokaryotic genomes sourced from oil reservoirs. The catalogue comprises 7,229 prokaryotic genomes and 6,218 viral genomes from 182 oil reservoir metagenomes, respectively. Based on sequence clustering we identified 3,886 Operational Taxonomic Units (vOTUs) approximately at species level, 94.80% of which were not found in any other environments. The results showed that viruses were widely distributed in oil reservoirs, and oil reservoirs contain a significant number of unique and unexplored viruses. We also constructed a catalogue of 322,060 viral gene clusters including 105 virus-encoded putative auxiliary metabolic genes (AMGs) that participate in host metabolism and adaptation to the environment. Furthermore, our investigation has yielded a total of 7,197 putative virus-host pairs based on CRISPR and tRNA profiles. Viruses within oil reservoirs profoundly infected bacterial Actinobacteriota, Desulfobacterota, as well as archaeal Halobacteriota and Methanobacteriota. Combined microcosm enrichment experiments and bioinformatics analysis, we validated the ecological roles of viruses in regulating the community structure of sulfate reduction microorganisms, primarily through the predation of virulent viruses. Collectively, these findings have unveiled a rich diversity of novel viruses and their ecological functions within oil reservoirs. This study provides a comprehensive understanding of the role of viral communities in the biogeochemical cycle of the deep biosphere. It also facilitates microbial applications aimed at addressing challenges related to fossil-fuel production and carbon emissions in the petroleum industry.

microbiology↗

bioRxiv2023

The evolution of autonomy from two cooperative specialists in the fluctuating environments

From microbes to humans, organisms perform numerous tasks for their survival, including food acquisition, migration, and reproduction. A complex biological task can be performed by either an autonomous organism or by cooperation among several specialized organisms. However, it remains unclear how autonomy and cooperation evolutionarily switch. For example, it remains unclear whether and how a group of cooperative specialists can evolve into an autonomous organism. Here, we address this question by experimentally evolving a mutualistic microbial consortium composed of two specialists that cooperatively degrade naphthalene. We observed that autonomous genotypes capable of performing the entire naphthalene degradation pathway evolved from two cooperative specialists and came to dominate the microbial community. This evolutionary transition was driven by the horizontal gene transfer between the two specialists. However, this evolution was exclusively observed to occur in the fluctuating environment supplied with either naphthalene or pyruvate, where mutualism and competition between the two specialists alternated. The naphthalene-supplied environment exerts selective pressure that favors the expansion of autonomous genotypes but was associated with both low cell density and low coexistence levels. In contrast, the pyruvate-supplying environment promoted the coexistence and cell density of the cooperative specialists, thereby increasing the likelihood of horizontal gene transfer. Using a mathematical model, we quantitatively demonstrate that environmental fluctuations facilitate the evolution of autonomy through HGT when the relative growth rate and carrying capacity of the cooperative specialists allow enhanced coexistence and higher cell density in the competitive environment. Together, our results demonstrate that mutualistic cooperation can evolve into autonomous organisms through direct genetic exchange under specific conditions, including the alternation of mutualism and competition in fluctuating environments in a manner frequently occurring in nature.

evolutionary biology↗

bioRxiv2023

In-vivo whole-cortex estimation of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here we non-invasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically-plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the GABA-agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 years old) and Asian (7.2 to 7.9 years old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth. SignificanceHealthy brain function requires a delicate balance of neural excitation (E) and inhibition (I). In animals, this balance - the E/I ratio - is known to decrease with the maturation of inhibitory circuitry during healthy development. However, in humans, the normative development of cortex-wide E/I ratio remains unclear. Here, we use a biophysical model and non-invasive brain scans to estimate a marker of E/I ratio. Spatial changes in our E/I ratio marker are consistent with a drug that decreases E/I ratio. We also find that our cortex-wide E/I ratio marker decreases during development. Furthermore, North American and Asian children with lower E/I ratio, especially in higher-order cortex, have better cognitive performance. Overall, E/I ratio is a potential index of healthy neurocognitive development.

neuroscience↗

bioRxiv2023

Flexible Analysis of Spatial Transcriptomics Data (FAST): A Deconvolution Approach

MotivationSpatial transcriptomics is a state-of-art technique that allows researchers to study gene expression patterns in tissues over the spatial domain. As a result of technical limitations, the majority of spatial transcriptomics techniques provide bulk data for each sequencing spot. Consequently, in order to obtain high-resolution spatial transcriptomics data, performing deconvolution becomes essential. Deconvolution enables the determination of the proportions of different cell types along with the corresponding gene expression levels for each cell type within each spot. Most existing deconvolution methods rely on reference data (e.g., single-cell data), which may not be available in real applications. Current reference-free methods encounter limitations due to their dependence on distribution assumptions, reliance on marker genes, or the absence of leveraging histology and spatial information. Consequently, there is a critical demand for the development of highly adaptable, robust, and user-friendly reference-free deconvolution methods capable of unifying or leveraging case-specific information in the analysis of spatial transcriptomics data. ResultsWe propose a novel reference-free method based on regularized non-negative matrix factorization (NMF), named Flexible Analysis of Spatial Transcriptomics (FAST), that can effectively incorporate gene expression data, spatial coordinates, and histology information into a unified deconvolution framework. Compared to existing methods, FAST imposes fewer distribution assumptions, utilizes the spatial structure information of tissues, and encourages interpretable factorization results. These features enable greater flexibility and accuracy, making FAST an effective tool for deciphering the complex cell-type composition of tissues and advancing our understanding of various biological processes and diseases. Extensive simulation studies have shown that FAST outperforms other existing reference-free methods. In real data applications, FAST is able to uncover the underlying tissue structures and identify the corresponding marker genes.

bioinformatics↗

bioRxiv2023

Sexual dimorphism in melanocyte stem cell behavior reveals combinational therapeutic strategies for cutaneous repigmentation

Vitiligo is an autoimmune skin disease caused by cutaneous melanocyte loss. Although phototherapy and T cell suppression therapy have been widely used to induce epidermal re-pigmentation, full pigmentation recovery is rarely achieved due to our poor understanding of the cellular and molecular mechanisms governing this process. Here, we identify unique melanocyte stem cell (McSC) epidermal migration rates between male and female mice, which is due to sexually dimorphic cutaneous inflammatory responses generated by ultra-violet B exposure. Using genetically engineered mouse models, and unbiased bulk and single-cell mRNA sequencing approaches, we determine that manipulating the inflammatory response through cyclooxygenase and its downstream prostaglandin product regulates McSC proliferation and epidermal migration in response to UVB exposure. Furthermore, we demonstrate that a combinational therapy that manipulates both macrophages and T cells (or innate and adaptive immunity) significantly promotes epidermal melanocyte re-population. With these findings, we propose a novel therapeutic strategy for repigmentation in patients with depigmentation conditions such as vitiligo.

cell biology↗

bioRxiv2023

SPADE: Spatial Deconvolution for Domain Specific Cell-type Estimation

The advent of spatial transcriptomics technology has allowed for the acquisition of gene expression profiles with multi-cellular resolution in a spatially resolved manner, presenting a new milestone in the field of genomics. However, the aggregate gene expression from heterogeneous cell types obtained by these technologies poses a significant challenge for a comprehensive delineation of cell type-specific spatial patterns. Here, we propose SPADE (SPAtial DEconvolution), an in-silico method designed to address this challenge by incorporating spatial patterns during cell type decomposition. SPADE utilizes a combination of single-cell RNA sequencing data, spatial location information, and histological information to computationally estimate the proportion of cell types present at each spatial location. In our study, we showcased the effectiveness of SPADE by conducting analyses on synthetic data. Our results indicated that SPADE was able to successfully identify cell type-specific spatial patterns that were not previously identified by existing deconvolution methods. Furthermore, we applied SPADE to a real-world dataset analyzing the developmental chicken heart, where we observed that SPADE was able to accurately capture the intricate processes of cellular differentiation and morphogenesis within the heart. Specifically, we were able to reliably estimate changes in cell type compositions over time, which is a critical aspect of understanding the underlying mechanisms of complex biological systems. These findings underscore the potential of SPADE as a valuable tool for analyzing complex biological systems and shedding light on their underlying mechanisms. Taken together, our results suggest that SPADE represents a significant advancement in the field of spatial transcriptomics, providing a powerful tool for characterizing complex spatial gene expression patterns in heterogeneous tissues.

bioinformatics↗

bioRxiv2023

TTG2 defines trichome cell shape by modulating microtubules and actin cytoskeleton in Arabidopsis

The Arabidopsis TRANSPARENT TESTA GLABRA2 (TTG2) gene encodes a WRKY transcription factor that regulates a range of development events like trichome, seed coat, and atrichoblasts formation. Loss-of-function of TTG2 was previously shown to reduce or eliminate trichome branching. Here, we report the identification of a new allele of TTG2, ttg2-6. In contrast to the ttg2 mutants described before, ttg2-6 displays unique trichome phenotypes. Some ttg2-6 mutant trichomes are hyperbranched, whereas others are hypobranched, distorted, or clustered. Further, we found that in addition to specifically activating R3 MYB transcription factor TRIPTYCHON (TRY) to promote trichome branching, TTG2 also integrates cytoskeletal signaling to regulate trichome morphogenesis. The ttg2-6 trichomes display aberrant cortical microtubules (cMTs) and cortical actin filaments (F-actin) configurations. Moreover, genetic and biochemical analysis showed that TTG2 could directly bind and regulate the expression of BRICK1 (BRK1), a subunit of the actin nucleation promoting complex SCAR/WAVE. Collectively, taking advantage of ttg2-6, we uncovered new functions for TTG2 in facilitating cMT and F-actin cytoskeleton-dependent trichome development. Our evidence for a direct relationship between TTG2 and cytoskeletal regulators establishes an unprecedented understanding of cellular signaling events downstream of the core transcriptional regulation during trichome development in Arabidopsis.

developmental biology↗