Biology subjects

Sundaresan, V.

Publications and source records attributed to Sundaresan, V..

At least 19 recordsLinked to original sources

bioRxiv2025

In-situ diversification and regional attributes shape asymmetric diversity of Miliusa (Annonaceae) in tropical Asia.

AimWe examine biogeography and speciation patterns in Miliusa Lesch. ex A. DC. ([~]65 species) distributed in tropical Asia to understand the uneven distribution of its extant diversity. LocationTropical Asia TaxonMiliusa (Annonaceae) MethodsPhylogenetic reconstruction was performed using six plastid markers across 52 species using both ML and BI approaches. Divergence times were estimated using two fossil calibrations and an optimised relaxed clock, and ancestral areas were inferred with BioGeoBEARS. Speciation rates were examined using ClaDS and the DR statistic, and community structure was assessed using phylogenetic diversity metrics. ResultsMiliusa likely originated in the mid-Miocene, with Indo-Burma and peninsular India (PI) as its ancestral range. Its extant diversity is primarily attributed to in-situ speciation, with dispersal or vicariance playing limited but important roles in PI, and Wallacea and Sahul. Lineages in Indo-Burma began accumulating in the mid-Miocene, preceding those in PI ([~]10 Myr) and Wallacea and Sahul ([~]5 Myr). PI showed signs of lineage saturation and had lower speciation rates compared to Wallacea and Sahul and Indo-Burma, both of which had similar rates. All regions exhibited phylogenetic clustering, but Indo-Burma and PI differed in their sensitivity to phylogenetic depths. Main conclusionsThe uneven diversity of Miliusa is shaped by time for speciation, age, and dispersal, although their relative influence varies across regions. In Indo-Burma, long-term geo-climatic stability and greater niche availability likely facilitated the persistence of lineages, rapid speciation, and dispersal, making it an evolutionary hotspot for Miliusa. In contrast, PI exhibited lower richness and speciation rates despite being old, likely due to the contraction of wet habitats in the Miocene that limited available niches for speciation. Lineages in Wallacea and Sahul show typical island-like radiations, with speciation rates comparable to the larger and more geo-climatically stable Indo-Burma, despite their more recent origin. Overall, our results highlight the role of Miocene-driven climatic vicariance and Pliocene-Pleistocene climatic fluctuations in shaping the diversification dynamics and regional diversity patterns across tropical Asia.

evolutionary biology↗

bioRxiv2025

Diploid gametes in maize by mutation of A-Type cyclins: a step towards apomeiosis and synthetic apomixis.

Hybrid crops are agriculturally desirable due to heterosis but are costly or difficult to produce. Synthetic apomixis, clonal reproduction through seed, offers the ability to fix hybrid vigor. Two components are needed to achieve this goal: the formation of diploid gametes identical to the maternal parent through apomeiosis, and the induction of embryogenesis in the egg cell without fertilization, known as parthenogenesis. In maize, parthenogenesis was achieved by egg cell expression of the transcription factor ZmBABY BOOM 1 but a viable apomeiosis strategy has not been reported. In the MiMe (Mitosis instead of Meiosis) system, in addition to mutants that skip recombination and sister chromatid adhesion, mutation of genes involved in cell cycle control during meiosis is needed to skip the second division and ensure diploid gametes. In this report we describe the effect of mutation of maize A-type cyclin genes with similarity to Arabidopsis TARDY ASYNCHRONOUS MEIOSIS (TAM). In double mutant plants, we find that diploid gametes are formed with high efficiency and that the progeny are tetraploid. These genes provide a viable route towards creating synthetic apomixis in maize.

plant biology↗

bioRxiv2025

Damage signals preferentially activate killer CD8+/- regulatory T cells to protect injured tissue

Self-antigens that are obscure to immune cells under homeostasis, are exposed following tissue damage and can trigger autoimmunity. Our previous work showed that conventional type 1 dendritic cells (cDC1s) mediate immunoregulation after traumatic skeletal muscle injury. Here, we found that, immune responses to injury in cDC1-depleted mice (Batf3-/-) mirror those of autoimmune mice (Aire-/-). Mechanistically, we determined that cDC1s prime killer regulatory T cells (CD8+/-Ly49+Tregs) which express Ly49 inhibitory receptors and HELIOS. These killer-like Tregs are clonally diverse and carry T cell receptors associated with self-reactivity and response to hydrophobicity. cDC1 or CD8 deletion promoted CD62L+CCR7+ naive T cell retention and B cell recruitment to injured muscle. These naive T cells, which are implicated in autoimmunity, strongly correlate with B cell abundance in the muscle and are selectively pruned by CD8+/-Ly49+ Tregs. Furthermore, clinically used materials that promote wound healing enrich CD8+/-Ly49+ Treg function whereas those that are associated with pathology promote naive T cell and B cell accumulation. We hypothesize that CD8+/-Ly49+ Tregs maintain self-tolerance after tissue damage and avert autoimmunity by eliminating naive T cells and preventing pathogenic B cell activation.

immunology↗

bioRxiv2023

Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data

Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deep-learning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.

neuroscience↗

bioRxiv2022

High-frequency synthetic apomixis in hybrid rice

Introducing asexual reproduction through seeds - apomixis - into crop species could revolutionize agriculture by allowing F1 hybrids with enhanced yield and stability to be clonally propagated. Engineering synthetic apomixis has proven feasible in inbred rice through the inactivation of three genes (MiMe), which results in the conversion of meiosis into mitosis in a line ectopically expressing the BABYBOOM1 (BBM1) parthenogenetic trigger in egg cells. However, only 10-30% of the seeds were clonal. We show here that synthetic apomixis can be induced in a F1 hybrid of rice by inducing MiMe mutations and egg cell expression of BBM1 in a single step. We generated hybrid plants that produced more than 95% of clonal seeds across multiple generations. Clonal apomictic plants maintained the phenotype of the F1 hybrid along successive generations. Our results demonstrate that there is no barrier to almost fully penetrant synthetic apomixis in an important crop species, rendering it compatible with use in agriculture.

plant biology↗

bioRxiv2022

Isolation and Transfection of Rice Egg Cells and Zygotes for Cellular Localization

Due to the difficulty in accessing to gametes and zygotes in flowering plants, which are controlled in single cells deeply embedded in multiple tissues, little is known about how the initiation of plant embryogenesis may reflect or contrast from such systems in other eukaryotes. In this study we has developed an approach of isolation and transfection of rice egg cells and zygotes for cellular localization of rice cell cycle factors (KRP5, KRP4 and FB3), which opened a pathway to monitor protein expression in rice egg cells and zygotes at different developmental stages. The advantageous feature of isolated rice cells may serve as an ideal system for studying the molecular mechanism underlying the rice zygotic division to initiate seed formation.

cell biology↗

bioRxiv2022

A CDKB/KRP/FB3 cell cycle core complex functions in rice gametes and zygotes

The cell cycle controls division and proliferation of all eukaryotic cells and is tightly regulated at multiple checkpoints by complexes of core cell cycle proteins. Due to the difficulty in accessing female gametes and zygotes of flowering plants, little is known about the molecular mechanisms underlying initiation embryogenesis despite the crucial importance of this process for seed crops. In this study, we reveal four levels of factors involved in rice zygotic cell cycle control and characterize their functions and regulation. Protein-protein interaction studies, including within zygote cells, and in vitro biochemical analyses delineate a model of the zygotic cell cycle core complex for rice. In this model, CDKB1, a major regulator of plant mitosis, is a cyclin (CYCD5)-dependent kinase; its activity is coordinately inhibited by two cell cycle inhibitors, KRP4 and KRP5; and both KRPs are regulated via F-box protein 3 (FB3)-mediated proteolysis. Supporting their critical role in controlling the rice zygotic cell cycle, mutations in KRP4, KRP5, and FB3 result in the compromised function of sperm cells and abnormal organization of female germ units, embryo and endosperm, thus significantly reducing seed-set rates. This work helps reveals regulatory mechanisms controlling the zygotic cell cycle toward seed formation in angiosperms.

cell biology↗

bioRxiv2021

Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ

AO_SCPLOWBSTRACTC_SCPLOWFluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame are either computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson-Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to the conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high performance (> 8 dB) denoising in less time than other fluorescence microscopy denoising methods.

neuroscience↗

bioRxiv2021

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

Robust automated segmentation of white matter hyperintensities (WMHs) in different datasets (domains) is highly challenging due to differences in acquisition (scanner, sequence), population (WMH amount and location) and limited availability of manual segmentations to train supervised algorithms. In this work we explore various domain adaptation techniques such as transfer learning and domain adversarial learning methods, including domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network, which is our baseline model. We evaluated the domain adaptation techniques on source and target domains consisting of 5 different datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For transfer learning, we also studied various training options such as minimal number of unfrozen layers and subjects required for finetuning in the target domain. On comparing the performance of different techniques on the target dataset, unsupervised domain adversarial training of neural network gave the best performance, making the technique promising for robust WMH segmentation.

neuroscience↗

bioRxiv2020

Drought duration determines the recovery dynamics of rice root microbiomes

As extreme droughts become more frequent, dissecting the responses of root-associated microbiomes to drying-wetting events is essential to understand their influence on plant performance. Here, we show that rhizosphere and endosphere communities associated with drought-stressed rice plants display compartment-specific recovery trends. Rhizosphere microorganisms were mostly affected during the stress period, whereas endosphere microorganisms remained altered even after irrigation was resumed. The duration of drought stress determined the stability of these changes, with more prolonged droughts leading to decreased microbiome resilience. Drought stress was also linked to a permanent delay in the temporal development of root microbiomes, mainly driven by a disruption of late colonization dynamics. Furthermore, a root-growth-promoting Streptomyces became the most abundant community member in the endosphere during drought and early recovery. Collectively, these results reveal that severe drought results in enduring impacts on root-associated microbiomes that could potentially reshape the recovery response of rice plants.

microbiology↗

bioRxiv2020

Resetting of 24-nt siRNA landscape is initiated in the unicellular zygote in rice

BackgroundThe zygote, a totipotent stem cell, constitutes a critical stage of the life cycle of sexually reproducing organisms. It is produced by the fusion of two differentiated cells -- the egg and sperm, which in plants have radically different siRNA transcriptomes from each other and from multicellular embryos. Due to technical challenges, the epigenetic changes that accompany the zygotic transition are poorly understood. ResultsHere, we characterized the small RNA transcriptome of rice zygotes. We found widespread redistribution of 24-nt siRNAs relative to gametes, including absence of sperm signature siRNAs, reduction at egg signature siRNA loci, and upregulation at seedling signature siRNA loci. Loci with reduced siRNAs in zygote relative to egg were gene-distal and heterochromatic, while loci with increased siRNAs relative to egg had a similar genomic distribution to canonical siRNA loci. Although both egg and zygote siRNA loci had higher mCHH level in wildtype than in drm2 embryo, zygote but not egg siRNA loci were associated with hypermethylation in mature embryo. A small fraction of siRNA loci (~1%) called siren loci accounted for 60% of all siRNAs within zygote siRNA loci, that likely arose from maternal carryover as they had similarly abundant siRNAs in egg; these siren loci were not associated with embryo hypermethylation. ConclusionsTaken together, our results indicate re-distribution of siRNAs in rice zygotes towards the canonical vegetative profile, that are consistent with the initiation of resetting of the gametic epigenome before the first embryonic division.

plant biology↗

bioRxiv2020

Rice embryogenic trigger BABY BOOM1 promotes somatic embryogenesis by upregulation of auxin biosynthesis genes

Somatic embryogenesis, a powerful tool for clonal propagation and for plant transformation, involves cellular reprogramming of differentiated somatic cells to acquire pluripotency. Somatic embryogenesis can be induced by treating explants with plant growth regulators. However, several plant species including agronomically important cereal crops remain recalcitrant to dedifferentiation and transformation except from embryonic tissues. Somatic embryogenesis can also be induced by ectopic expression of select embryonic factors, including in cereals by BABY BOOM (BBM) transcription factors. How BBM genes bypass the need for exogenous hormones is not well understood. Here, we investigated downstream targets during induction of somatic embryogenesis in rice by OsBBM1 ((Oryza sativa BABY BOOM1). Transient induction of OsBBM1 led to the upregulation of auxin biosynthesis OsYUCCA genes. Continued induction of OsBBM1 resulted in somatic embryogenesis without the need for exogenous auxins. Genetic mutant analysis of OsBBM1 downstream targets, OsYUCCA6, OsYUCCA7 and OsYUCCA9, show that they are required for normal rice development including root and shoot development. Somatic embryogenic potential of OsYUCCA triple mutants was highly compromised despite the presence of exogenous auxin. Additionally, we show that somatic embryogenesis induction by exogenous auxin in rice requires functional BBM genes. Thus, OsBBM1 mediated cellular reprogramming and somatic embryogenesis likely involves increased localized auxin through direct upregulation of OsYUCCA genes. This study reveals mechanistic details of how somatic embryogenesis is established in differentiated tissues in rice, a monocot model and agronomically important cereal crop, with the potential utility to improve regeneration from tissue culture for recalcitrant plants in future. One-sentence summaryRice BABY BOOM1 induces somatic embryogenesis from differentiated tissues by promoting auxin biosynthesis through direct upregulation of YUCCA genes.

developmental biology↗

bioRxiv2020

Integrating large-scale neuroimaging research datasets: harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise sample differences contributing to study-level differences in WMH variations. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data. HIGHLIGHTSO_LIWe harmonised measures of WMHs across two studies on healthy ageing C_LIO_LISpecific pre-processing strategies can increase comparability across studies C_LIO_LIModelling of biological differences is crucial to provide calibrated measures C_LI

neuroscience↗

bioRxiv2020

Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

White matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. Also, the network uses anatomical information regarding WMH spatial distribution in loss functions for improving the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning method of MWSC 2017.

neuroscience↗

bioRxiv2020

ARF1 dimerization is essential for vesicle trafficking and dependent on activation by ARF-GEF dimers in Arabidopsis

Membrane traffic maintains the organization of the eukaryotic cell and delivers cargo proteins to their subcellular destinations such as sites of action or degradation. Membrane vesicle formation requires ARF GTPase activation by the SEC7 domain of ARF guanine-nucleotide exchange factors (ARF-GEFs), resulting in the recruitment of coat proteins by GTP-bound ARFs. In vitro exchange assays were done with monomeric proteins, although ARF-GEFs have been shown to form dimers in vivo. This feature is conserved across the eukaryotes, however its biological significance is unknown. Here we demonstrate ARF1 dimerization in vivo and we show that ARF-GEF dimers mediate ARF1 dimer formation. Mutational disruption of ARF1 dimers interfered with ARF1-dependent trafficking but not coat protein recruitment in Arabidopsis. Mutations disrupting simultaneous binding of two ARF1*GDPs by the two SEC7 domains of GNOM ARF-GEF dimer prevented stable interaction of ARF1 with ARF-GEF and thus, efficient ARF1 activation. Our results suggest a model of activation-dependent dimerization of membrane-inserted ARF1*GTP molecules required for coated membrane vesicle formation. Considering the evolutionary conservation of ARFs and ARF-GEFs, this initial regulatory step of membrane trafficking might well occur in eukaryotes in general.

plant biology↗

bioRxiv2019

Reprogramming of 24nt siRNAs in rice gametes

Gametes constitute a critical stage of the plant life cycle, during which the genome undergoes reprogramming in preparation for embryogenesis. Here we characterized the small RNA transcriptomes of egg cells and sperm cells from rice to elucidate genome-wide distributions of 24nt siRNAs, which are a hallmark of RNA-directed DNA methylation (RdDM) in plants and are typically concentrated at boundaries of heterochromatin. We found that 24nt siRNAs were depleted from heterochromatin boundaries in both gametes, reminiscent of siRNA patterns in DDM1-type nucleosome remodeler mutants. In sperm, 24nt siRNAs were spread across broad heterochromatic regions, while in eggs, 24nt siRNAs were concentrated at a smaller number of heterochromatic loci throughout the genome, which were shared with vegetative tissues and sperm. In both gametes, patterns of CHH methylation, typically a strong indicator of RdDM, were similar to vegetative tissues, although lower in magnitude. These findings indicate that the small RNA transcriptome undergoes large-scale re-programming in both male and female gametes, which is not correlated with recruitment of DNA methyltransferases in gametes and suggestive of unexplored regulatory activities of gamete small RNAs in seeds after fertilization.

molecular biology↗

bioRxiv2018

Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurode-generative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions.\n\nIn this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature, with respect to K-nearest neighbour algorithm currently used for lesion probability map estimation in BIANCA. Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort and a vascular cohort.\n\nWe observed that including population-level parametric lesion probabilities with re-spect to age and using alternative machine learning techniques provided negligible im-provement. However, LOCATE provided a substantial improvement in the lesion segmentation performance when compared to the global thresholding currently used in BIANCA. We further validated LOCATE on a cohort of CADASIL (Cerebral autoso-mal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease characterised by extensive WMH burden, and healthy controls showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.

neuroscience↗

bioRxiv2018

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model.\n\nIn this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework.\n\nWe tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease.\n\nOn simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27 x 10-5, which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.

neuroscience↗