Biology subjects

Arya, S.

Publications and source records attributed to Arya, S..

14 recordsLinked to original sources

bioRxiv2026

Portable, multilocus DNA barcoding across the diversity of meiofauna

Molecular data have revolutionised taxonomic and ecological research on the hyperdiverse communities of aquatic benthic microinvertebrates known as meiofauna. However, reference sequence databases remain highly incomplete, with variable barcode genes or fragments studied from taxon to taxon. Furthermore, there is a typical tradeoff between universality of primers and phylogenetic resolution, with rRNA markers being robustly recoverable but failing to resolve species-level divergences, and mitochondrial markers showing the reverse trend. Here, we introduce Oxford Nanopore rRNA and COI amplicon sequencing (OrCa-seq), a rapid, low-cost protocol for parallel long-range PCR amplification and multiplexed sequencing of four amplicons, spanning the nearly-complete rRNA cistron ([~]7-8 kb) and the widely studied Folmer region of COI (represented as overlapping 313 and 658 bp amplicons). This protocol, with its associated bioinformatic workflow, was designed for conducting biodiversity inventories of meiofauna and can be easily carried out in field research and educational contexts, with data available from 96-well plates of specimens within a day of lysis. To validate the method, we processed six plates of student-isolated freshwater and limno-terrestrial meiofauna, characterising the recovery of target genes and taxa with both automated and human-curated BLAST database comparisons. These data demonstrate the universal applicability of OrCa-seq across effectively all meiofauna, including the very smallest species. Nonetheless, recovery efficiency for each amplicon shows variation by taxon, with the full-length Folmer COI amplicon standing out as the most challenging. We present exemplar phylogenetic trees integrating reference sequences, demonstrating the utility of these data in confirming morphological determinations and in identifying anonymous specimens in a reverse taxonomy context. While developed in a specific educational context for use on meiofauna, the OrCa-seq approach should be readily scalable to larger research datasets, adaptable to many specimen types, and to any combination of taxon-or target-specific primers. As such, it represents a compelling multi-locus extension to the ever-growing repertoire of nanopore DNA barcoding protocols.

zoology↗

bioRxiv2026

CoLa-VAE: Cell-Cell Communication-aware Variational Autoencoder with Dynamic Graph Laplacian Constraints

Single-cell RNA sequencing provides a powerful view of cellular heterogeneity, but its sparsity and dropout noise remain major obstacles for recovering biologically meaningful gene expression programs and for downstream analyses that depend on reliable expression measurements. Ligand-receptor-based cell-cell communication inference is such analysis, missing ligand or receptor expression can cause substantial false negatives in sparse single-cell data. Here, we present CoLa-VAE, a cell-cell communication-aware variational autoencoder that jointly learns latent representations and denoised expression profiles by incorporating ligand-receptor-derived communication topology through dynamic graph Laplacian regularization. Rather than treating denoising as a secondary output of representation learning, CoLa-VAE uses denoised expression to iteratively refine communication estimates and uses the resulting communication structure to guide both latent organization and expression reconstruction. In addition to improving latent space organization and producing robust denoised expression matrices, CoLa-VAE-denoised matrices also improved downstream biological analyses, including the detection of robust differential cell-cell communication programs, mitigation of batch-associated variation and enhanced spatial transcriptomic deconvolution when spatially constrained communication structure was incorporated. Together, these results establish CoLa-VAE as a communication-guided denoising and representation learning framework that recovers biologically meaningful expression signals from sparse single-cell and spatial transcriptomic data, enabling more sensitive and reliable downstream analysis.

bioinformatics↗

bioRxiv2026

L-pentahomoserine correlates with therapy outcome in esophageal cancer and promotes metabolic adaptations that support cell survival under nutrient-deprived conditions

Esophageal adenocarcinoma (EAC) is the sixth-leading cause of cancer-related death. Although pyrimidine analogue-based neoadjuvant and adjuvant therapies are widely used, patient responses remain variable. Emerging evidence indicates that bacteria-derived metabolites influence tumor biology and therapy outcomes. To identify non-canonical plasma metabolites linked to cancer biology, we performed correlation analyses between untargeted metabolomics profiles and overall survival. This approach revealed a bacterial metabolite called L-pentahomoserine, or L-2-amino-5-hydroxypentanoic acid (L-2A5HPA), to be positively associated with overall survival. Notably, L-2A5HPA promoted cell survival under nutrient limitation by redirecting glucose metabolism towards aspartate and pyrimidine biosynthesis. In vitro, L-2A5HPA uptake varied among cell lines and was controlled by stereospecific transporters. Furthermore, metabolic profiling in mouse models of liver cancer showed different levels of L-2A5HPA and a strong correlation with pyrimidine intermediates, dihydroorotate and orotate. The link between L-2A5HPA, pyrimidine nucleotide metabolism, and cell survival provides mechanistic insight into its association with patient outcome. Our findings position L-2A5HPA as a metabolite with potential to become a prognostic biomarker for EAC and underscores its role in metabolic adaptation under nutrient-deprived conditions.

cancer biology↗

bioRxiv2024

Structural Insights into the Assembly and Regulation of 2'-O RNA Methylation by SARS-CoV-2 nsp16/nsp10

2-O-ribose methylation of the first transcribed base (adenine or A1 in SARS-CoV-2) of viral RNA mimics the host RNAs and subverts the innate immune response. How nsp16, with its obligate partner nsp10, assembles on the 5-end of SARS-CoV-2 mRNA to methylate the A1 has not been fully understood. We present a [~] 2.4 [A] crystal structure of the heterotetrameric complex formed by the cooperative assembly of two nsp16/nsp10 heterodimers with one 10-mer Cap-1 RNA (product) bound to each. An aromatic zipper-like motif in nsp16 and the N-terminal regions of nsp10 and nsp16 orchestrate an oligomeric assembly for efficient methylation. The front catalytic pocket of nsp16 stabilizes the upstream portion of the RNA while the downstream RNA remains unresolved, likely due to its flexibility. An inverted nsp16 dimer extends the positively charged surface area for longer RNA to influence the catalysis. Additionally, a non-specific nucleotide-binding pocket on the backside of nsp16 plays a critical role in catalysis, further contributing to its enzymatic activity.

microbiology↗

bioRxiv2023

The brittle star genome illuminates the genetic basis of animal appendage regeneration

Species within nearly all extant animal lineages are capable of regenerating body parts. However, it remains unclear whether the gene expression programme controlling regeneration is evolutionarily conserved. Brittle stars are a species-rich class of echinoderms with outstanding regenerative abilities, but investigations into the genetic bases of regeneration in this group have been hindered by the limited genomic resources. Here, we report a chromosome-scale genome assembly for the brittle star Amphiura filiformis. We show that the brittle star genome is the most rearranged amongst echinoderms sequenced to date, featuring a reorganised Hox cluster reminiscent of the rearrangements observed in sea urchins. In addition, we performed an extensive profiling of gene expression during brittle star adult arm regeneration and identified sequential waves of gene expression governing wound healing, proliferation and differentiation. We conducted comparative transcriptomic analyses with other invertebrate and vertebrate models for appendage regeneration and uncovered hundreds of genes with conserved expression dynamics, particularly during the proliferative phase of regeneration. Our findings emphasise the crucial importance of echinoderms to detect long-range expression conservation between vertebrates and classical invertebrate regeneration model systems.

evolutionary biology↗

bioRxiv2023

IRF8-mutant B cell lymphoma evades immunity through a CD74-dependent deregulation of antigen processing and presentation in MHC CII complexes

In diffuse large B-cell lymphoma (DLBCL), the transcription factor IRF8 is the target of a series of potentially oncogenic events, including, chromosomal translocation, focal amplification, and super-enhancer perturbations. IRF8 is also frequently mutant in DLBCL, but how these variants contribute to lymphomagenesis is unknown. We modeled IRF8 mutations in DLBCL and found that they did not meaningfully impact cell fitness. Instead, IRF8 mutants, mapping either to the DNA-binding domain (DBD) or c-terminal tail, displayed diminished transcription activity towards CIITA, a direct IRF8 target. In primary DLBCL, IRF8 mutations were mutually exclusive with mutations in genes involved in antigen presentation. Concordantly, expression of IRF8 mutants in murine B cell lymphomas uniformly suppressed CD4, but not CD8, activation elicited by antigen presentation. Unexpectedly, IRF8 mutation did not modify MHC CII expression on the cell surface, rather it downmodulated CD74 and HLA- DM, intracellular regulators of antigen peptide processing/loading in the MHC CII complex. These changes were functionally relevant as, in comparison to IRF8 WT, mice harboring IRF8 mutant lymphomas displayed a significantly higher tumor burden, in association with a substantial remodeling of the tumor microenvironment (TME), typified by depletion of CD4, CD8, Th1 and NK cells, and increase in T-regs and Tfh cells. Importantly, the clinical and immune phenotypes of IRF8-mutant lymphomas were rescued in vivo by ectopic expression of CD74. Deconvolution of bulk RNAseq data from primary human DLBCL recapitulated part of the immune remodeling detected in mice and pointed to depletion of dendritic cells as another feature of IRF8 mutant TME. We concluded that IRF8 mutations contribute to DLBCL biology by facilitating immune escape.

cancer biology↗

bioRxiv2023

Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0

Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice. Author summaryMicrobes are fascinating in their own right and play a tremendously important role in ecosystems. They often form complex, self-organized communities with spatial heterogeneity that is changing over time. Such complexity is challenging to understand and manage without the help of mathematical models. Individual-based models are one type of mathematical model that is particularly suited if differences between individual microbes, local interactions and adaptive behavior are important. We have developed a completely overhauled version of iDynoMiCS, a software that allows users to develop, run and analyze a wide range of individual-based models without having to program the software themselves. There are several capability enhancements and numerous small improvements, for example the ability to model different shapes of cells combined with physically realistic mechanical interactions between neighboring cells. We showcase this by simulating the competition between filaments, long chains of cells, with single cells and find that filaments outcompete single cells as they can spread quickly to new territory with higher levels of resources. Users now have a wider choice of platforms so we provide guidance on which platform might be most suitable for a given purpose.

ecology↗

bioRxiv2023

Sparsity of higher-order interactions enables learning and prediction for microbiomes

Microbiome engineering offers the potential to lever-age microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloguing community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanism are included and which are omitted. Here, we resolve both issues by introducing a new, mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the discovery of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [~] 1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets, and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

ecology↗

bioRxiv2022

RootSlice: a novel functional-structural model for root anatomical phenotypes

Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes. Summary statementRoot anatomy remains an underutilized target for crop breeding. RootSlice, a multicellular functional-structural model of root anatomy, simulates the costs and benefits of diverse root anatomical phenotypes to estimate their utility for plant fitness in unfavorable soil environments.

plant biology↗

bioRxiv2022

Identification of plasma proteins associated with oesophageal cancer chemotherapeutic treatment outcomes using SWATH-MS

Oesophageal adenocarcinoma (OAC) is an aggressive cancer with a five-year survival of <15%. Current chemotherapeutic strategies only benefit a minority (20-30%) of patients and there are no methods available to differentiate between responders and non-responders. We performed quantitative proteomics using Sequential Window Acquisition of all THeoretical fragment-ion spectra-Mass Spectrometry (SWATH-MS) on albumin/IgG-depleted and non-depleted plasma samples from 23 patients with locally advanced OAC prior to treatment. Individuals were grouped based on tumour regression (TRG) score (TRG1/2/3 vs TRG4/5) after chemotherapy, and differentially abundant proteins were compared. Protein depletion of highly abundant proteins led to the identification of around twice as many proteins. SWATH-MS revealed significant quantitative differences in the abundance of several proteins between the two groups. These included complement c1q subunit proteins, C1QA, C1QB and C1QC, which were of higher abundance in the low TRG group. Of those that were found to be of higher abundance in the high TRG group, GSTP1 was found to exhibit the lowest p-value and highest classification accuracy and Cohens kappa value. Concentrations of these proteins were further examined using ELISA-based assays. This study provides quantitative information relating to differences in the plasma proteome that underpin response to chemotherapeutic treatment in oesophageal cancers.

cancer biology↗

bioRxiv2022

RNA binding to human METTL3-METTL14 restricts N6-deoxyadenosine methylation of DNA in vitro

Methyltransferase like-3 (METTL3) and METTL14 complex transfers a methyl group from S-adenosyl-L-methionine to N6 amino group of adenosine bases in RNA (m6A) and DNA (m6dA). Emerging evidence highlights a role of METTL3-METTL14 in the chromatin context, especially in processes where DNA and RNA are held in close proximity. However, a mechanistic framework about specificity for substrate RNA/DNA and their interrelationship remain unclear. By systematically studying methylation activity and binding affinity to a number of DNA and RNA oligos with different propensities to form inter- or intra-molecular duplexes or single-stranded molecules in vitro, we uncover an inverse relationship for substrate binding and methylation and show that METTL3-METTL14 preferentially catalyzes the formation of m6dA in single-stranded DNA (ssDNA), despite weaker binding affinity to DNA. In contrast, it binds structured RNAs with high affinity, but methylates the target adenosine in RNA (m6A) much less efficiently than it does in ssDNA. We also show that METTL3-METTL14-mediated methylation of DNA is largely regulated by structured RNA elements prevalent in long noncoding and other cellular RNAs.

biochemistry↗

bioRxiv2021

A metal ion orients mRNA to ensure accurate 2'-O ribosyl methylation of the first nucleotide of the SARS-CoV-2 genome

The SARS-CoV-2 nsp16/nsp10 enzyme complex modifies the 2-OH of the first transcribed nucleotide of the viral mRNA by covalently attaching a methyl group to it. The 2-O methylation of the first nucleotide converts the status of mRNA cap from Cap-0 to Cap-1, and thus, helps the virus evade immune surveillance in the host cell. Here, we report two structures of nsp16/nsp10 representing pre- and post-release states of the RNA product (Cap-1). We observe overall widening of the enzyme upon product formation, and an inward twisting motion in the substrate binding region upon product release. These conformational changes reset the enzyme for the next round of catalysis. The structures also identify a unique binding mode and the importance of a divalent metal ion for 2-O methylation. We also describe underlying structural basis for the perturbed enzymatic activity of a clinical variant of SARS-CoV-2, and a previous SARS-CoV outbreak strain.

biochemistry↗

bioRxiv2020

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

Many antibiotic resistance genes co-occur with resistance genes for transition metals, such as copper, zinc, or mercury. In some environments, a positive correlation between high metal concentration and high abundance of antibiotic resistance genes has been observed, suggesting co-selection due to metal presence. Of particular concern is the use of copper and zinc in animal husbandry, leading to potential co-selection for antibiotic resistance in animal gut microbiomes, slurry, manure, or amended soils. For antibiotics, predicted no effect concentrations have been derived from laboratory measured minimum inhibitory concentrations and some minimal selective concentrations have been investigated in environmental settings. However, minimal co-selection concentrations for metals are difficult to identify. Here, we use mathematical modelling to provide a general mechanistic framework to predict minimal co-selective concentrations for metals, given knowledge of their toxicity at different concentrations. We apply the method to copper (Cu), Zinc (Zn), mercury (Hg), lead (Pb) and silver (Ag), predicting their minimum co-selective concentrations in mg/L (Cu: 5.5, Zn: 1.6, Hg: 0.0156, Pb: 21.5, Ag: 0.152). To exemplify use of these thresholds, we consider metal concentrations from slurry and slurry-amended soil from a UK dairy farm that uses copper and zinc as additives for feed and antimicrobial footbath: the slurry is predicted to be co-selective, but not the slurry-amended soil. This modelling framework could be used as the basis for defining standards to mitigate risks of antimicrobial resistance applicable to a wide range of environments, including manure, slurry and other waste streams.

microbiology↗

bioRxiv2020

Structural Basis of RNA Cap Modification by SARS-CoV-2 Coronavirus

The novel severe acute respiratory syndrome coronoavirus-2 (SARS-CoV-2), the causative agent of COVID-19 illness, has caused over 2 million infections worldwide in four months. In SARS coronaviruses, the non-structural protein 16 (nsp16) methylates the 5-end of virally encoded mRNAs to mimic cellular mRNAs, thus protecting the virus from host innate immune restriction. We report here the high-resolution structure of a ternary complex of full-length nsp16 and nsp10 of SARS-CoV-2 in the presence of cognate RNA substrate and a methyl donor, S-adenosyl methionine. The nsp16/nsp10 heterodimer was captured in the act of 2-O methylation of the ribose sugar of the first nucleotide of SARS-CoV-2 mRNA. We reveal large conformational changes associated with substrate binding as the enzyme transitions from a binary to a ternary state. This structure provides new mechanistic insights into the 2-O methylation of the viral mRNA cap. We also discovered a distantly located ligand-binding site unique to SARS-CoV-2 that may serve as an alternative target site for antiviral development.

biophysics↗