Biology subjects

Singh, O.

Publications and source records attributed to Singh, O..

9 recordsLinked to original sources

bioRxiv2026

Qombucha: Reconstructing unobserved progenitor methylation profiles reveals distinct developmental programs in glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer characterized by substantial intratumoral heterogeneity. Previous research demonstrates that GBM may have complex cell origins. To elucidate the interplay between brain development and GBM progression, we developed Qombucha (Quadratic prOgraMming Based tUmor deConvolution with cell HierArchy), a computational framework that uses DNA methylation data to infer tumor cell-type composition and profiles of unobserved progenitor cells. Unprecedentedly, Qombucha incorporates a developmental cell hierarchy that models mature brain cell types and their progenitors. Applied to a large TCGA GBM dataset spanning the RTK I, RTK II, and MES TYP subtypes, Qombucha identifies a distinct cell type composition profile for each subtype and recapitulates known biological patterns, including elevated microglia infiltration in MES TYP tumors. It also identifies subtype-specific developmental programs and shows that higher progenitor-cell abundance is associated with poorer survival. Qombucha-imputed cell fractions map methylation profiles of tumor samples to a compact, 11-dimensional latent space; in an independent NCI GBM cohort, this compact representation improves subtype clustering and enables accurate subtype classification, achieving performance comparable to state-of-the-art models based on full methylation profiles with much higher dimensionality. These results suggest that tumor cellular composition captures the core biological axes along which GBM subtypes diverge.

bioinformatics↗

bioRxiv2026

qPCR Guru, a free browser-based platform, strengthens microRNA analyses using full-curve Cq estimation

Quantitative PCR (qPCR) depends on reliable quantification cycle (Cq) estimation from amplification curves, which are not always well-behaved. We developed qPCR Guru to provide a complete analysis pipeline including data quality assessment, relative quantification, standard-curve diagnostics, and dual-method Cq evaluation. The latter compares the conventional instrument-derived threshold ("Reported") Cq versus the full-curve five-parameter logistic (5PL) second-derivative-maximum ("Fit") Cq and automatically flags curve-shape abnormalities and disagreement between the two estimates. On high-expressing targets (mRNA and microRNA), the two methods showed strong convergence, confirming general-purpose performance. On low-expressing targets, such as serum microRNA, baseline artifacts and biphasic amplification result in threshold miscalls that standard instrument analysis does not flag. Fit Cq restored replicate-concordant values where Reported Cq split the technical replicates by 17-20 cycles, recovered MIQE-compliant amplification efficiencies lost to biphasic miscalls (from 74% to 102% and 387% to 98%), and lowered within-group variability by 48% and 68% in feline and bovine samples, respectively. Together, these results demonstrate that full-curve estimation, with integrated curve-level diagnostics, strengthens qPCR analyses against threshold miscalls. ARTICLE HIGHLIGHTSO_LIqPCR Guru is a free, browser-based platform that provides a complete analysis pipeline and facilitates side-by-side comparisons of an instruments threshold (Reported) Cq and a full-curve (Fit) Cq, from the five-parameter logistic fitting with second-derivative-maximum (SDM/cpD2). C_LIO_LIFor every well the application automatically flags curve-shape abnormalities and disagreement between the two Cq estimates. C_LIO_LIOn clean, high-expressing mRNA and microRNA targets, the two estimators (Reported Cq and Fit Cq) were strongly concordant and produced equivalent relative quantification with comparable precision. C_LIO_LIIn low-expressing serum microRNA, baseline artifacts and biphasic amplification produced threshold Cq miscalls of up to [~]20 cycles and were detected by curve-shape flags and/or method disagreement. C_LIO_LIThe full-curve Cq estimate recovered replicate-concordant values, restored MIQE-compliant amplification efficiencies, and reduced within-group variability in serum microRNA. C_LI

Molecular Biology↗

bioRxiv2026

Integrating glycosylation in de novo protein design with ReGlyco Binder Design Filter

Artificial Intelligence (AI)-based methods for 3D protein structure prediction are revolutionising structural biology, rapidly giving users a 3D perspective on any molecular architecture and protein-protein interaction (PPI) virtually on-demand. Regardless of the inherent limitations of the various methods to date, the continuous improvement of the algorithms, and the broad availability of open access (OA) web servers, software packages and databases are bound to accelerate biopharmaceuticals discovery. Within this context, the development of computational pipelines for the de novo design of target-specific protein binders is especially exciting. As it stands, these processes are still rather inefficient and expensive, rapidly outputting thousands of designs relatively quickly, which translate into meagre yields. Here we show how the explicit integration of glycosylation as a filter in the 3D de novo design pipeline of biologics can significantly improve efficiency and reduce laboratory costs with minimal additional computational resources. As a proof-of-concept, we used the GlycoShape database and ReGlyco tools (https://glycoshape.org) to filter the results of an open competition launched by Adaptyv Bio in October 2025 for the design of binders as inhibitors against the heavily glycosylated Nipah virus glycoprotein (NiV-G) (https://proteinbase.com/competitions/adaptyv-nipah-competition). Screening of the 1,201 selected designs in block with ReGlyco allows users to eliminate 20% of the confirmed non-binders from the pool in approximately 3 hours on a dual-core CPU. Refinement with a rotamer search enabled flags 11% of non-binders prior to experiment and catches all but 5 confirmed binders from the given predicted 3D complexes. The availability of alternative binding poses improves this score. We complement this analysis with a demo OA colab notebook (https://colab.research.google.com/github/Ojas-Singh/GlycoShape-Resources/blob/main/colab/ReGlyco_Binder_Design_filter.ipynb) to illustrate our workflow. In this demo users can design mini binders against human erythropoietin (hEPO) by integrating GlycoShape resources with the RFdiffusion3 (RFD3) pipeline from the Institute for Protein Design (IDP).

bioinformatics↗

bioRxiv2025

Improvement in Orchard Nutrient Status and Productivity Through Integrated Nutrient Management in Mango (Mangifera indica L.) Under Medium-Density Planting

AbstractBalanced nutrient management is essential for sustaining productivity and soil health in perennial fruit crops. A two-year field study (2022-2024) was conducted to evaluate the effects of integrating soil and foliar micronutrient applications with reduced recommended doses of fertilizers (RDF) on vegetative growth, nutrient status, yield, fruit quality, and economic performance of mango (Mangifera indica L.) cv. Dashehari under medium-density planting. Nine nutrient modules combining 25-100% RDF with soil- and foliar-applied Fe, Zn, B, Cu, and Ca were compared using ANOVA, PCA, and PLS-SEM. Integrated treatments significantly enhanced canopy height, spread, and volume, with the 75% RDF + two foliar micronutrient sprays producing the greatest vegetative growth. Yield attributes, fruit size, retention, and shelf life improved markedly under micronutrient-enriched treatments. The highest fruit yield and benefit-cost ratio was obtained with 75% RDF + two foliar sprays, outperforming the conventional 100% RDF. Soil pH and EC were moderated, and organic carbon improved slightly under integrated nutrient management. Soil and leaf macro- and micronutrient concentrations were consistently higher with combined soil-foliar approaches. PCA clustered high-performing nutrient modules with yield and canopy variables, while PLS-SEM identified plant nutrient status as the strongest predictor of fruit yield. Overall, integrating micronutrient supplementation with 75% RDF improved nutrient-use efficiency, productivity, and economic returns while reducing dependence on full-dose fertilizers. The study highlights 75% RDF + foliar micronutrient sprays as an effective and sustainable nutrient management strategy for enhancing yield and orchard performance in mango. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/697138v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@227e25org.highwire.dtl.DTLVardef@87b769org.highwire.dtl.DTLVardef@1a73b2org.highwire.dtl.DTLVardef@a1cf12_HPS_FORMAT_FIGEXP M_FIG C_FIG

plant biology↗

bioRxiv2025

Regulation of N-glycosylation efficiency by eukaryotic oligosaccharyltransferase

Oligosaccharyltransferase (OST) catalyses the key step of N-glycosylation, transferring immature N-glycans to select Asn residues in nascent proteins in the endoplasmic reticulum (ER). Asn are more likely to be glycosylated in the context of a "glycosylation sequon", N-x-S/T (x=P), but not every sequon is glycosylated. Tight positive and negative regulation of site-specific N-glycosylation across the glycoproteome is essential because some Asn require glycosylation for productive protein folding or function, while others must remain unglycosylated. Despite its importance, the underlying bases for this regulation are illdefined. Here we characterise the molecular determinants regulating OST catalytic efficiency. We identify preferred substrates of OST, and show that the local sequence determines glycosylation efficiency by fine-tuning binding to an extended groove in OST along an eight residue stretch centered at the Asn acceptor. Tight control of OST activity is achieved through a switch, set ON when a peptide binds with high complementarity to the groove, triggering release of the assisting base Glu350 for catalysis. Based on analysis of buried sequons, we identify sequence characteristics associated with inherently non-preferred acceptor substrates for OST, and show these have a high propensity for local secondary structure incompatible with the optimal conformation of a peptide bound to OST. Finally, we validate these mechanisms by examining the determinants of efficient glycosylation at a variably glycosylated Asn in a set of human Fabs. These mechanistic and functional insights show how local protein sequence controls glycosylation occupancy with immediate implications in secretory protein function and evolution, pathogen immunity and epistasis, and in engineering protein sequences for the desired degree of site-specific glycosylation.

biochemistry↗

bioRxiv2025

Glycolipid recognition and binding by Siglec-6 hinges on interactions with the cell membrane

Sialic acid-binding immunoglobulin-type lectins (Siglecs) regulate immune response through interactions with sialylated glycans on glycoproteins and glycolipids. Human Siglecs count 14 unique proteins and in all of those the recognition and binding of the sialic acid on the glycan target involves a conserved, or canonical, Arg residue. For a subset of human Siglecs, namely MAG, Siglec-6, and Siglec-11, this Arg appears not to be essential, suggesting that a different binding mechanism may be at play. In this work, we used all-atom molecular dynamics (MD) simulations, binding assays, and mutagenesis to investigate the structural, mechanistic and energetic details of the binding of Siglec-6 to monosialylated gangliosides. Our results show that Siglec-6 relies only partially on its conserved Arg122 for recognition of membrane-bound gangliosides and that it supplements its binding free energy through interactions with the phospholipids in the membrane surrounding the target epitope. We confirmed by mutagenesis assays that the loss of the key residues (Lys 126 and Trp 127) for membrane interaction abrogates binding. These results provide a step-change in our understanding of the diversification of human Siglecs as molecular precision tools to bind specific sialosides by adapting their structure to the biological environment where these are found.

biochemistry↗

bioRxiv2025

Path2Omics: Enhanced transcriptomic and methylation prediction accuracy from tumor histopathology

Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. While molecular data provide comprehensive insights, obtaining such data remains costly and time-consuming. To address this challenge, we developed Path2Omics, a deep learning model that predicts gene expression and methylation from histopathology for 23 cancer types. Path2Omics was trained on 20,497 slides (9,456 formalin-fixed and paraffin-embedded (FFPE) and 11,041 fresh frozen (FF)) from 8,007 patients across 23 The Cancer Genome Atlas cohorts. When tested on FFPE slides, the most readily available format in clinical pathology practice, the integrated model outperformed its individual FF and FFPE components, robustly predicting nearly 5,000 genes on average, approximately five times more than our recently published DeepPT model. Externally evaluated on seven independent cohorts, Path2Omics robustly predicted the expression of approximately 4,400 genes, yielding a 30% increase over the FFPE model alone. Finally, we demonstrate that the inferred gene expression is nearly as effective as the actual values in predicting patient survival and treatment response. These results lay the basis for using Path2Omics to advance precision oncology from histopathology slides in a speedy and cost-effective manner. Statement of significancePath2Omics is a deep learning model that accurately predicts gene expression and methylation from histopathology slides across 23 cancer types. Unlike existing approaches that rely solely on FFPE slides for training, Path2Omics leverages both FFPE and FF slides by constructing two separate models and integrating them. Downstream analyses show that the inferred values from Path2Omics are nearly as effective as actual values in predicting patient survival and treatment response.

bioinformatics↗

bioRxiv2024

Bacterial ADP-heptose initiates a revival stem cell program in the intestinal epithelium

The intestinal epithelium has an exceptional capacity to repair following injury, and recent evidence has suggested that YAP-dependent signaling was crucial for the expansion of Clu+ revival stem cells (revSCs) with fetal-like characteristics, which are essential for epithelial regeneration. However, neither the mechanism underlying where these revSCs emerge from nor the nature of the physiological cues that induce this revSC program, are clearly identified. Here, we first demonstrate that Alpk1 and Tifa, which encode the proteins essential for the detection of the bacterial metabolite ADP-heptose (ADP-Hep), were expressed by the stem cell pool in the intestinal epithelium. Treatment of intestinal organoids with ADP-Hep not only induced acute NF-{kappa}B pro-inflammatory signaling but also TNF-dependent apoptosis within the crypt, causing blunted proliferation and acute disruption of the crypt architecture, while also triggering induction of a revSC program. To identify the molecular underpinnings of this process, we performed single-cell RNA-seq analysis of ADP-Hep-treated organoids as well as lineage-tracing experiments. Our data reveal that ADP-Hep induced the specific ablation of the homeostatic intestinal stem cell (ISC) pool. Removal of ADP-Hep resulted in the rapid recovery of ISCs through dedifferentiation of Paneth cells, which transiently acquired revSC features and expressed nuclear YAP. Moreover, lineage tracing from Lyz1+ Paneth cells showed that ADP-Hep triggered Paneth cell de-differentiation towards pluripotent and proliferative cells in organoids. In vivo, revSC emergence in response to irradiation-induced injury was severely blunted in Tifa-deficient mice, suggesting that efficient epithelial regeneration in this model required detection of microbiota-derived ADP-Hep by the ALPK1-TIFA pathway. Together, our work reveals that Paneth cells can serve as the cell of origin for revSC induction in the physiological context of microbial stimulation, and that the transient loss of Alpk1-expressing ISCs is the initiating event for this regenerative process.

cell biology↗

bioRxiv2023

Restoring Protein Glycosylation with GlycoShape

During the past few years, we have been witnessing a revolution in structural biology. Leveraging on technological and computational advances, scientists can now resolve biomolecular structures at the atomistic level of detail by cryogenic electron microscopy (cryo-EM) and predict 3D structures from sequence alone by machine learning (ML). One technique often supports the other to provide the view of atoms in molecules required to capture the function of molecular machines. An example of the extraordinary impact of these advances on scientific discovery and on public health is given by how structural information supported the rapid development of COVID-19 vaccines based on the SARS-CoV-2 spike (S) glycoprotein. Yet, none of these new technologies can capture the details of the dense coat of glycans covering S, which is responsible for its natural, biologically active structure and function and ultimately for viral evasion. Indeed, glycosylation, the most abundant post-translational modification of proteins, is largely invisible through experimental structural biology and in turn it cannot be reproduced by ML, because of the lack of data to learn from. Molecular simulations through high-performance computing (HPC) can fill this crucial information gap, yet the computational resources, the users skills and the long timescales involved limit applications of molecular modelling to single study cases. To broaden access to structural information on glycans, here we introduce GlycoShape (https://glycoshape.org) an open access (OA) glycan structure database and toolbox designed to restore glycoproteins to their native functional form by supplementing the structural information available on proteins in public repositories, such as the RCSB PDB (www.rcsb.org) and AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/), with the missing glycans derived from over 1 ms of cumulative sampling from molecular dynamics (MD) simulations. The GlycoShape Glycan Database (GDB) currently counts over 435 unique glycans principally covering the human glycome and with additional structures, fragments, and epitopes from other eukaryotic and prokaryotic organisms. The GDB feeds into Re-Glyco, a bespoke algorithm in GlycoShape designed to rapidly restore the natural glycosylation to protein 3D structures and to predict N-glycosylation occupancy, where unknown. Ultimately, integration of GlycoShape with other OA protein structure databases can provide a step-change in scientific discovery, from the structural and functional characterization of the active form of biomolecules, all the way down to pharmacological applications and drug discovery.

biophysics↗