Biology subjects

Singh, K. B.

Publications and source records attributed to Singh, K. B..

6 recordsLinked to original sources

bioRxiv2025

The fungal pathogen Rhizoctonia solani AG-8 has two nuclear haplotypes that differ in abundance

The fungal pathogen Rhizoctonia solani infects a diverse range of host plants and remains an intractable and economically significant disease for many crops. R. solani is classified into reproductively incompatible anastomosis groups (AGs). In the vegetative stage, most plant-pathogenic R. solani isolates are multinuclear and heterokaryotic, but little was previously known about the diversity between haplotypes due to highly fragmented, collapsed short-read assemblies. We present fully-phased, chromosome-scale genome assemblies of the broad host-range R. solani isolates AG8-1 and AG8-3. We demonstrate that both AG8 isolates have two distinct haplotypes, each of which is [~]50 Mbp spread across 16 chromosomes and use PacBio Iso-Seq data to achieve a high-quality gene annotation. We show that the two nuclear haplotypes display high heterozygosity and differences in haplotype abundance in vegetative cultures. Using transcriptome sequencing during infection of three types of host plants, we show that the less abundant haplotype in both AG8-1 and AG8-3 harbour more genes up-regulated during infection. Taken together, these findings address some of the observed phylogenetic heterogeneity of AG-8 isolates and provide a platform to further dissect the mechanisms enabling this globally significant agricultural pathogen to inflict losses to a range of crop hosts.

microbiology↗

bioRxiv2025

Integrating Artificial Intelligence-Driven Digital Pathology and Genomics to Establish Patient-Derived Organoids as a Novel Alternative Model for Drug Response in Head and Neck Cancer

Patient-derived organoids (PDOs) are emerging as advanced 3D ex vivo novel alternative method (NAM) preclinical models, offering significant advantages over traditional cell lines and monolayer cultures for therapeutic development. In this study, we established PDOs from surgically resected fresh tissues of human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) across anatomical sites, tumor T-categories, and sample types. These PDOs faithfully recapitulate the tumors pathology, mutational profile, and drug response. To enable rapid classification of PDO identity, we developed a new convolutional neural network (CNN) model, TransferNet-PDO, which accurately distinguished tumor versus normal PDOs in culture using digital histopathology images (AUC[≥]0.88). PDOs maintained stable cultures and were cryopreserved between passages 5 and 12. Immunohistochemistry (IHC) staining (PanCK, p63, Cytokeratin 13, Ki67) confirmed squamous phenotype and histologic aggression of the original tumor. For tumors harboring TP53 mutations by whole-exome sequencing (WES), PDOs retained the corresponding p53 functional status as confirmed by IHC (enhanced or loss of expression). Somatic mutational landscape revealed that PDOs preserved driver somatic mutations, copy number variations (CNVs), and clonal architecture including low-prevalence subclones. Drug sensitivity assessment of PDOs showed that cisplatin reduced cell viability, whereas cetuximab and lenvatinib had minimal effects. Chemoradiation led to greater tumor organoid killing compared to radiation or chemotherapy alone. This study presents an integrated HNSCC PDO platform combining tissue biobanking, organoid establishment, multi-omics characterization, functional drug screening, and AI-driven histopathologic classification, providing a comprehensive and scalable system for translational cancer research.

cancer biology↗

bioRxiv2022

Chromatin-immunoprecipitation reveals the PnPf2 transcriptional network controlling effector-mediated virulence in a fungal pathogen of wheat

The regulation of virulence in plant-pathogenic fungi has emerged as a key area of importance underlying host infections. Recent work has highlighted the role of transcription factors (TFs) that mediate the expression of virulence-associated genes. A prominent example is Pf2, a member of the Zn2Cys6 family of fungal TFs, where orthologues regulate the expression of genes linked to parasitism in several plant-pathogen lineages. These include PnPf2 which controls effector-gene expression in Parastagonospora nodorum, thereby determining the outcome of effector-triggered susceptibility on its host, wheat. PnPf2 is a promising target for disease suppression but the genomic targets, or whether other are regulators involved, remain unknown. This study used chromatin immunoprecipitation (ChIP-seq) and a mutagenesis analysis to investigate these components. Two distinct binding motifs connected to positive gene-regulation were characterised and genes directly targeted by PnPf2 were identified. These included genes encoding major effectors and other components associated with the P. nodorum pathogenic lifestyle, such as carbohydrate-active enzymes and nutrient assimilators. This supports a direct involvement of PnPf2 in coordinating virulence on wheat. Other TFs were also prominent PnPf2 targets, suggesting it also operates within a transcriptional network. Several TFs were therefore functionally investigated in connection to fungal virulence. Distinct metabolic and developmental roles were evident for the newly characterised PnPro1, PnAda1, PnEbr1 and the carbon-catabolite repressor PnCreA. Overall, the results uphold PnPf2 as the central transcriptional regulator orchestrating genes that contribute to virulence on wheat and provide mechanistic insight into how this occurs. ImportanceFungal pathogens cause large crop losses worldwide and consequently much attention has focused on improving host genetic resistance to diseases. These pathogens use effectors, which require coordinated expression at specific stages of the pathogenic lifecycle, to manipulate the host plant metabolism in favour of infection. However, our understanding of the underlying regulatory network in coordination with other genes involved in fungal pathogenicity is lacking. The Pf2 TF orthologues are key players underpinning virulence and effector gene expression in several fungal phytopathogens, including P. nodorum. This study provided significant insight into the DNA-binding regulatory mechanisms of P. nodorum PnPf2, as well as further evidence that it is central to the coordination of virulence. In the context of crop protection, the Pf2 taxonomic orthologues present opportune targets in major fungal pathogens that can be perturbed to reduce the impact of effector triggered-susceptibility and improve disease resistance.

pathology↗

bioRxiv2020

Novel mechanistic targets of Forkhead box Q1 transcription factor in human breast cancer cells

The transcription factor forkhead box Q1 (FoxQ1), which is overexpressed in different solid tumors, has emerged as a key player in the pathogenesis of breast cancer by regulating epithelial-mesenchymal transition, maintenance of cancer-stem like cells, and metastasis. However, the mechanism underlying oncogenic function of FoxQ1 is still not fully understood. In this study, we compared the RNA-seq data from FoxQ1 overexpressing SUM159 cells with that of empty vector-transfected control (EV) cells to identify novel mechanistic targets of this transcription factor. Consistent with published results in basal-like subtype, immunohistochemistry revealed upregulation of FoxQ1 protein in luminal-type human breast cancer tissue microarrays when compared to normal mammary tissues. Many previously reported transcriptional targets of FoxQ1 (e.g., E-cadherin, N-cadherin, fibronectin 1, etc.) were verified from the RNA-Seq analysis. FoxQ1 overexpression resulted in downregulation of genes associated with cell cycle checkpoints, M phase, and cellular response to stress/external stimuli as evidenced from the Reactome pathway analysis. Consequently, FoxQ1 overexpression resulted in S, G2M and mitotic arrest in basal-like SUM159 and HMLE cells, but not in luminal-type MCF-7 cells. There were differences in expression of cell cycle-associated proteins between FoxQ1 overexpressing SUM159 and MCF-7 cells. Finally, we show for the first time that FoxQ1 is a direct transcriptional regulator of interleukin (IL)-1, IL-8, and vascular endothelial growth factor in breast cancer cells. Chromatin immunoprecipitation revealed FoxQ1 occupancy at the promoters of IL-1, IL-8, and VEGF. In conclusion, the present study reports novel mechanistic targets of FoxQ1 in human breast cancer cells.

cancer biology↗

bioRxiv2018

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

Plant-pathogenic fungi secrete effector proteins to facilitate infection. We describe extensive improvements to EffectorP, the first machine learning classifier for fungal effector prediction. EffectorP 2.0 is now trained on a larger set of effectors and utilizes a different approach based on an ensemble of classifiers trained on different subsets of negative data, offering different views on classification. EffectorP 2.0 achieves accuracy of 89%, compared to 82% for EffectorP 1.0 and 59.8% for a small size classifier. Important features for effector prediction appear to be protein size, protein net charge as well as the amino acids serine and cysteine. EffectorP 2.0 decreases the number of predicted effectors in secretomes of fungal plant symbionts and saprophytes by 40% when compared to EffectorP 1.0. However, EffectorP 1.0 retains value and combining EffectorP 1.0 and 2.0 results in a stringent classifier with low false positive rate of 9%. EffectorP 2.0 predicts significant enrichments of effectors in 12 out of 13 sets of infection-induced proteins from diverse fungal pathogens, whereas a small cysteine-rich classifier detects enrichment only in 7 out of 13. EffectorP 2.0 will fast-track prioritization of high-confidence effector candidates for functional validation and aid in improving our understanding of effector biology. EffectorP 2.0 is available at http://effectorp.csiro.au.

bioinformatics↗

bioRxiv2017

ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning

The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting if an effector or plant protein localizes to the apoplast. ApoplastP uncovers features for apoplastic localization common to both effectors and plant proteins, namely an enrichment in small amino acids and cysteines as well as depletion in glutamic acid. ApoplastP predicts apoplastic localization in effectors with sensitivity of 75% and false positive rate of 5%, improving accuracy of cysteine-rich classifiers by over 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted plant and effector proteins. The secretomes of fungal saprophytes, necrotrophic pathogens and extracellular pathogens are enriched for predicted apoplastic proteins. Rust pathogen secretomes have the lowest percentage of apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells. ApoplastP is available at http://apoplastp.csiro.au.

bioinformatics↗