Biology subjects

Sidhu, G. S.

Publications and source records attributed to Sidhu, G. S..

6 recordsLinked to original sources

bioRxiv2026

Fecal metabolomics reveals preferential complex carbohydrate utilization and guides cultivation of murine gut Firmicutes

The murine gut microbiota provides robust colonization resistance against Clostridioides difficile infection (CDI), yet murine-associated microbes remain notoriously difficult to cultivate in vitro, limiting mechanistic investigation. To identify the ecological and nutritional basis of this cultivation barrier, we leveraged CDI susceptibility as a functional readout of microbial community metabolism to infer in vivo nutrient utilization. Germ-free C57BL/6 mice colonized with varying dilutions of ethanol-treated murine microbiota were challenged with C. difficile resulting in a spectrum of CDI outcomes. Comparative metabolomics of pre-challenge fecal samples revealed a consistent carbohydrate signature: glucose accumulated in communities that resisted C. difficile challenge, whereas complex carbohydrates, including raffinose, sucrose, trehalose, lactose, sorbitol, and mannitol, were significantly depleted. The broad depletion of these complex carbohydrates supports their functional importance within the collective microbial community. Conventional glucose-based media (CMA, BHI+I, RCMT) failed to support robust growth or subculture of murine gut microbiota. Guided by the metabolomics findings, we developed Peptone Yeast Extract with Six Salts and Sugars (PYE6S), a glucose-free medium supplemented with the complex carbohydrates identified as depleted. PYE6S enabled cultivation of 22 unique Firmicutes ASVs, 82% of which lacked named cultured representatives in reference databases. These findings suggest a plausible explanation for why conventional media fail and support a metabolomics-guided framework for rational cultivation of host-associated microbiota across diverse systems. This strategy may be extended to guide media design for other host-associated microbiotas.

microbiology↗

bioRxiv2025

Network analysis of flowering time genes suggests regulatory changes among SOC1 orthologues in response to cold in Brassica napus

Flowering plants respond to multiple environmental and endogenous cues to determine the timing of their transition from the vegetative to floral state. Most of our knowledge of the gene regulatory network (GRN) controlling flowering time has been derived from the model plant Arabidopsis thaliana. This knowledge needs to be translated to crop plants to support the development of varieties that can be grown in different and rapidly changing climatic conditions. However, due to increased genome complexity and limited prior knowledge, translation into crops is not always straightforward. Here, we present a study of the GRN controlling flowering time in Brassica napus (oilseed rape), an allotetraploid crop that is a close relative of Arabidopsis. Using a comparative transcriptomics approach, we show that the majority of the orthologous gene pairs have similar expression dynamics over development between Arabidopsis and Brassica napus. Some genes, however, have experienced regulatory changes, with flowering time genes in Brassica napus having higher than average differences in their expression profiles from their Arabidopsis orthologues. Despite these differences, the inferred GRN for flowering in Brassica napus exhibits a similar network topology to the network known in Arabidopsis. This is likely due to preferential retention of these genes in higher paralogue numbers, which allows subtle changes in the regulation of individual paralogues, while still conserving the overall regulatory structure through evolution. We discover and present a detailed analysis of one such example where orthologues of SUPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) have similar expression patterns under normal conditions, but different dynamics under cold temperature conditions, suggesting possible subfunctionalisation among paralogues in response to temperature change.

plant biology↗

bioRxiv2025

What is a differentially expressed gene?

The concept of Differentially Expressed Genes (DEGs) is central to RNA-Seq studies, yet their identification suffers from reproducibility issues. This is largely a consequence of the inherent biological and technical variation that cannot be captured with small numbers of replicates. When thresholds for p-values and log2 fold changes are introduced, this variability can propagate an incomplete description of the data, leading to differing interpretations. Here, we compare traditional binary DEG classification with a rank-based method, grounded in Bayesian statistics, using a published yeast dataset comprising over 40 replicates. This analysis reveals how the choice of thresholds and number of replicates results in discrepancies between studies and potentially interesting genes being overlooked. Furthermore, by comparing wild-type with wild-type samples, we show how variability in gene expression can be mistaken for differential expression. Evaluating current practices for navigating the accuracy-error trade-off in the search for differentially expressed genes leads us to advocate rank-based methods and Bayesian statistics to mitigate the limitations of binary classifications and communicate uncertainty.

bioinformatics↗

bioRxiv2025

A Bayesian framework for ranking genes based on their statistical evidence for differential expression

Advances in sequencing technologies have revolutionised our ability to capture the complete RNA profile in tissue samples, allowing for comparative analyses of RNA levels between developmental processes, environmental responses, or treatments. However, quantifying changes in gene expression remains challenging, given inherent biological variability and technological limitations. To address this, we introduce a Bayesian framework for differential gene expression (DGE) analysis. Our framework unifies and streamlines a complex analysis, typically involving parameter estimations and multiple statistical tests, into a concise mathematical equation. This allows statistical evidence for differential expression to be computed rapidly and transparently. We show how this approach can be used to evaluate variabilty of individual genes between replicates. A comparison of our framework with existing tools revealed differences that can be explained by commonly employed thresh-olds in other packages. This motivated us to explore ranking genes based on their statistical evidence as opposed to a binary classification as DEGs. Our analysis leads us to advocate the use of Bayes factors within a rank-based approach. This framework offers enhanced computational efficiency and delivers a transparent way to analyse, interpret and communicate DGE results.

bioinformatics↗

bioRxiv2023

Recognition of Necrosis and Ethylene-inducing like peptides confers disease resistance in Brassica napus and is modulated by BSK1 in Arabidopsis

Brassicas are important crops susceptible to significant losses caused by disease: thus, breeding resistant lines can mitigate the effects of pathogens. MAMPs (microbe-associated molecular patterns) are conserved molecules of pathogens that elicit host defence responses known as pattern-triggered immunity (PTI). Necrosis & Ethylene-inducing peptide 1-like proteins (NLPs) are MAMPs found in a wide range of phytopathogens, including major disease-causing fungal species. We studied the response to the BcNEP2 from Botrytis cinerea as a representative NLP in Brassica napus to improve our understanding of recognition mechanisms that could enable the development of disease-resistant crops. To genetically map regions responsible for NLP recognition, we used an associative transcriptomics (AT) approach using diverse B. napus accessions and bulk segregant analysis (BSA) on DNA pools created from a bi-parental cross of NLP-responsive (Ningyou1) and non-responsive (Ningyou7) lines. In silico mapping with AT identified two peaks associated with NLP recognition on chromosomes A04 and C05 whereas the BSA narrowed it down to a main peak on A04. BSA delimited the region associated with NLP-responsiveness to 3 Mbp, containing [~]245 genes on the Darmor-bzh reference genome. Variants detected in the region were used for KASP marker design and four KASP markers were identified co-segregating with the phenotype. The same pipeline was performed with the ZS11 genome, and the highest associated region was confirmed on chromosome A04. Comparative BLAST analysis revealed there were unannotated clusters of RLP homologs on ZS11 chromosome A04. To reduce the number of candidate genes responsible for NLP recognition, RNA-Seq data was used to detect the unannotated expressed putative genes. Screening the BSA Ning1x7 population demonstrated a highly significant association between NLP-recognition and resistance to Botrytis cinerea. Also, the lines non-responsive to NLP had significantly greater response to the bacterial MAMP flg22. Additionally, BnaA01g02190D, a homologue of Arabidopsis AtBSK1 (At4g35230) BR-SIGNALLING KINASE1, was associated with a high BcNEP2-induced ROS response phenotype. We show that in Arabidopsis, Atbsk1 mutants had significantly lower response to BcNEP2 and increased susceptibility to B. cinerea (p-value=1.12e-14***). Overall, the results define the genomic location for NLP-recognition on the B. napus genome and demonstrate that NLP recognition has a positive contribution to disease resistance which can have practical application in crop improvement.

plant biology↗

bioRxiv2020

Enhanced tissue penetration of antibodies through pressurized immunohistochemistry

To address the inefficiency of passive diffusion for antibody penetration in thick tissue samples, which limits clearing-technique applications, we developed a versatile and simple device to perform antibody incubation under increased barometric pressure. Pressurized immunohistochemistry greatly improves the uniformity, intensity, and depth of fluorescent immunostaining in thick human and mouse brain samples. Furthermore, pressurized immunohistochemistry substantially decreases the time required for classic staining of thin sections. SUBMISSION CATEGORYNew Results

molecular biology↗