Biology subjects

Bose, N.

Publications and source records attributed to Bose, N..

8 recordsLinked to original sources

bioRxiv2025

L2 loop engineering enhances the enzymatic activity and synergism property of the AA16 family Lytic polysaccharide monooxygenase enzyme from Aspergillus fumigatus.

Lytic polysaccharide monooxygenase (LPMO) is an enzyme that has enormous potential for industrial applications. It has a synergistic effect with the cellulase enzyme complex. The LPMO enzymes typically adopt a compact {beta}-sandwich fold that consists of a 7 to 9 {beta}-strand with a flat active site containing copper in its active site. There are a few loops in LPMO, among them the L2 loop is reported to take a key role in shaping the active site and substrate binding. Now, in this work, we want to investigate the role of the L2 loop in enzymatic activity and synergistic effect. In achieving our goal, we have replaced the L2 loop of our concerning AfLPMO16 with other L2 loops from different LPMOs: HiLPMO9B (PDB: 5NNS), McLPMO9 (PDB: 7NTL) and CsLPMO9 (PDB: 7EXK). Interestingly, L2 loop replacement from CsLPMO9 (PDB: 7EXK) showed enhanced activity and synergism compared to others. The secondary structural analysis by circular dichroism also suggested that it changed the structure significantly. Moreover, this is the first report of complete L2 loop engineering in LPMO.

biochemistry↗

bioRxiv2023

FocA: A deep learning tool for reliable, near-real-time imaging focus analysis in automated cell assay pipelines

The increasing use of automation in cellular assays and cell culture presents significant opportunities to enhance the scale and throughput of imaging assays, but to do so, reliable data quality and consistency are critical. Realizing the full potential of automation will thus require the design of robust analysis pipelines that span the entire workflow in question. Here we present FocA, a deep learning tool that, in near real-time, identifies in-focus and out-of-focus images generated on a fully automated cell biology research platform, the NYSCF Global Stem Cell Array(R). The tool is trained on small patches of downsampled images to maximize computational efficiency without compromising accuracy, and optimized to make sure no sub-quality images are stored and used in downstream analyses. The tool automatically generates balanced and maximally diverse training sets to avoid bias. The resulting model correctly identifies 100% of out-of-focus and 98% of in-focus images in under 4 seconds per 96-well plate, and achieves this result even in heavily downsampled data ([~]30 times smaller than native resolution). Integrating the tool into automated workflows minimizes the need for human verification as well as the collection and usage of low-quality data. FocA thus offers a solution to ensure reliable image data hygiene and improve the efficiency of automated imaging workflows using minimal computational resources.

bioinformatics↗

bioRxiv2023

ScaleFExSM: a lightweight and scalable method to extract fixed features from single cells in high-content imaging screens

Leveraging artificial intelligence (AI) in image-based morphological profiling of cell populations is proving increasingly valuable for identifying diseased states and drug responses in high-content imaging (HCI) screens. When the differences between populations (such as a healthy and diseased) are completely unknown and undistinguishable by the human eye, it is crucial that HCI screens are large in scale, allowing numerous replicates for developing reliable models, as well as accounting for confounding factors such as individual (donor) and intra-experimental variation. However, as screen sizes increase, challenges arise including the lack of scalable solutions for analyzing high-dimensional datasets and processing the results in a timely manner. For this purpose, many tools have been developed to reduce images into a set of features using unbiased methods, such as embedding vectors extracted from pre-trained neural networks or autoencoders. While these methods preserve most of the predictive power contained in each image despite reducing the dimensionality significantly, they do not provide easily interpretable information. Alternatively, techniques to extract specific cellular features from data are typically slow, difficult to scale, and often produce redundant outputs, which can lead to the model learning from irrelevant data, which might distort future predictions. Here we present ScaleFExSM, a memory efficient and scalable open-source Python pipeline that extracts biologically meaningful features from large high-content imaging datasets. It requires only modest computational resources but can also be deployed on high-powered cloud computing infrastructure. ScaleFExSM can be used in conjunction with AI models to cluster data and subsequently explore, identify, and rank features to provide insights into the morphological hallmarks of the phenotypic categories. We demonstrate the performance of this tool on a dataset consisting of control and drug-treated cells from a cohort of 20 donors, benchmarking it against the state-of-the-art tool, CellProfiler, and analyze the features underlying the phenotypic shift induced by chemical compounds. In addition, the tools generalizability and utility is shown in the analysis of publicly available datasets. Overall, ScaleFExSM constitutes a robust and compact pipeline for identifying the effects of drugs on morphological phenotypes and defining interpretable features that can be leveraged in disease profiling and drug discovery.

cell biology↗

bioRxiv2022

Reduced ribosome activity influences the non-uniform evolution of 16S rRNA hypervariable regions

16S rRNA gene sequences are commonly analyzed for taxonomic and phylogenetic studies because they contain hypervariable regions that can help distinguish different genera. However, intra-genus distinction is often difficult due to high sequence identities among closely related species. Although common tools for 16S sequence taxonomic classification weight residue variations equally during comparisons, specific residues within hypervariable regions have not drifted evenly through evolution, suggesting that portions of them may be biologically important. We developed an in vivo test system where 16S variants coexisted among natural ribosome populations which allowed their fitness to be evaluated. We found that versions with evolutionarily disparate hypervariable regions were underpopulated in ribosomes and active translation pools, even for a single nucleotide polymorphism (SNP), which indicates functional constraints to the free evolutionary drift of hypervariable regions. Using an in silico method (positional relative entropy), we analyzed over 12,000 16S V3-V4 sequences across Escherichia and Shigella strains and identified species that can be distinguished by position-specific SNPs present in multiple 16S alleles in a genome. When we evaluated these informative SNPs with our in vivo system, we discovered that ribosomes harboring them were compromised, suggesting that their evolution is indeed biologically constrained. Overall, this study demonstrates that SNPs within hypervariable regions are not necessarily inconsequential and that common computational approaches for taxonomic 16S rRNA sequence classification should not assume an even probability of residues at each position. ImportanceHypervariable regions within 16S rRNA genes are commonly analyzed to determine microbial diversity. However, because sequences within a genus are highly similar, strain- or species-specific identification is often uncertain. Because there are no established functions of hypervariable regions, residue variations within them are often evenly weighted when making taxonomic comparisons. This study established that 16S rRNAs with naturally occurring variations in hypervariable regions can affect ribosome quality, indicating that their residues should not be weighted equally during taxonomic sequence classifications.

microbiology↗

bioRxiv2021

Microbial communities in retail draft beers and the biofilms they produce

In the beer brewing industry, microbial spoilage presents a consistent threat that must be monitored and controlled to ensure the palatability of a finished product. Many of the predominant beer spoilage microbes have been identified and characterized, but the mechanisms of contamination and persistence remain an open area of study. Post-production, many beers are distributed as kegs that are attached to draft delivery systems in retail settings where ample opportunities for microbial spoilage are present. As such, restaurants and bars can experience substantial costs and downtime for cleaning when beer draft lines become heavily contaminated. Spoilage monitoring on the retail side of the beer industry is often overlooked, yet this arena may represent one of the largest threats to the profitability of a beer if its flavor profile becomes substantially distorted. In this study, we sampled and cultured microbial communities found in beers dispensed from a retail draft system to identify the contaminating bacteria and yeasts. We also evaluated their capability to establish new biofilms in a controlled setting. Among four tested beer types, we identified over a hundred different contaminant bacteria and nearly twenty wild yeasts. The culturing experiments demonstrated that most of these microbes were viable and capable of joining new biofilm communities. From these data, we provide an important starting point for the efficient monitoring of beer spoilage in draft systems and provide suggestions for cleaning protocol improvements that can benefit the retail community. ImportanceBeer production, packaging, and service are each vulnerable to contamination by microbes that metabolize beer chemicals and impart undesirable flavors, which can result in the disposal of entire batches. Therefore, great effort is taken by brewmasters to reduce and monitor contamination during production and packaging. A commonly overlooked quality control stage of a beer supply chain is at the retail service end, where beer kegs supply draft lines in bars and restaurants under non-sterile conditions. We found that retail draft line contamination is rampant and that routine line cleaning methods are insufficient to efficiently suppress beer spoilage. Thus, many customers unknowingly experience spoiled versions of the beers they consume. This study identified the bacteria and yeast that were resident in draft beer samples and also assessed their abilities to colonize tubing material as members of stable biofilm communities.

microbiology↗

bioRxiv2021

Comprehensive Proteomic Quantification of Bladder Stone Progression in a Cystinuric Mouse Model Using Data-Independent Acquisitions

Cystinuria is one of various disorders that cause biomineralization in the urinary system, including bladder stone formation in humans. It is most prevalent in children and adolescents and more aggressive in males. There is no cure, and only limited disease management techniques help to solubilize the stones. Recurrence, even after treatment, occurs frequently. Other than a buildup of cystine, little is known about factors involved in the formation, expansion, and recurrence of these stones. This study sought to define the growth of bladder stones, guided by micro-computed tomography imaging, and to profile dynamic stone proteome changes in a cystinuria mouse model. After bladder stones developed in vivo, they were harvested and separated into four developmental stages (sand, small, medium and large stone), based on their size. Data-dependent and data-independent acquisitions allowed deep profiling of stone proteomics. The proteomic signatures and pathways illustrated major changes as the stones grew. Stones initiate from a small nidus, grow outward, and show major enrichment in ribosomal proteins and factors related to coagulation and platelet degranulation, suggesting a major dysregulation in specific pathways that can be targeted for new therapeutic options.

cell biology↗

bioRxiv2018

A conserved role of the insulin-like signaling pathway in uric acid pathologies revealed in Drosophila melanogaster

Elevated uric acid (UA) is a key factor for disorders, including gout or kidney stones and result from abrogated expression of Urate Oxidase (Uro) and diet. To understand the genetic pathways influencing UA metabolism we established a Drosophila melanogaster model with elevated UA using Uro knockdown. Reduced Uro expression resulted in the accumulation of UA concretions and diet-dependent shortening of lifespan. Inhibition of insulin-like signaling (ILS) pathway genes reduced UA and concretion load. In humans, SNPs in the ILS genes AKT2 and FOXO3 were associated with UA levels or gout, supporting a conserved role for ILS in modulating UA metabolism. Downstream of the ILS pathway UA pathogenicity was mediated partly by NADPH Oxidase, whose inhibition attenuated the reduced lifespan and concretion accumulation. Thus, genes in the ILS pathway represent potential therapeutic targets for treating UA associated pathologies, including gout and kidney stones.\n\nHighlightsO_LIIn Drosophila high uric acid (UA) levels shorten lifespan and cause UA aggregation\nC_LIO_LIConserved in flies and humans, the ILS pathway associates with UA pathologies\nC_LIO_LIFoxO dampens concretion formation by reducing UA levels and ROS formation\nC_LIO_LIInhibition of NOX alleviates the lifespan attenuation and UA aggregation\nC_LI

pathology↗

bioRxiv2017

Predator-secreted sulfolipids induce fear-like defense responses in C. elegans

Animals respond to predators by altering their behavior and physiological states, but the underlying signaling mechanisms are poorly understood. Using the interactions between Caenorhabditis elegans and its predator, Pristionchus pacificus, we show that neuronal perception by C. elegans of a predator-specific molecular signature induces instantaneous escape behavior and a prolonged reduction in oviposition. Chemical analysis revealed this predator-specific signature to consist of a class of sulfolipids, produced by a biochemical pathway required for developing predacious behavior and specifically induced by starvation. These sulfolipids are detected by four pairs of C. elegans amphid sensory neurons that act redundantly and recruit cyclic nucleotide-gated (CNG) or transient receptor potential (TRP) channels to drive both escape and reduced oviposition. Specific abolishment of predator-evoked C. elegans responses by the anti-anxiety drug sertraline as well as functional homology of the delineated signaling pathways suggests a conserved or convergent strategy for managing predator threats.

neuroscience↗