Biology subjects

Ghosh, D.

Publications and source records attributed to Ghosh, D..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

On the pH-dependence of α-synuclein amyloid polymorphism and the role of secondary nucleation in seeding experiments

The aggregation of the protein -synuclein is closely associated with several neurodegenerative disorders and as such the structures of the amyloid fibril aggregates have high scientific and medical significance. However, there are dozens of unique atomic-resolution structures of these aggregates, and such a highly polymorphic nature of the -synuclein fibrils hampers efforts in disease-relevant in vitro studies on -synuclein amyloid aggregation. In order to better understand the factors that affect polymorph selection, we studied the structures of -synuclein fibrils in vitro as a function of pH and buffer using cryo-EM helical reconstruction. We find that in the physiological range of pH 5.8-7.4 a pH- dependent selection between Types 1, 2 and 3 polymorphs occurs. Our results indicate that even in the presence of seeds, the polymorph selection during aggregation is highly dependent on the buffer conditions, attributed to the non-polymorph-specific nature of secondary nucleation. We also uncovered two new polymorphs that occur at pH 7.0 in phosphate-buffered saline. The first is a monofilament Type 1 fibril that highly resembles the structure of the juvenile-onset synucleinopathy polymorph found in patient-derived material. The second is a new Type 5 polymorph that resembles a polymorph that has been recently reported in a study that used diseased tissues to seed aggregation. Taken together, our results highlight the shallow amyloid energy hypersurface that can be altered by subtle changes in the environment, including the pH which is shown to play a major role in polymorph selection and in many cases appears to be the determining factor in seeded aggregation. The results also suggest the possibility of producing disease-relevant structure in vitro.

biophysics↗

bioRxiv2023

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms revealing interesting biological insights.

bioinformatics↗

bioRxiv2023

Systematic identification of structure-specific protein-protein interactions

Protein-protein interactions (PPIs) mediate numerous essential functions and regulatory events in living organisms. The physical interactome of a protein can be abnormally altered in response to external and internal cues, thus modulating cell physiology and contributing to human disease. In particular, neurodegenerative diseases due to the accumulation of aberrantly folded and aggregated proteins may lead to alterations in protein interactomes. Identifying changes in the interactomes of normal and disease states of proteins could help to understand molecular disease mechanisms, but current interactomics methods are limited in the ability to pinpoint structure-specific PPIs and their interaction interfaces on a proteome-wide scale. Here, we adapted limited proteolysis-mass spectrometry (LiP-MS) to systematically identify putative structure-specific PPIs by probing protein structural alterations within cellular extracts upon treatment with specific structural states of a given protein. We demonstrate the feasibility of our method to detect well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and show that it pinpoints PPI interfaces. We then applied the LiP-MS approach to study the structure-specific interactors of the Parkinsons disease hallmark protein alpha-synuclein (aSyn). We identified several previously known interactors of both aSyn monomer and amyloid fibrils and provide a resource of novel putative structure-specific interactors for further studies. This approach is applicable to identify structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound structural states of proteins.

systems biology↗

bioRxiv2022

A Platform-Independent Framework for Phenotyping of Multiplex Tissue Imaging Data

Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-accurate representations of the input data, but exhibit signifi-cantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.

bioinformatics↗

bioRxiv2022

The role of myosin contractility as a molecular determinant of traction force regulation

Cells generate traction forces on the extracellular matrix to crawl forward. A complex molecular machinery is involved in the generation, transmission, and transduction of cellular forces inside and outside of cells. The molecular clutch hypothesis, with motors as rudimentary force generators, has been beneficial in modelling the distinctive biomechanical roles played by the components of this machinery. In this paper, we propose an analytical model that incorporates the active dynamics of myosin motors and establishes their roles in regulating the traction force in an experimentally accessible parameter space. As the parameters pertaining to molecular determinants are varied, we show that the system traverses between diverse states of stabilities - from decaying oscillations to self-sustaining limit cycles. The hallmarks of motor-clutch models like load-and-fail dynamics and shift in traction optima are successfully encapsulated. Modulating myosin activity in our model via different pathways exhibits striking shifts in optimal stiffness, providing excellent agreement with experiments and additional testable predictions.

biophysics↗

bioRxiv2022

Atm loss does not radiosensitize a primary mouse model of Pten-deleted brainstem glioma

Diffuse midline gliomas arise in the brainstem and other midline brain structures and cause a large proportion of childhood brain tumor deaths. Radiation therapy is the most effective treatment option, but these tumors ultimately progress. Inhibition of the phosphoinositide-3-kinase (PI3K)-like kinase ataxia telangiectasia mutated (ATM), which orchestrates the cellular response to radiation-induced DNA damage, may enhance the efficacy of radiation therapy. Diffuse midline gliomas in the brainstem contain loss-of-function mutations in the tumor suppressor PTEN, or functionally similar alterations in the phosphoinositide-3-kinase (PI3K) pathway, at moderate frequency. Here, we sought to determine if Atm inactivation could radiosensitize a primary mouse model of brainstem glioma driven by Pten loss. Using Cre/loxP recombinase technology and the RCAS/TVA retroviral gene delivery system, we established a mouse model of brainstem glioma driven by Pten deletion. We find that Pten-null brainstem gliomas are relatively radiosensitive at baseline. In addition, we show that deletion of Atm in the tumor cells does not extend survival of mice bearing Pten-null brainstem gliomas after focal brain irradiation. These results characterize a novel primary mouse model of PTEN-mutated brainstem glioma and provide insights into the mechanism of radiosensitization by Atm deletion, which may guide the design of future clinical trials. Brief SummaryWe develop a mouse model of PTEN-mutated brainstem glioma and find that perturbation of the ATM does not enhance radiation efficacy in this model.

cancer biology↗

bioRxiv2022

Negative Electrospray Supercharging Mechanisms of Oligonucleotide Single Strands and G-quadruplexes

When sprayed from physiological ionic strength, nucleic acids typically end up with low levels of charging and in compact conformations. Increasing the electrospray negative charging of nucleic acids while preserving the native non-covalent interactions can help distinguish solution folds by ion mobility mass spectrometry. To get fundamental insight into the supercharging mechanisms of nucleic acids in the negative mode, we studied model G-quadruplex structures and single strand controls in 100 mM ammonium acetate. We found that adding 0.4% propylene carbonate, 0.4% sulfolane or 0.1% m-NBA induces native supercharging through the charged residue mechanism. However, although 0.4% m-NBA shows the highest supercharging ability, it induces unwanted unfolding of solution-folded G-quadruplexes. The supercharging effect resembles the effect of lowering the ionic strength, which could be explained by partial neutralization of the ampholytes when droplets become more concentrated in their non-aqueous components. The supercharging ability ranks: PC < sulfolane < m-NBA. m-NBA adducts to G-quadruplexes with high charge states confirms that the supercharging agent interacts directly with DNA. Surprisingly, in presence of supercharging agents, more negative charge states also bear more alkali metal ion adducts. This suggests that native supercharging results from larger droplets evaporating to the charged residue, leading to higher concentration of both the supercharging agent and of alkali counterions. However, when negative charge carriers from the electrolyte become too rare, chain ejection accompanied by denaturation, and hence non-native supercharging, can become predominant.

biophysics↗

bioRxiv2022

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular architectures by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering for the spatial contribution. As such, none of the existing approaches has fully accounted for the heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage the spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular architectures. Then, functional principal component analysis (FPCA) targeting sparse data is applied to estimate FPC scores which are then predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, holding other clinical variables constant. Using an ovarian cancer dataset (n = 114) as a case study, we found that the spatial heterogeneity in the TME immune compositions of CD19+ B cells, CD4+ T cells, CD8+ T cells, and CD68+ macrophages, had a significant non-zero effect on the overall survival (p = 0.027). In the simulations studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

bioinformatics↗

bioRxiv2022

MIAMI: Mutual Information-based Analysis of Multiplex Imaging data

MotivationStudying the interaction or co-expression of the proteins or markers in the tumor microenvironment (TME) of cancer subjects can be crucial in the assessment of risks, such as death or recurrence. In the conventional approach, the cells need to be declared positive or negative for a marker based on its intensity. For multiple markers, manual thresholds are required for each marker, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness. ResultsWe present a new method where different marker intensities are viewed as dependent random variables, and the mutual information (MI) between them is considered to be a metric of co-expression. Estimation of the joint density, as required in the traditional form of MI, becomes increasingly challenging as the number of markers increases. We consider an alternative formulation of MI which is conceptually similar but has an efficient estimation technique for which we develop a new generalization. With the proposed method, we analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies. AvailabilityThe associated R package can be found here, https://github.com/sealx017/MIAMI. Contactsouvik.seal@cuanschutz.edu Supplementary informationThe Supplementary Material is attached.

bioinformatics↗

bioRxiv2021

SPF: A Spatial and Functional Data Analytic Approach to cell Imaging data

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subjects clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available Multiplexed Ion beam Imaging (MIBI) triple-negative breast cancer dataset.

bioinformatics↗

bioRxiv2021

DenVar: Density-based Variation analysis of multiplex imaging data

Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) platforms have become increasingly popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions, often known as functional markers, in the TME becomes extremely crucial for subject-specific assessment of risks, such as risk of recurrence and risk of death. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular functional marker, and the other to classify the subjects based on the proportion of the positive cells. The selection of the thresholds has a large impact on the results and an arbitrary selection can lead to an incomprehensible conclusion. In light of this problem, we present a threshold-free distance between the subjects based on the probability densities of the functional markers. The distance can be used to classify the subjects into meaningful groups or can be used in a linear mixed model setup for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling an easier but interpretable analysis of these types of data. With the proposed method, we analyze a lung cancer dataset from an mIHC platform, finding the difference in the density of functional marker HLA-DR to be significantly associated with the overall survival. The approach is also applied on an MIBI triple-negative breast cancer dataset to analyze effects of multiple functional markers. Finally, we demonstrate the reliability of our method through extensive simulation studies.

bioinformatics↗

bioRxiv2021

A Kernel Method for Dissecting Genetic Signals in Tests of High-Dimensional Phenotypes

Genomewide association studies increasingly employ multivariate tests of multiple correlated phenotypes to exploit likely pleiotropy to improve power. Typical multivariate methods produce a global p-value of association between a variant (or set of variants) and multiple phenotypes. When the global test is significant, subsequent interest then focuses on dissecting the signal and, in particular, delineating the set of phenotypes where the genetic variant(s) have a direct effect from the remaining phenotypes where the genetic variant(s) possess either indirect or no effect. While existing techniques like mediation models can be utilized for this purpose, they generally cannot handle high-dimensional phenotypic and genotypic data. To assist in filling this important gap, we propose a modification of a kernel distance-covariance framework for gene mapping of multiple variants with multiple phenotypes to test instead whether the association between the variants and a group of phenotypes is driven through a direct association with just a subset of the phenotypes. We use simulated data to show that our new method controls for type I error and is powerful to detect a variety of models demonstrating different patterns of direct and indirect effects. We further illustrate our method using GWAS data from the Grady Trauma Project and show that an existing signal between genetic variants in the ZHX2 gene and 21 items within the Beck Depression Inventory appears to be due to a direct effect of these variants on only 3 of these items. Our approach scales to genomewide analysis, and is applicable to high-dimensional correlated phenotypes.

genomics↗

bioRxiv2021

Transcriptional regulation of neural stem cell expansion in adult hippocampus

Experience governs neurogenesis from radial-glial neural stem cells (RGLs) in the adult hippocampus to support memory. Transcription factors in RGLs integrate physiological signals to dictate self-renewal division mode. Whereas asymmetric RGL divisions drive neurogenesis during favorable conditions, symmetric divisions prevent premature neurogenesis while amplifying RGLs to anticipate future neurogenic demands. The identities of transcription factors regulating RGL symmetric self-renewal, unlike those that regulate RGL asymmetric self-renewal, are not known. Here, we show that the transcription factor Kruppel-like factor 9 (Klf9) is elevated in quiescent RGLs and inducible, deletion of Klf9 promotes RGL activation state. Clonal analysis and longitudinal intravital 2-photon imaging directly demonstrate that Klf9 functions as a brake on RGL symmetric self-renewal. In vivo translational profiling of RGLs lacking Klf9 generated a blueprint of RGL symmetric self-renewal for stem cell community. Together, these observations identify Klf9 as a transcriptional regulator of neural stem cell expansion in the adult hippocampus.

neuroscience↗

bioRxiv2021

Collation of indigenous and local knowledge as evidence base for herpetofauna conservation outside protected areas: Case study from an agricultural landscape in Eastern India

Systematic appraisal of communitys knowledge as evidence for biodiversity conservation has been widely recognized. For conserving the rich biodiversity in the rural landscape outside the protected areas, it is important to document the knowledge and perception of the farming community. Although such appraisal is available for different taxa, no such systematic study is available for herpetofauna-one of the most vulnerable faunal groups. Our study attempts to document the impact of agricultural intensification on herpetofauna in an agricultural landscape through knowledge and perception appraisal of the farming community. A semi-structured questionnaire survey and validation was conducted in areas of low, medium, and high agricultural intensification. In all areas, farmers indicated an overall decrease in herpetofauna abundance. Farmers at the mid and high agricultural intensification zones reported a more significant decrease in herpetofauna sightings specifically for amphibians and snakes compared to those under low intensification regions. Farmers at low intensification area recognized significantly more herpetofauna. Farmers attributed five major threats to herpetofauna and ranked pesticide as the most significant reason, especially those in higher intensification. The majority were aware of the importance of herpetofauna as a biological pest control agent. Level of education or farming experience did not seem to have any influence on the farmers knowledge. Our findings integrated with other quantitative studies will facilitate future community-driven conservation in the studied agricultural landscapes.

ecology↗

bioRxiv2021

How effective are frogs in regulating crop pest population in a natural multi-trophic system??

Potential of frogs as important natural pest control agents has been highlighted earlier. But the effectiveness of frogs in regulating the pest load in intensive agricultural landscape in a multi-trophic system is not clear. We performed controlled field experiment in paddy field with a varying density (observed in high and low agricultural intensity (AI) areas) of a commonly found frog species and compared the pest and pest predator build-up. The consumption rate of the model amphibian was studied using enclosure experiment. The consequent trophic cascade effect of frogs on both crop pest and other arthropod pest predator was analyzed using mathematical population growth models. Although frogs consumed pests, they could not reduce crop pest abundance. although a lesser frog density found in high AI areas significantly affected the pest predator abundance. Based on the functional response result, mathematical growth models demonstrated that with a constant harvesting factor (Hollings Type II) frogs will always have a negative impact on the beneficial natural enemy population due to intraguild predation thereby limiting its potential as a pest regulator. Our study challenges the notion of frogs as an effective pest control agent and argues that increasing habitat diversity might improve overall biological pest suppression.

ecology↗

bioRxiv2021

Compositional Data Analysis using Kernels in Mass Cytometry Data

MotivationCell type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass cytometry data, especially when the sample size is small. ResultsWe proposed a new multivariate statistical learning methodology, Compositional Data Analysis using Kernels (CODAK), based on the kernel distance covariance (KDC) framework to test the association of the cell type compositions with important predictors (categorical or continuous) such as disease status. CODAK scales well for high-dimensional data and provides satisfactory performance for small sample sizes (n < 25). We conducted simulation studies to compare the performance of the method with existing methods of analyzing cell type abundance data from mass cytometry studies. The method is also applied to a high-dimensional dataset containing different subgroups of populations including Systemic Lupus Erythematosus (SLE) patients and healthy control subjects. Availability and ImplementationCODAK is implemented using R. The codes and the data used in this manuscript are available on the web at http://github.com/GhoshLab/CODAK/. Supplementary informationSupplementary Materials.pdf.

bioinformatics↗

bioRxiv2021

PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes

High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e. nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT is capable of providing more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure. Author SummaryPaIRKAT is a tool for improving testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. Our paper proposes PaIRKAT, a method for including known biological connections between high dimensional variables by representing them as edges of graphs or networks. It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or smoothing approach for managing this issue. We demonstrate the benefits of this approach through simulation studies and an application to the metabolomic data from the COPDGene study.

bioinformatics↗

bioRxiv2021

Pathogenic bacteria grown in iron and zinc milieu exhibit magnetotaxis

The growing number of multiple drug resistant (MDR) bacteria and the dwindling pipeline of new antibiotics are driving us towards a post-antibiotic era in which even common infections would become difficult to treat. To address this, an antibiotic-free strategy that can combat multiple bacteria is recommended. Most of the proposed approaches nevertheless have several limitations, including bacterial targeting. To overcome such limitations, the proposed strategy employs the bacterial machinery to self-destruct. Herein, the biosynthesis of magnetic nanoparticle (MNP) is reported for the first time in multiple pathogenic bacteria, including MDR bacteria. The intracellular MNPs composed of superparamagnetic zinc ferrites were formed in presence of iron and zinc precursors. Exposure of the treated bacteria/biofilms to an alternating magnetic field (AMF) exhibited hyperthermia (5-6{degrees}C) and a dramatic decrease in bacterial viability, suggesting the MNPs therapeutic potential. Likewise, the bacteria existing in vivo biosynthesize the MNPs by mining these elements from the host. To determine its therapeutic efficacy, the infected tissues were exposed directly to AMF. A 3-4 log reduction in bacterial burden, as compared to antibiotics treatment, confirmed the significance of using naturally existing MNPs to combat bacterial infections. The proposed broad-spectrum approach can therefore aid in overcoming the challenges facing anti-bacterial therapies.

microbiology↗