Biology subjects

Ghosh, P.

Publications and source records attributed to Ghosh, P..

At least 73 records · Page 4Linked to original sources

bioRxiv2023

A Living Organoid Biobank of Crohn's Disease Patients Reveals Molecular Subtypes for Personalized Therapeutics

ABSTRACT (Structured)Crohns disease (CD) is a complex, clinically heterogeneous disease of multifactorial origin; there is no perfect pre-clinical model, little insight into the basis for such heterogeneity, and still no cure. To address these unmet needs, we sought to explore the translational potential of adult stem cell-derived organoids that not only retain their tissue identity, but also their genetic and epigenetic disease-driving traits. We prospectively created a biobank of CD patient-derived organoid cultures (PDOs) using biopsied tissues from colons of 34 consecutive subjects representing all clinical subtypes (Montreal Classification B1-B3 and perianal disease). PDOs were generated also from healthy subjects. Comparative gene expression analyses enabled benchmarking of PDOs as tools for modeling the colonic epithelium in active disease and revealed that despite the clinical heterogeneity there are two major molecular subtypes: immune-deficient infectious-CD [IDICD] and stress and senescence-induced fibrostenotic-CD [S2FCD]. The transcriptome, genome and phenome show a surprising degree of internal consistency within each molecular subtype. The spectrum of morphometric, phenotypic, and functional changes within the "living biobank" reveals distinct differences between the molecular subtypes. These insights enabled drug screens that reversed subtype-specific phenotypes, e.g., impaired microbial clearance in IDICD was reversed using agonists for nuclear receptors, and senescence in S2FCD was rectified using senotherapeutics, but not vice versa. Phenotyped-genotyped CD-PDOs may fill the gap between basic biology and patient trials by enabling pre-clinical Phase 0 human trials for personalized therapeutics. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/532245v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@1edcea0org.highwire.dtl.DTLVardef@198a3c8org.highwire.dtl.DTLVardef@28d394org.highwire.dtl.DTLVardef@5a9dbb_HPS_FORMAT_FIGEXP M_FIG C_FIG In BriefThis work creates a prospectively biobanked phenotyped-genotyped Crohns disease patient-derived organoids (CD-PDOs) as platforms for molecular subtyping of disease and for ushering personalized therapeutics. HIGHLIGHTSO_LIProspectively biobanked CD-organoids recapitulate the disease epithelium in patients C_LIO_LIThe phenome-transcriptome-genome of CD-organoids converge on two molecular subtypes C_LIO_LIOne subtype shows impaired microbial clearance, another increased cellular senescence C_LIO_LIPhenotyped-genotyped PDOs are then used for integrative and personalized therapeutics C_LI

immunology↗

bioRxiv2023

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

ContextProtein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ( classic type functions of proteins) or a signal transduction ( non-classic), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters ({Delta}Gbinding, Kd) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe {Delta}Gbinding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX etc.) that directly compute {Delta}Gbinding without involving intermediate structural descriptors. MethodsHere we present EnCPdock (www.scinetmol.in/EnCPdock/), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted {Delta}Gbinding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and, renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional Complementarity Plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Prfmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

bioinformatics↗

bioRxiv2023

Intracellular interaction of FNDC1/FN1/AR/cMYC in prostate cancer cells

MicroRNAs play an important role in modulating normal cellular functions through protein-protein interaction in addition to regulating gene expression via mRNA degradation or translational repression. PVT1 is a non-protein coding gene that encodes six annotated microRNAs (miRNAs), including miR-1207-3p, a demonstrated modulator in PCa. MiR-1207-3p is significantly underexpressed in PCa cells when compared to normal prostatic cells and directly targets fibronectin type III domain containing 1 (FNDC1), which was found to be overexpressed in PCa cells along with concomitant overexpression of fibronectin (FN1)/androgen receptor (AR)/c-MYC. To better understand the role of FNDC1/FN1/AR/c-MYC in PCa progression we examined the interaction between FNDC1/FN1/AR/c-MYC. Coimmunoprecipitation study showed direct physical interaction between FNDC1/FN1/AR/c-MYC in PCa cells and FN1/AR/cMYC in normal prostatic cell. Knockdown of FN1, AR and cMYC not only confirmed FN1/FNDC1/AR/cMYC interaction but also gave rise to the possibility of a complex. We also examined the spatial localization of the FNDC1/FN1/AR/c-MYC pathway by performing immunofluorescence staining in PCa cells. Single staining analysis revealed that FNDC1, AR and c-MYC localize to the nucleus and cytoplasm while FN1 localizes only to the cytoplasm in PCa cells but not in the non-tumorigenic prostate cells. However, our findings show that miR-1207-3p has no direct effect on the FNDC1/FN1/AR/cMYC interaction. Understanding this molecular interaction can reveal additional insights into the role of FNDC1/FN1/AR/cMYC interaction in PCa progression.

cancer biology↗

bioRxiv2022

Growth Signaling Autonomy in Circulating Tumor Cells Aids Metastatic Seeding

Self-sufficiency (autonomy) in growth signaling, the earliest recognized hallmark of cancer, is fueled by the tumor cells ability to secrete-and-sense growth factors; this translates into cell survival and proliferation that is self-sustained by auto-/paracrine secretion. A Golgi-localized circuitry comprised of two GTPase switches has recently been implicated in the orchestration of growth signaling autonomy. Using breast cancer cells that are either endowed or impaired (by gene editing) in their ability to assemble the circuitry for growth signaling autonomy, here we define the transcriptome, proteome, and phenome of such autonomous state, and unravel its role during cancer progression. We show that autonomy is associated with enhanced molecular programs for stemness, proliferation, and epithelial-mesenchymal plasticity (EMP). Autonomy is both necessary and sufficient for anchorage-independent growth factor-restricted proliferation and resistance to anti-cancer drugs and is required for metastatic progression. Transcriptomic and proteomic studies show that autonomy is associated, with a surprising degree of specificity, to self-sustained EGFR/ErbB signaling. Derivation of a gene expression signature for autonomy revealed that growth signaling autonomy is uniquely induced in circulating tumor cells (CTCs), the harshest phase in the life of tumor cells when it is deprived of biologically available EGF. We also show that autonomy in CTCs tracks therapeutic response and prognosticates outcome. These data support a role for growth signaling autonomy in multiple processes essential for the blood-borne dissemination of human breast cancer. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/518910v2_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@167b370org.highwire.dtl.DTLVardef@135b62dorg.highwire.dtl.DTLVardef@14b5e9forg.highwire.dtl.DTLVardef@1311e84_HPS_FORMAT_FIGEXP M_FIG C_FIG Significance StatementA Golgi-localized molecular circuitry has been recently implicated in the orchestration of secrete-and-sense auto-/paracrine loops that impart self-sufficiency in growth signaling, a.k.a., growth signaling autonomy. Using a transdisciplinary approach, this work shows that growth signaling autonomy is uniquely induced in tumor cells that are in circulation. Circulating tumor cells (CTCs) represent a brutish and risky phase in the lifetime of tumor cells when they are exposed to the immune system and hemodynamic sheer forces, all in the setting of growth factor starvation. Cancer cells appear to rely on the autonomy circuit to survive and enhance their fitness to seed metastases. Autonomy generates the kind of eat-what-you-kill entrepreneurial spirit which minimizes the risk of CTCs dying on an otherwise risky journey.

cancer biology↗

bioRxiv2022

Artificial Intelligence Guided Discovery of Gastric Cancer Continuum

BackgroundDetailed understanding of pre, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to Gastric Cancers (GCs) and medical treatment to intercept such progression. MethodsWe built a Boolean Implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low-to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean Implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean Implication relationships can decipher fundamental time series underlying the biological data. Pursuing this method, we developed a healthy mucosa [->]GC continuum model based on this approach. ResultsOur model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia [->]dysplasia [->]neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression. ConclusionsA Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression. MINI-ABSTRACTWe developed predictive models of early and late neoplastic states in gastric cancer and identified gene clusters that are up/down-regulated at various points along the gastric cancer disease continuum.

bioinformatics↗

bioRxiv2022

Phosphorylation of Gαi shapes canonical Gα(i)βγ/GPCR signaling

A long-standing question in the field of signal transduction is to understand the interplay between distinct signaling pathways that control cell behavior. For growth factors and heterotrimeric G proteins, the two major signaling hubs in eukaryotes, the mechanisms of independent signal transduction have been extensively characterized; however, if/how they may cross talk remains obscure. Here we use linear-ion-trap mass spectrometry in combination with cell-based biophysical, biochemical, and phenotypic assays to chart at least three distinct ways in which growth factors may impact canonical G(i){beta}{gamma} signaling downstream of a GPCR (CXCR4) via phosphorylation of Gi. Phosphomimicking mutations in a cluster of residues in the E helix (Y154/Y155) result in the suppression of agonist-induced G(i) activation while promoting constitutive G{beta}{gamma} signaling; others in the P-loop (Ser44, Ser47, Thr48) suppress Gi activation entirely thus completely segregating the growth factor and GPCR pathways. While most phosphoevents appear to impact, as expected, the core properties of G(i) (conformational stability, nucleotide binding, G{beta}{gamma} association and release, etc.), one phosphomimicking mutation promoted mislocalization of Gi from the plasma membrane: a novel and unexpected mechanism of GPCR signal suppression. A phosphomutation of C-terminal Y320 was sufficient to orchestrate such suppression by protein compartmentalization. Findings not only elucidate how growth factor and chemokine signals crosstalk through phosphomodulation of Gi, but also how such crosstalk may generate signal diversity.

cell biology↗

bioRxiv2022

Accountable Prediction of Drug ADMET Properties with Molecular Descriptors

Drugs are chemical substances of low molecular weights that require to travel from the site of administration to the site of action. For safety and effective permeability, drugs are required to exhibit ideal absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. Given the Simplified Molecular Input Line Entry System (SMILES) representation of drugs, we aim to predict their ADMET properties. By feeding molecular descriptors (as global features) to traditional machine learning models, we show that ADMET properties of drug molecules can be predicted with an accuracy competitive with the state-of-the-art deep learning models. We demonstrate that the proposed approach with only 31 molecular descriptors beats the state-of-the-art for 3 datasets. Moreover, it stands the second best for 2 other datasets, where none of the best provides a statistically significant improvement. We also demonstrate that two-dimensional descriptors can better represent absorption, distribution and excretion properties than the fingerprints widely used in the literature. However, they fail to distinguish metabolism and toxicity properties.

bioinformatics↗

bioRxiv2022

Machine Learning Identifies Signatures of Macrophage Reactivity and Tolerance that Predict Disease Outcomes

Single-cell transcriptomic studies have greatly improved organ-specific insights into macrophage polarization states are essential for the initiation and resolution of inflammation in all tissues; however, such insights are yet to translate into therapies that can predictably alter macrophage fate. Using machine learning algorithms on human macrophages, here we reveal the continuum of polarization states that is shared across diverse contexts. A path, comprised of 338 genes accurately identified both physiologic and pathologic spectra of "reactivity" and "tolerance", and remained relevant across tissues, organs, species and immune cells (> 12,500 diverse datasets). This 338-gene signature identified macrophage polarization states at single-cell resolution, in physiology and across diverse human diseases, and in murine pre-clinical disease models. The signature consistently outperformed conventional signatures in the degree of transcriptome-proteome overlap, and in detecting disease states; it also prognosticated outcomes across diverse acute and chronic diseases, e.g., sepsis, liver fibrosis, aging and cancers. Crowd-sourced genetic and pharmacologic studies confirmed that model-rationalized interventions trigger predictable macrophage fates. These findings provide a formal and universally relevant definition of macrophage states and a predictive framework (http://hegemon.ucsd.edu/SMaRT) for the scientific community to develop macrophage-targeted precision diagnostics and therapeutics. One Sentence SummarySignatures of macrophage reactivity and tolerance (SMaRT) predict disease outcomes

systems biology↗

bioRxiv2022

Design principles of dose-response alignment in coupled GTPase switches

"Dose-response alignment" (DoRA), where the downstream response of cellular signaling path-ways closely matches the fraction of activated receptor, can improve the fidelity of dose information transmission. It is believed that a key component for DoRA is negative feedback and thus a natural question that arises is whether there exist design principles for signaling motifs within such negative feedback loops, which may enable these motifs to attain near-perfect DoRA. Here, we investigated several model formulations of an experimentally validated circuit that couples two molecular switches--mGTPase (monomeric GTPase) and tGTPase (heterotrimeric GTPases) -- with negative feedback loops. We find that, in the absence of feedback, the low and intermediate mGTPase activation levels benefit DoRA in the mass action and Hill-function models, respectively. In other cases, where the mass action model with a high mGTPase activation level or the Hill-function model with a non-intermediate mGTPase activation level, the DoRA can be improved by adding negative feedback loops. Furthermore, we found that DoRA in a longer cascade (i.e., tGTPase) can be obtained using Hill-function kinetics under certain conditions. In summary, we show how ranges of activity of mGTPase, reaction kinetics, the negative feedback, and the cascade length affect DoRA. This work provides a framework for improving the DoRA performance in signaling motifs with negative feedback loops. Significance StatementDose-response alignment helps cells faithfully transmit dose information; how this alignment is achieved in motifs with negative feedback is unclear. Through rigorous studies interrogating a naturally occurring motif comprised of two species of GTPases coupled by negative feedback loops, this work reveals the versatile roles of negative feedback loops and GTPase regulators on DoRA. We find that the negative feedback can enhance DoRA only with specific kinetic forms and with certain ranges of GTPases activation levels. This knowledge advances our understanding of the role of negative feedback on DoRA and sheds light on the importance of dynamic range of signaling processes as an essential determinant of how cells transfer information about stimuli. Findings can help design signaling circuits with better DoRA behavior, and ultimately augment cell signaling studies.

systems biology↗

bioRxiv2022

Water-Quality analysis and Fish diversity of Southern West Part of the West-Bengal

Marine fishes are one of the important factors to stabilize the local aquatic ecosystem and regulating socio-economy of local fishermen. In this study we emphasized upon finding out the interrelationship in the water quality, anthropogenic activity, and fish habitat through the 31km stretch of East Medinipur coast in West Bengal, India which is known for its tourist destinations. The study was conducted monthly on different trawler fish landing sites from Dec 2018 to Dec 2021. During this period, we took fish samples and identified them. We took water quality data such as SST, Concentration of Chl-a, Turbidity, and DO for further correlation between the water quality and species diversity. Total 154 numbers of commercially important marine fish species were documented. Among them, 12 species are from Elasmobranch and the rest is Actinopterygii. As per the IUCN database, 13% of the total fish species are under the red list category and 16% of the species showing decreasing population trend. After analyzing the water quality data, we found out that DO, SST, Turbidity, and Chlorophyll-A correlate with the species richness and it also differs during the seasons.

ecology↗

bioRxiv2022

A mechanistic understanding of biofilm morphogenesis: Coexistence of mobile and sessile aggregates and phase-separated patterns

Most bacteria in the natural environment self-organize into collective phases such as cell clusters, swarms, patterned colonies, or biofilms. The occurrence of different phases and their coexistence is governed by several intrinsic and extrinsic factors such as the growth, motion, and physicochemical interactions. Hence, it is crucial to predict the conditions under which a collective phase emerges due to the individual-level interactions. Here we develop a particle-based biophyiscal model of bacterial cells and self-secreted extracellular polymeric substances (EPS) to decipher the interplay of growth, motility-mediated dispersal, and mechanical interactions during microcolony morphogenesis. We show that depending upon the heterogeneous production and physicochemical properties of EPS, whether sticky or nonadsorbing in nature, the microcolony dynamics and architecture significantly varies. In particular, in sticky EPS, microcolony shows the coexistence of both motile and sessile aggregates rendering a transition towards biofilm formation. Wherein for the nonad-sorbing EPS, which behaves as depletant in the media, a variety of phase-segregated patterned colonies either localizing the matrix component or cells at the colony periphery may emerge. We identified that the interplay of differential dispersion and the mechanical interactions among the components of the colony determines the fate of the colony morphology. Our results provide a significant understanding of the mechano-self-regulation during biofilm morphogenesis and open up possibilities of designing experiments to test the predictions.

biophysics↗

bioRxiv2022

Strengthening of enterococcal biofilms by Esp

Multidrug-resistant (MDR) Enterococcus faecalis are major causes of hospital-acquired infections. Numerous clinical strains harbor a large pathogenicity island that encodes enterococcal surface protein (Esp), which is suggested to promote biofilm production and virulence, but this remains controversial. To resolve this issue, we characterized the Esp N-terminal region, the portion implicated in biofilm production. Small angle X-ray scattering indicated that the N-terminal region had a globular head, which consisted of two DEv-Ig domains as visualized by X-ray crystallography, followed by an extended tail. The N-terminal region was not required for biofilm production but instead significantly strengthened biofilms against mechanical or degradative disruption, greatly increasing retention of Enterococcus within biofilms. Biofilm strengthening required low pH, which resulted in Esp unfolding, aggregating, and forming amyloid-like structures. The pH threshold for biofilm strengthening depended on protein stability. A truncated fragment of the first DEv-Ig domain, plausibly generated by a host protease, was the least stable and sufficient to strengthen biofilms at pH [≤] 5.0, while the entire N-terminal region and intact Esp on the enterococcal surface was more stable and required a pH [≤] 4.3. These results suggested a virulence role of Esp in strengthening enterococcal biofilms in acidic abiotic or host environments.

microbiology↗

bioRxiv2022

BOA: A Partitioned View of Genome Assembly

De novo genome assembly is a fundamental problem in computational molecular biology that aims to reconstruct an unknown genome sequence from a set of short DNA sequences (or reads) obtained from the genome. High throughput sequencers could generate several billions of such short reads in a single run. However, the relative ordering of the reads along the target genome is not known a priori. This lack of information is one of the main contributors to the increased complexity of the assembly process. Typically, state-of-the-art approaches produce an ordering of the reads toward the end of the assembly process, making it rather too late to benefit from the ordering information. In this paper, with the dual objective of improving assembly quality as well as exposing a high degree of parallelism for assemblers, we present a partitioning-based approach. Our framework--which we call BOA (for bucket-order-assemble)--uses a bucketing alongside graph- and hypergraph-based partitioning techniques to produce a partial ordering of the reads. This partial ordering enables us to divide the read set into disjoint blocks that can be independently assembled in parallel using any state-of-the-art serial assembler of choice. We tested the BOA framework on a variety of genomes. Experimental results show that the hypergraph variant of our approach, Hyper-BOA, consistently improves both the overall assembly quality and performance. For the inputs tested, the Hyper-BOA framework consistently improves the N50 values of the popular standalone MEGAHIT assembler by an average of 1.70x and up to 2.13x; while the largest alignment length improves 1.47x on average and up to 1.94x. The time to solution also consistently improves between 3-4x for the system sizes tested.

genomics↗

bioRxiv2022

Coupling of NOD2 to GIV is Required for Bacterial Sensing

ABSTRACT/SUMMARYSensing of pathogens by Nucleotide oligomerization domain (NOD)-like 2 receptor (NOD2) induces a protective inflammatory response that coordinates bacterial clearance. Polymorphisms in NOD2 impair bacterial clearance, leading to chronic gut inflammation in Crohns disease (CD) via mechanisms that remain incompletely understood. We identify GIV/Girdin (CCDC88A) as a NOD2-interactor that shapes bacterial sensing-and-signaling in macrophages. Myeloid-specific GIV depletion exacerbated and protracted infectious colitis and abolished the protective effect of muramyl dipeptide (MDP) in both chemical colitis and severe sepsis. In the presence of GIV, macrophages enhance anti-bacterial pathways downstream of NOD2, clear microbes rapidly and concomitantly suppress inflammation. GIVs actions are mediated via its C-terminus, which directly binds the terminal leucine-rich repeat (LRR#10) of NOD2; binding is augmented by MDP and ATP, precedes receptor oligomerization, and is abolished by the 1007fs CD-risk variant which lacks LRR#10. Findings illuminate mechanisms that underlie protective NOD2 signaling and loss of function in the major 1007fs variant. In briefThis work reveals a mechanism by which macrophages use their innate immune sensor, NOD2, to protect the host against overzealous inflammation during bacterial infections, and the consequences of its loss, as occurs in the most important Crohns disease-risk variant. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/489574v2_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@139a1feorg.highwire.dtl.DTLVardef@ba5354org.highwire.dtl.DTLVardef@b4d933org.highwire.dtl.DTLVardef@11fe12e_HPS_FORMAT_FIGEXP M_FIG GRAPHIC ABSTRACT C_FIG HIGHLIGHTSO_LIGIV is a functional and direct interactor of the terminal LRR repeat of NOD2 C_LIO_LIMice lacking M[FE] GIV develop dysbiosis, protracted ileocolitis and sepsis C_LIO_LIMDP/NOD2-dependent protective host responses require GIV C_LIO_LICD-risk NOD2 1007fs variant lacking the terminal LRR#10 cannot bind GIV C_LI

immunology↗

bioRxiv2022

Ganglioside enriched phospholipid vesicles induce cooperative Aβ oligomerization and membrane disruption

A major hallmark of Alzheimer disease (AD) is the accumulation of extracellular aggregates of amyloid-{beta} (A{beta}). Structural polymorphism observed among A{beta} fibrils in AD brains seem to correlate with the clinical sub-types suggesting a link between fibril polymorphism and pathology. Since fibrils emerge from a templated growth of low-molecular weight oligomers, understanding the factors affecting oligomer generation is important. The membrane lipids are key factors that influence early stages of A{beta} aggregation and oligomer generation, and cause membrane disruption. We have previously demonstrated that conformationally discrete A{beta} oligomers can be generated by modulating the charge, composition, chain length of lipids and surfactants. Here, we extend our studies into liposomal models by investigating A{beta} oligomerization on large unilamellar vesicles (LUVs) of total brain extracts (TBE), reconstituted lipid rafts (LRs) or 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC). Specifically, we varied the vesicle composition by varying the amount of GM1 gangliosides added as a constituent. We found that liposomes enriched in GM1 induce the formation of toxic, low-molecular weight oligomers that are isolable in a lipid-complexed form. Importantly, the data indicate that oligomer formation and membrane disruption are highly cooperative processes. Numerical simulations on the experimental data confirm cooperativity and reveal that GM1-enriched liposomes form twice as many numbers of pores as those without GM1. Overall, this study uncovers mechanisms of cooperativity between oligomerization and membrane disruption under controlled lipid compositional bias, and refocuses the significance of the early stages of A{beta} aggregation in polymorphism, propagation, and toxicity in AD. TOC figure O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

biophysics↗

bioRxiv2022

Concurrent separation of phase-locked and non-phase-locked activity

Brain dynamics recorded via electroencephalography (EEG) is conceptualized as a sum of two components, "phase-locked" and "non-phase-locked" to the stimulus. Both activities are understood to be stemming from different neuronal mechanisms and hence accurately characterizing them is of immense importance in neuroscientific studies. Here, we discuss the drawbacks of currently used methods to separate the phase-locked and non-phase-locked activity and propose a new method that decomposes the two components simultaneously. First, we demonstrate that single-trial separation of phase-locked and non-phase-locked power is an ill-posed problem. Second, using simulations where ground truth validation is possible, we elucidate the drawbacks of the widely used averaging method and efficacy of the proposed concurrent phaser method (CPM). Using two experimental datasets, audio oddball EEG data and auditory steady-state responses (ASSR) we show how the empirical signal-to-noise estimates warrant the usage of CPM to separate phase-locked and non-phase-locked activity.

neuroscience↗

bioRxiv2022

Prediction error processing speeds up in presence of multisensory contexts

Prediction errors in the brain are indexed by two event-related potentials - MMN and P300, which are elicited upon violation of regularity in the occurrence of repetitive stimuli. While MMN reflects the brains ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection, P300 is associated with cognitive processes such as update in working memory. Till date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. However, the relationship between these two ERPs, specifically in terms of their underlying cortical generators, in context of prediction error propagation along the hierarchical brain across multiple modalities is an open question. Our objective in this article is two-fold. First, we reconfirm previous reports regarding the generators of MMN and P300 in sensor space through source-space analysis using an accurate individual subject level co-registration of MRI and EEG data collected from healthy humans. We demonstrate that in multisensory environments, MMN and P300 markers represent "modality-specific" and "modality-independent" information processing, respectively. Advancing an earlier understanding that multisensory contexts speed up early sensory processing, our study reveals that this temporal facilitation extends to even the later components of prediction error processing, using custom-designed experiments that allow comparisons across different modality combinations. Such knowledge can be of immense value in clinical research for determining the stages of various treatments in aging, schizophrenia and depression, and their efficacy on cognitive function.

neuroscience↗

bioRxiv2022

AI-assisted Discovery of an Ethnicity-influenced Driver of Cell Transformation in Esophageal and Gastroesophageal Junction Adenocarcinomas

Although Barretts metaplasia of the esophagus (BE) is the only known precursor lesion to esophageal adenocarcinomas (EACs), drivers of the metaplasia[->]dysplasia[->]neoplasia cascade in the esophagus remains incompletely understood. Using an AI-guided network transcriptomics approach, in which EAC initiation and progression is modeled as networks to simplify complex multi-cellular processes, we first predict cellular continuum states and disease driving processes with an unprecedented degree of precision. Key AI-guided predictions are subsequently validated in a human organoid model and patient-derived biopsies of BE, a case-control study of genomics of BE progression, and in a cross-sectional study of 113 patients with BE and EACs. We find that all EACs must originate from BE, pinpoint a CXCL8/IL8{leftrightarrow}neutrophil immune microenvironment as a driver of cellular transformation in both EACs and gastroesophageal junction-ACs. This driver is prominent in Caucasians (Cau), but notably absent in African Americans (AAs). Network-derived gene signatures, independent signatures of neutrophil processes, CXCL8/IL8, and an absolute neutrophil count (ANC) are associated with risk of progression. SNPs associated with ethnic changes in ANC modify that risk. Thus, findings define a racially influenced immunological basis for cell transformation and suggest that benign ethnic neutropenia in AAs may serve as a deterrent to BE[->]EAC progression. BRIEF SUMMARYEsophageal adenocarcinoma (EAC) is a highly lethal cancer among Caucasians, while African Americans are somewhat protected; what factors drive transformation with racial disparity remain unknown. AI-enabled creation of the first computational map of neoplastic progression in the esophagus built and validated using transcriptomic datasets from diverse cohorts of human samples pinpointed CXCL8{leftrightarrow}neutrophil tumor immune-microenvironment as a racially influenced driver of EACs and GEJ-ACs. Computational tools pinpoint a racially influenced driver of cell transformation during BE[->]EAC progression; in doing so, it reveals new novel biology, informs disease modeling, therapeutic strategies, and biomarkers. LAY SUMMARYBy modeling diseases as networks, this work unravels a fundamental race-influenced immunologic driver of cell transformation in adenocarcinomas of the esophagus and the gastroesophageal junction.

cancer biology↗