Biology subjects

Ghosh, P.

Publications and source records attributed to Ghosh, P..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

A reaction-diffusion model captures the essence of liquid-liquid phase separation

In this work we propose the formulation of a continuum model for liquid-liquid phase separation (LLPS) using reaction diffusion framework. We consider a well mixed liquid consisting of three phases, the dense droplet phase, the dilute phase and the remaining part to be solvent phase. As a key feature, the model includes both the spatial and temporal aspects and a free energy functional between dense and dilute phase that is physically motivated by reference molecular simulation. The exhaustive numerical simulations of model captures the dynamical formation of droplets and existence of LLPS. As the time progresses, simulation reveal that smaller droplets gradually vanish, and a single droplet undergoes continuous growth until it reaches a stable size. The model predicts that that extent of diffusivity of dense and dilute phase as well as their mutual interaction would modulate the kinetics of droplet formation. Finally we show that introduction of fluctuation in the model accelerate the phase separation process.

biophysics↗

bioRxiv2024

Comparative Analysis of Machine Learning Algorithms for Breast Cancer Classification: SVM Outperforms XGBoost, CNN, RNN, and Others

This study evaluates ten machine learning algorithms for classifying breast cancer cases as malignant or benign based on physical attributes. Algorithms tested include XGBoost, CNN, RNN, AdaBoost, Adaptive Decision Learner, fLSTM, GRU, Random Forest, SVM, and Logistic Regression. Using a robust dataset from UCI machine learning Breast Cancer, SVM emerged as the most accurate, achieving 98.2456% accuracy. While AdaBoost, Logistic Regression, Neural Networks, and Random Forest showed promise, none matched SVMs accuracy. These findings underscore the potential of machine learning, particularly SVMs, in cancer diagnosis and treatment by analyzing physical attributes for improved diagnostics and targeted therapies.

bioinformatics↗

bioRxiv2024

Conservation of C4BP-binding Sequence Patterns in Streptococcus pyogenes M and Enn Proteins

Antigenically sequence variable M proteins of the major bacterial pathogen Streptococcus pyogenes (Strep A) are responsible for recruiting human C4b-binding protein (C4BP) to the bacterial surface, which enables Strep A to evade destruction by the immune system. The most sequence divergent portion of M proteins, the hypervariable region (HVR), is responsible for binding C4BP. Structural evidence points to the conservation of two C4BP-binding sequence patterns (M2 and M22) in the HVR of numerous M proteins, with this conservation applicable to vaccine immunogen design. These two patterns, however, only partially explain C4BP-binding by Strep A. Here, we identified several M proteins that lack these patterns but still bind C4BP, and determined the structures of two, M68 and M87 HVRs, in complex with a C4BP fragment. Mutagenesis of these M proteins led to identification of amino acids that are crucial for C4BP-binding, enabling formulation of new C4BP-binding patterns. Mutagenesis was also carried out on M2 and M22 proteins to refine or generate experimentally grounded C4BP-binding patterns. The M22 pattern was the most populated among M proteins, followed by the M87 and M2 patterns, while the M68 pattern was rare. These patterns, except for M68, were also evident in numerous M-like Enn proteins. Binding of C4BP via these patterns to Enn proteins was verified. We conclude that C4BP-binding patterns occur frequently in Strep A strains of differing M types, being present in their M or Enn proteins, or frequently both, providing further impetus for their use as vaccine immunogens.

microbiology↗

bioRxiv2024

Rapid differentiation of estrogen receptor status in patient biopsy breast cancer aspirates with an optical nanosensor

Breast cancer is a substantial source of morbidity and mortality worldwide. It is particularly more difficult to treat at later stages, and treatment regimens depend heavily on both staging and the molecular subtype of the tumor. However, both detection and molecular analyses rely on standard imaging and histological method, which are costly, time-consuming, and lack necessary sensitivity/specificity. The estrogen receptor (ER) is, along with the progesterone receptor (PR) and human epidermal growth factor (HER-2), among the primary molecular markers which inform treatment. Patients who are negative for all three markers (triple negative breast cancer, TNBC), have fewer treatment options and a poorer prognosis. Therapeutics for ER+ patients are effective at preventing disease progression, though it is necessary to improve the speed of subtyping and distribution of rapid detection methods. In this work, we designed a near-infrared optical nanosensor using single-walled carbon nanotubes (SWCNT) as the transducer and an anti-ER antibody as the recognition element. The nanosensor was evaluated for its response to recombinant ER in buffer and serum prior to evaluation with ER- and ER+ immortal cell lines. We then used a minimal volume of just 10 {micro}L from 26 breast cancer biopsy samples which were aspirated to mimic fine needle aspirates. 20 samples were ER+, while 6 were ER-, representing 13 unique patients. We evaluated the potential of the nanosensor by investigating several SWCNT chiralities through direct incubation or fractionation deployment methods. We found that the nanosensor can differentiate ER-from ER+ patient biopsies through a shift in its center wavelength upon sample addition. This was true regardless of which of the three SWCNT chiralities we observed. Receiver operating characteristic area under the curve analyses determined that the strongest classifier with an AUC of 0.94 was the (7,5) chirality after direct incubation and measurement, and without further processing. We anticipate that further testing and development of this nanosensor may push its utility toward field-deployable, rapid ER subtyping with potential for additional molecular marker profiling.

bioengineering↗

bioRxiv2024

Physics-informed machine learning for automatic model reduction in chemical reaction networks

Physics-informed machine learning emerges as a transformative approach, bridging the gap between the high fidelity of mechanistic models and the adaptive, data-driven insights afforded by artificial intelligence and machine learning. In the realm of chemical reaction network modeling, this synergy is particularly valuable. It offers a solution to the pro-hibitive computational costs associated with detailed mechanistic models, while also capitalizing on the predictive power and flexibility of machine learning algorithms. This study exemplifies this innovative fusion by applying it to the critical biomedical challenge of A{beta} fibril aggregation, shedding light on the mechanisms underlying Alzheimers disease. A corner-stone of this research is the introduction of an automatic reaction order model reduction framework, tailored to optimize the scale of reduced order kinetic models. This framework is not merely a technical enhancement; it represents a paradigm shift in how models are constructed and refined. By automatically determining the most appropriate level of detail for modeling reaction networks, our proposed approach significantly enhances the efficiency and accuracy of simulations. This is particularly crucial for systems like A{beta} aggregation, where the precise characterization of nucleation and growth kinetics can provide insights into potential therapeutic targets. The potential generalizability of this automatic model reduction technique to other network models is a key highlight of this study. The methodology developed here has far-reaching implications, offering a scalable and adaptable tool for a wide range of applications beyond biomedical research. The ability to dynamically adjust model complexity in response to the specific demands of the system under study is a powerful asset. This flexibility ensures that the models remain both computationally feasible and scientifically relevant, capable of accommodating new data and evolving understandings of complex phenomena.

biochemistry↗

bioRxiv2024

CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state of the art clustering methods: CHAI-AvgSim and CHAI-SNF. Both methods demonstrate improved performance on a diverse selection of benchmarking datasets, besides also outperforming a previous consensus clustering method. We demonstrate CHAIs practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI is intuitive and easily customizable; it provides a way for users to add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai

bioinformatics↗

bioRxiv2024

COFFEE: CONSENSUS SINGLE CELL-TYPE SPECIFIC INFERENCE FOR GENE REGULATORY NETWORKS

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared to individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated and experimental datasets when compared to baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level.

bioinformatics↗

bioRxiv2023

Structural Requirements for Reverse Transcription by a Diversity-generating Retroelement

Diversity-generating retroelements (DGRs) create massive protein sequence variation in ecologically diverse microbes. Variation occurs during reverse transcription of a protein-encoding RNA template coupled to misincorporation at adenosines. In the prototypical Bordetella bacteriophage DGR, the template must be surrounded by upstream and downstream RNA segments for cDNA synthesis by the reverse transcriptase bRT and associated protein Avd. The function of the surrounding RNA was unknown. Cryo-EM revealed that this RNA enveloped bRT and lay over barrel-shaped Avd, forming an intimate ribonucleoprotein (RNP). An abundance of essential interactions between RNA structural elements and bRT-Avd precisely positioned an RNA homoduplex for initiation of cDNA synthesis by cis-priming. Our results explain how the surrounding RNA primes cDNA synthesis, promotes processivity, terminates polymerization, and strictly limits mutagenesis to select proteins through mechanisms that are likely conserved in DGRs from distant taxa.

microbiology↗

bioRxiv2023

Hindlimb Unloading Induces Bone Microarchitectural and Transcriptomic Changes in Murine Long Bones in an Age-Dependent Manner

Age and disuse-related bone loss both result in decreases in bone mineral density, cortical thickness, and trabecular thickness and connectivity. Disuse induces physiological changes in bone like those seen with aging. Bone microarchitecture and biomechanical properties were compared between 6- and 22-month-old C57BL/6J male control mice and 6-month-old mice that were hindlimb unloaded (HLU) for 3 weeks. Epiphyseal trabecular bone was the compartment most affected by HLU and demonstrated an intermediate bone phenotype between age-matched controls and aged controls. RNA extracted from whole-bone marrow-flushed tibiae was sequenced and analyzed. Differential gene expression analysis additionally included 4-month-old male mice unloaded for 3 weeks compared to age-matched controls. Gene ontology analysis demonstrated that there were age-dependent differences in differentially expressed genes. Genes related to downregulation of cellular processes were most affected in 4-month-old mice after disuse whereas those related to mitochondrial function were most affected in 6- month-old mice. Cell-cycle transition was downregulated with aging. A publicly available dataset (GSE169292) from 3-month female C57BL/6N mice unloaded for 7 days was included in ingenuity pathway analysis with the other datasets. IPA was used to identify the leading canonical pathways and upstream regulators in each HLU age group. IPA identified "Senescence Pathway" as the second leading canonical pathway enriched in mice exposed to HLU. HLU induced activation of the senescence pathway in 3- month and 4-month-old mice but inhibited it in 6-month-old mice. In conclusion, we demonstrate that hindlimb unloading and aging initiate similar changes in bone microarchitecture and gene expression. However, aging is responsible for more significant transcriptome and tissue-level changes compared to hindlimb unloading. HighlightsO_LIEpiphyseal trabecular bone is most susceptible to hindlimb unloading. C_LIO_LIHindlimb unloaded limbs resemble an intermediate phenotype between age-matched and aged controls. C_LIO_LIHindlimb unloading induces gene expression changes that are age dependent and may lead to inflammation and/or mitochondrial dysfunction depending on context. C_LIO_LIYounger mice (3-4 months) activate the senescence pathway upon hindlimb unloading, whereas skeletally mature (6 months) mice inhibit it. C_LI

bioengineering↗

bioRxiv2023

Reinstatement of CDX2 as a differentiation therapy for colorectal cancers

Despite advances in artificial intelligence (AI) within cancer research, its application toward realizing differentiation therapy in solid tumors remains limited. Using colorectal cancer (CRC) as a model, we developed a machine learning (ML) framework, CANDiT (Cancer Associated Nodes for Differentiation Targeting), to selectively induce differentiation and death of cancer stem cells (CSCs)--a key obstacle to durable response. Centering on one node, CDX2, a master differentiation factor lost in high-risk, poorly differentiated CRCs, we built a transcriptomic network to identify therapeutic strategies for CDX2 restoration. Network-based prioritization identified PRKAB1, a stress polarity sensor, as a top target. A clinical-grade PRKAB1 agonist reprogrammed transcriptional networks, induced crypt differentiation, and selectively eliminated CDX2-low CSCs in CRC cell lines, xenografts and patient-derived organoids (PDOs). Multivariate analyses in PDOs revealed a strong therapeutic index, linking efficacy (IC) to the biomarker-defined CDX2-low state. A 50-gene response signature--derived from an integrated analyses of all three models and trained across multiple datasets--revealed that CDX2 restoration therapy may translate into a [~]50% reduction in recurrence and mortality risk. Mechanistically, treatment activated a differentiation-associated stress polarity signaling axis while dismantling Wnt and YAP-driven stemness programs essential to CSC survival. Thus, CANDiT offers a scalable path to CSC-directed therapy in solid tumors by translating transcriptomic vulnerabilities into precision treatments. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=174 SRC="FIGDIR/small/557628v4_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@16eeb1eorg.highwire.dtl.DTLVardef@10e308borg.highwire.dtl.DTLVardef@9512f0org.highwire.dtl.DTLVardef@10e74eb_HPS_FORMAT_FIGEXP M_FIG C_FIG One sentence summaryIn this work, Sinha et al. introduce a machine learning-guided framework to identify and target transcriptomic vulnerabilities in colorectal cancer, demonstrating that differentiation therapy selectively eliminates cancer stem cells and reduces recurrence risk. HighlightsO_LIAn ML framework (CANDiT) identifies target for differentiation therapy for CRCs C_LIO_LITherapy induces crypt differentiation and CSC-specific cytotoxicity C_LIO_LICDX2-low state predicts therapeutic response; restoration improves prognosis C_LIO_LITherapy dismantles stemness via reactivation of stress polarity signaling C_LI

cancer biology↗

bioRxiv2023

Early spiral arteriole remodeling in the uterine-placental interface: a rat model

The mammalian placentas interface with the parent is a richly vascularized tissue whose development relies upon communication between many different cell types within the uterine microenvironment. The uterine blood vessels of the interface are reshaped during pregnancy into wide-bore, flaccid vessels that convey parental blood to the exchange region of the placenta. Fetally-derived invasive trophoblast as well as parental uterine macrophages and Natural Killer cells are involved in the stepwise remodeling of these vessels and their respective contributions to this crucial process are still being delineated. However, the earliest steps in arteriole remodeling are understudied as they are difficult to study in humans, and other species lack the deep trophoblast invasion that is so prominent a feature of placentation in humans. Here, we establish the rat, with deep hemochorial placentation akin to humans, as a model system in which to tease apart the earliest, relatively understudied events in spiral arteriole remodeling. We show that the rat uterine placental interface increases in size and vascularity rapidly, before trophoblast invasion. The remodeling stages in the arterioles of the rat uterine-placental interface follow a sequence of anatomical changes similar to those in humans, and there are changes to the arterioles muscular tunica media prior to the marked influx of immune cells. The rat is a tractable model in which to better understand the cell/cell interactions occurring in vivo in an intact tissue microenvironment over time.

developmental biology↗

bioRxiv2023

Spatiotemporal dynamics and emergent ordering in a mixture of morphologically distinct bacteria having different cell motility

Microbial communities exhibit complex behaviors driven by species interactions and individual characteristics. In this study, we delve into the dynamics of a mixed bacterial population comprising two distinct species with different morphology and motility aspects. Employing agent-based modeling and computer simulations, we analyze the impacts of size ratios and packing fractions on dispersal patterns, aggregate formation, clustering, and spatial ordering. Notably, we find that motility and anisotropy of elongated bacteria significantly influence the distribution and spatial organization of nonmotile spherical species. Passive spherical cells display superdiffusive behavior, particularly at smaller size ratios, while active rod-like cells exhibit normal diffusive behavior in the diffusion regime. As the size ratio increases, clustering of passive cells is observed, accompanied by enhanced alignment and closer packing of active cells in the presence of higher passive cell area fractions. As the size ratio increases, clustering of passive cells is observed, accompanied by enhanced alignment and closer packing of active cells in the presence of higher passive cell area fractions. Additionally, we identify the pivotal role of passive cell area fraction in influencing the response of active cells toward nematicity, with its dependence on size ratio. These findings shed light on the significance of morphology and motility in shaping the collective behavior of microbial communities, providing valuable insights into complex microbial behaviors with implications for ecology, biotechnology, and bioengineering.

biophysics↗

bioRxiv2023

Transcriptome profiling of type VI secretion system core gene tssM mutant of Xanthomonas perforans highlights regulators controlling diverse functions ranging from virulence to metabolism

Type VI secretion system (T6SS) is a versatile, contact dependent contractile nano-weapon in Gram-negative bacteria that fires proteinaceous effector molecules directly into prokaryotic and eukaryotic cells aiding in manipulation of the host and killing of competitors in complex niches. In plant pathogenic xanthomonads, T6SS has been demonstrated to play these diverse roles in individual pathosystems. However, the regulatory circuit involved in mediating biological functions carried out by T6SS are still elusive in Xanthomonas sp. To bridge this knowledge gap, we conducted an in vitro transcriptome screen using plant apoplast mimicking minimal medium, XVM2 medium, to decipher the effect of tssM deletion, a core gene belonging to T6SS-cluster i3*, on the regulation of gene expression in Xanthomonas perforans strain AL65. Transcriptomic data revealed that a total of 277 and 525 genes were upregulated, while 307 and 392 genes were downregulated in the mutant strain post 8 and 16 hours of growth in XVM2 medium. The transcript abundance of several genes associated with flagellum and pilus biogenesis as well as type III secretion system were downregulated in the mutant strain. Deletion of tssM of cluster-i3* resulted in upregulation of several T6SS genes belonging to cluster-i3*** and genes involved in biofilm and cell wall biogenesis. Similarly, transcription regulators like rpoN, Pho regulon, rpoE and csrA were identified to be upregulated in the mutant strain. Our results suggest that T6SS modulates the expression of global regulators like csrA, rpoN and pho regulons triggering a signaling cascade and co-ordinates the expression of suite of virulence factors, stress response genes and metabolic genes. ImportanceType VI secretion system (T6SS) has received attention due to its significance in mediating inter-organismal competition through contact-dependent release of effector molecules into prokaryotic and eukaryotic cells. Reverse-genetic studies targeting core genes of T6SS have indicated the role of T6SS in virulence in a variety of plant pathogenic bacteria, including Xanthomonas studied here. However, it is not clear whether such effect on virulence is merely because of a shift in the microbiome-mediated protection or if T6SS is involved in a complex regulatory network governing virulence in plant pathogens involving type III secretion system or c-di-GMP signaling pathways. In this study, we conducted in vitro transcriptome profiling in minimal medium to decipher the signaling pathways regulated by tssM-i3* in Xanthomonas perforans strain AL65. We show that TssM-i3* regulates the expression of a suite of genes associated with virulence and metabolism either directly or indirectly by altering the transcription of several regulators. These findings further expand our knowledge on the intricate molecular circuits regulated by T6SS in phytopathogenic bacteria.

microbiology↗

bioRxiv2023

Assessment of Nutrient Composition and utilization of some popular wild Edible fruits of Kumaun Himalayan Region as Anti-oxidant agents

Food and Nutrition security is the main problem faced by developing and underdeveloped countries. Proper utilization of wild edible plants for nutrition security is a great choice for reducing the wastage of these powerful foods. The present investigation has been made to understand the nutritional and phytochemical parameter of selected underutilized fruits such as Diospyros kaki, Pyrus pashia, Ficus semicordata, Diploknema butyracea, Pyracantha crenulata, and Rubus niveus. The proximate analyses showed that Diospyros kaki is most promising fruit with 44.86{+/-} 0.4 mg/g carbohydrate, 9.29{+/-}0.80 mg/g protein, 81.31{+/-}0.4, 7% moisture and 1.1{+/-}0.11% little amount of ash content. Micronutrient iron was quantify highest in Pyracantha crenulata (3.52{+/-}0.24mg/100g) and zinc in Rubus niveus(8.13{+/-}0.05 mg/100g). Phytochemical screening was recorded in the ethanolic and aqueous extract, in which Phenolic and flavonoid content were highest in ethanolic extract of Rubus niveus; 64.05{+/-}0.13mg GAE/gm, 108.83{+/-}2.93mg QE/ g extract respectively and tannin content was highest in case of Diospyros kaki (79.94{+/-}0.40mg TAE/100g of extract). The free radical scavenging activity of fruits have been analyzed by DPPH, H2O2 and NO assay; it was observed that the ethanolic extract of Rubus niveus fruit is most promising with an IC50value 16.97g/ml. In contrast, aqueous extract scored IC50value 28.86g/ml. The lowest IC50 value was found in aqueous extract of Diospyros kaki i.e., 81.9 g/ml in DPPH assay. The potential usage of these plants will help the people of these regions to combat nutrient deficiency diseases. Further development of research may help us to come out with powerful functional foods using these fruits. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/545978v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@11d5ddborg.highwire.dtl.DTLVardef@fce3cborg.highwire.dtl.DTLVardef@cf5f67org.highwire.dtl.DTLVardef@808054_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2023

Neuron Enriched Exosomal MicroRNA Expression Profiles as a Marker of Early Life Alcohol Consumption

BackgroundAlcohol consumption may impact and shape brain development through perturbed biological pathways and impaired molecular functions. We investigated the relationship between alcohol consumption rates and neuron-enriched exosomal microRNA (miRNA) expression to better understand the impact of alcohol use on early life brain biology. MethodsNeuron-enriched exosomal miRNA expression was measured from plasma samples collected from young people using a commercially available microarray platform while alcohol consumption was measured using the Alcohol Use Disorders Identification Test. Linear regression and network analyses were used to identify significantly differentially expressed miRNAs and to characterize the implicated biological pathways, respectively. ResultsCompared to alcohol naive controls, young people reporting high alcohol consumption exhibited significantly higher expression of four neuron-enriched exosomal miRNAs including miR-30a-5p, miR-194-5p, and miR-339-3p, although only miR-30a-5p and miR-194-5p survived multiple test correction. The miRNA-miRNA interaction network inferred by a network inference algorithm did not detect any differentially expressed miRNAs with a high cutoff on edge scores. However, when the cutoff of the algorithm was reduced, five miRNAs were identified as interacting with miR-194-5p and miR-30a-5p. These seven miRNAs were associated with 25 biological functions; miR-194-5p was the most highly connected node and was highly correlated with the other miRNAs in this cluster. ConclusionsOur observed association between neuron-enriched exosomal miRNAs and alcohol consumption concurs with results from experimental animal models of alcohol use and suggests that high rates of alcohol consumption during the adolescent/young adult years may impact brain functioning and development by modulating miRNA expression.

genetics↗

bioRxiv2023

Diverse Gut Pathogens Exploit the Host Engulfment Pathway via a Conserved Mechanism

Macrophages clear infections by engulfing and digesting pathogens within phagolysosomes. Pathogens escape this fate by engaging in a molecular arms race; they use WxxxE motif-containing "effector" proteins to subvert the host cells they invade and seek refuge within protective vacuoles. Here we define the host component of the molecular arms race as an evolutionarily conserved polar hotspot on the PH-domain of ELMO1 (Engulfment and Cell Motility1), which is targeted by diverse WxxxE-effectors. Using homology modeling and site-directed mutagenesis, we show that a lysine triad within the patch directly binds all WxxxE-effectors tested: SifA (Salmonella), IpgB1 and IpgB2 (Shigella), and Map (enteropathogenic E. coli). Using an integrated SifA*host protein-protein interaction (PPI) network, in-silico network perturbation, and functional studies we show that the major consequences of preventing SifA*ELMO1 interaction are reduced Rac1 activity and microbial invasion. That multiple effectors of diverse structure, function, and sequence bind the same hotpot on ELMO1 suggests that the WxxxE-effector(s)*ELMO1 interface is a convergence point of intrusion detection and/or host vulnerability. We conclude that the interface may represent the fault line in co-evolved molecular adaptations between pathogens and the host and its disruption may serve as a therapeutic strategy. GRAPHICAL ABSTRACTO_ST_ABSIn briefC_ST_ABSThis work defines the nature of a conserved molecular interface, assembled between diverse WxxxE motif-containing effector proteins encoded by gut pathogens and the host innate immune sensor, ELMO1, via which pathogens exploit the hosts engulfment machinery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/536168v4_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@21dbcorg.highwire.dtl.DTLVardef@ee27ccorg.highwire.dtl.DTLVardef@176f2c4org.highwire.dtl.DTLVardef@3c55e6_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

Multiomic Analysis of Intercellular Communication through Tumor-Stroma Tunnels in Breast Cancers

Estrogen receptor-positive (ER+) breast cancer commonly disseminates to bone marrow, where interactions with mesenchymal stromal cells (MSCs) shape disease trajectory. We modeled these interactions with tumor-MSC co-cultures and used an integrated transcriptome-proteome-network-analyses workflow to identify a comprehensive catalog of contact-induced changes. Conditioned media from MSCs failed to recapitulate genes and proteins, some borrowed and others tumor-intrinsic, induced in cancer cells by direct contact. Protein-protein interaction networks revealed the rich connectome between borrowed and intrinsic components. Bioinformatics prioritized one of the borrowed components, CCDC88A/GIV, a multi-modular metastasis-related protein that has recently been implicated in driving a hallmark of cancer, growth signaling autonomy. MSCs transferred GIV protein to ER+ breast cancer cells (that lack GIV) through tunnelling nanotubes via connexin (Cx)43-facilitated intercellular transport. Reinstating GIV alone in GIV-negative breast cancer cells reproduced [~]20% of both the borrowed and the intrinsic gene induction patterns from contact co-cultures; conferred resistance to anti-estrogen drugs; and enhanced tumor dissemination. Findings provide a multiomic insight into MSC[->]tumor cell intercellular transport and validate how transport of one such candidate, GIV, from the haves (MSCs) to have-nots (ER+ breast cancer) orchestrates aggressive disease states.

cancer biology↗

bioRxiv2023

Hierarchy and networks in the transcriptional response of Mycobacterium abscessus to antibiotics

Mycobacterium abscessus causes acute and chronic pulmonary infection in patients with chronic lung damage. It is intrinsically resistance to antibiotics effective against other pathogenic mycobacteria largely due to the drug-induced expression of genes that confer resistance. Induction of genes upon exposure to ribosome targeting antibiotics proceeds via WhiB7-dependent and -independent pathways. WhiB7 controls the expression of >100 genes, a few of which are known determinants of drug resistance. The function of the vast majority of genes within the regulon is unknown, but some conceivably encode additional mechanisms of resistance. Furthermore, the hierarchy of gene expression within the regulon, if any, is poorly understood. In the present work we have identified 56 WhiB7 binding sites using chromatin immunoprecipitation sequencing (CHIP-Seq) which accounts for the WhiB7-dependent upregulation of 70 genes, and find that M. abscessus WhiB7 functions exclusively as a transcriptional activator at promoters recognized by {sigma}A/{sigma}B We have investigated the role of 18 WhiB7 regulated genes in drug resistance and demonstrated the role of MAB_1409c and MAB_4324c in aminoglycoside resistance. Further, we identify a {sigma}H-dependent pathway in aminoglycoside and tigecycline resistance which is induced upon drug exposure and is further activated by WhiB7 demonstrating the existence of a crosstalk between components of the WhiB7-dependent and -independent circuits. Abstract ImportanceThe induction of multiple genes that confer resistance to structurally diverse ribosome-targeting antibiotics is funneled through the induction of a single transcriptional activator, WhiB7, by antibiotic-stalled ribosomes. This poses a severe restriction in M. abscessus therapy as treatment with one ribosome-targeting antibiotic confers resistance to all other ribosome-targeting antibiotics. Here we uncover the intricacies of the WhiB7 regulatory circuit, identify three previously unknown determinants of aminoglycoside resistance and unveil a communication between WhiB7 dependent and independent components. This not only expands our understanding of the antibiotic resistance potential of M. abscessus but can also inform the development of much needed therapeutic options.

microbiology↗