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Deshpande, R.

Publications and source records attributed to Deshpande, R..

16 recordsLinked to original sources

Biosynthetic Olfactory System from Multiplexed Engineered Microbes (BOSMEM)

Whole-cell biosensors can detect many types of chemicals in complex environments, but existing designs force a tradeoff in multiplexed sensing: separating sensor strains into individual reservoirs limits how many analytes can be monitored at once, while pooling strains in open co-culture lets faster-growing strains take over within days, corrupting the signal. The microfluidic "mother machine" has been used for over a decade, mostly to study individual bacterial strains with single-cell resolution, but never to house multiple engineered strains simultaneously for sensing applications. We repurpose this device to hold a library of genetically distinct biosensor strains, physically isolating each strain in its own growth channel while continuous flow keeps every strain fed. Each strain reports on a target chemical through a unique combination of three fluorescent proteins, so seven distinguishable signals emerge from only three optical channels. We characterized turn-on and turn-off kinetics of several biosensors in the mother machine. Over a multi-day experiment with repeated chemical exposures, biosensors housed this way matched the expected on/off state and held stable population ratios, while biosensors in an open batch co-culture drifted and lost reliable signal. This establishes mother-machine-based multiplexing as a scalable route to long-term chemical monitoring with living sensors.

bioengineering↗

Sperm whale foraging combines persistent search with flexible prey-capture tactics

Predators searching patchy environments must coordinate movements across scales, yet this behavioural hierarchy is not yet technically possible to observe in the deep ocean. Here we show that sperm-whale foraging is organized across two nested levels: directionally persistent search paths and flexible prey-capture tactics. We combine acoustic recordings with reconstructed three-dimensional trajectories from 34 sensor-tag deployments on 20 individual whales in both the eastern Caribbean and the mid-Atlantic. Complete foraging paths exhibit heavy-tailed step lengths and superdiffusive displacement, consistent with Levy-like search. Within these paths, echolocation buzzes resolve into two recurrent acoustic-kinematic tactics. Type A involves brief ramp-ups, slower and more oscillatory movement, and longer post-buzz pauses. Type B involves prolonged ramp-ups, faster and vertically persistent movement, greater roll, slightly straighter approaches, and an energy-model estimate approximately seven times higher. Every sampled whale used both tactics and switched between them within dives, showing that the contrast is not driven by specialization among individuals. An Azorean deployment with prey-field echograms links the tactics to local prey conditions, while independent Dominica deployments recover their core kinematic contrast. Together, these results reveal a foraging hierarchy in which flexible capture actions unfold within persistent, deliberate large-scale search.

animal behavior and cognition↗

Complementary Single-Cell Microflow HILIC and Ion Pair LC-MS Reveal Bystander Metabolic Effects in a Macrophage Model of Tuberculosis

Single-cell metabolomics remains analytically challenging due to the low abundance and chemical diversity of metabolites in individual cells. We have developed complementary microflow HILIC and ion pair LC-MS methods to expand metabolite coverage in single macrophages. Ion pair LC-MS was applied to single cells for the first time, enabling retention of highly polar and ionic metabolites that elute early under conventional reversed-phase conditions. Across Mycobacterium bovis BCG infected, uninfected bystander, and control unexposed THP-1 macrophages, both microflow methods detected significantly more features than a previously reported analytical-flow HILIC method. The two microflow methods provided complementary chemical space, together yielding 633 unique named metabolites with MS2 spectra. This depth enabled pathway-level interpretation at single-cell resolution, revealing infection-associated changes in purine-, arginine-, glutathione-, and one-carbon folate-associated metabolism. Metabolite-level interrogation indicated shared purine and amino acid changes in both infected and neighbouring macrophages, while revealing a distinct bystander phenotype characterised by elevated glycine and heterogeneous ATP levels. Finally, we demonstrate sequential IP and HILIC analysis of the same single cell, establishing a route toward maximal coverage from individual cells. These results position microflow HILIC and IP LC-MS as powerful, orthogonal strategies for advancing single-cell metabolomics and unveiling heterogeneity within complex biological microenvironments. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/733771v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@103ecfforg.highwire.dtl.DTLVardef@bc6734org.highwire.dtl.DTLVardef@11b96aforg.highwire.dtl.DTLVardef@41edfe_HPS_FORMAT_FIGEXP M_FIG Figure made in BioRender. C_FIG

microbiology↗

Nanoflow ion-pairing LC-MS for ultra-low-input polar metabolomics and isotope tracing

Low-input and single-cell metabolomics remain constrained by the poor retention of polar metabolites in conventional reversed-phase nanoflow LC-MS workflows. Here, we establish nanoflow tributylamine (TBA) ion-pairing LC-MS as a platform for ultra-low-input polar metabolomics and stable isotope tracing. By adapting an analytical-flow TBA ion-pairing method to the nanoflow scale, this workflow extends the sensitivity and inline concentration advantages of nanoflow chromatography to charged metabolites involved in central carbon metabolism. Using mouse liver metabolite extracts, we show that the nanoflow method preserves chromatographic retention and separation of chemically diverse metabolite classes, including adenine nucleotides, nucleotide cofactors, TCA cycle intermediates, acyl-CoAs, and bile acid isomers. Despite loading 20-fold less tissue-equivalent material on column, nanoflow LC-MS produced higher signal intensity than the analytical-flow method for many metabolites. Across representative compounds, the nanoflow workflow reduced the biomass required for detection by approximately 20- to >600-fold, with pronounced gains for low-abundance metabolites such as NADPH and acetyl-CoA. TBA ion-pairing also enabled trap-and-elute nanoflow analysis of retained polar metabolites from single-cell-equivalent inputs. ATP was detected from one cell equivalent using both full-scan and targeted parallel reaction monitoring acquisition, with targeted acquisition further increasing signal over blank. Finally, we applied the workflow to stable isotope tracing in uniformly labeled 13C-glucose-treated cells. 13C-labeled ATP isotopologues were detectable from single-cell-equivalent input, and targeted acquisition improved isotopologue measurement near the detection limit. Together, these results demonstrate that nanoflow TBA ion-pairing LC-MS enables retained, high-sensitivity analysis of polar metabolites from ultra-low inputs and provides a foundation for extending central carbon metabolite analysis and isotope tracing toward single-cell-scale applications.

biochemistry↗

MACHINE LEARNING FOR MISSING DATA IMPUTATION IN ALZHEIMER'S RESEARCH: PREDICTING MEDIAL TEMPORAL LOBE FLEXIBILITY

BACKGROUNDAlzheimers disease (AD) begins years before symptoms appear, making early detection essential. The medial temporal lobe (MTL) is one of the earliest regions affected, and its network flexibility, a dynamic measure of brain connectivity, may serve as a sensitive biomarker of early decline. Cognitive (acquisition, generalization), genetic (APOE, ABCA7), and biochemical (P-tau217) markers may predict MTL dynamic flexibility. Given the high rate of missing data in AD research, this study uses machine learning with advanced imputation methods to predict MTL dynamic flexibility from multimodal predictors in an aging cohort. METHODSIn an ongoing study at Rutgerss Aging and Brain Health Alliance, data from 656 participants are utilized, including cognitive assessments, genetic and blood-derived biomarkers, and demographics. Due to MRI-related constraints, only 34.15% of participants had measurable MTL dynamic flexibility from resting-state fMRI. To estimate MTL dynamic flexibility from available data, we evaluated four missing data handling methods (case deletion, MICE, MissForest, and GAIN), and trained five regression models: Ridge, k-NN, SVR, regression trees (bagging, random forest, boosting), and ANN. Hyperparameters were optimized via grid search with 3-fold cross-validation. Model performance was assessed using mean absolute error (MAE), root mean squared error (RMSE), and runtime through 5-fold cross-validation repeated 25 times to ensure robustness in clinical data settings. RESULTSA total of 1,866 missing values (25.86%) were identified in the dataset, with only 42 complete cases (6.40%) remaining after listwise deletion, highlighting the need for effective imputation. In the initial analysis using only complete cases, support vector regression (SVR) achieved the lowest mean absolute error (MAE = 0.184), though overall performance was limited due to small sample size. In the second phase, three imputation techniques were applied, significantly improving model accuracy. MissForest combined with Random Forest produced the best results (MAE = 0.083), representing a 54.7% improvement over case deletion. Statistical analysis confirmed significant differences in performance across imputation methods (p < 0.001), with MissForest outperforming GAIN and MICE. GAIN was the fastest imputation method. DISCUSSIONThe findings underscore the importance of using robust imputation strategies to maximize data utility and model reliability in studies with high missingness. Further research is needed, particularly incorporating additional neuroimaging measures, to localize the brain regions most affected by biomarker-driven changes and to refine predictive models for clinical applications.

neuroscience↗

Directed Evolution of Acoustic Reporter Genes Using High-Throughput Acoustic Screening

A major challenge in the fields of biological imaging and synthetic biology is noninvasively visualizing the functions of natural and engineered cells inside opaque samples such as living animals. One promising technology that addresses this limitation is ultrasound (US), with its penetration depth of several cm and spatial resolution on the order of 100 {micro}m.1 Within the past decade, reporter genes for US have been introduced2,3 and engineered4,5 to link cellular functions to US signals via heterologous expression in commensal bacteria and mammalian cells. These acoustic reporter genes (ARGs) represent a novel class of genetically encoded US contrast agent, and are based on air-filled protein nanostructures called gas vesicles (GVs).6 Just as the discovery of fluorescent proteins was followed by the improvement and diversification of their optical properties through directed evolution, here we describe the evolution of GVs as acoustic reporters. To accomplish this task, we establish high-throughput, semi-automated acoustic screening of ARGs in bacterial cultures and use it to screen mutant libraries for variants with increased nonlinear US scattering. Starting with scanning site saturation libraries for two homologs of the primary GV structural protein, GvpA/B, two rounds of evolution resulted in GV variants with 5- and 14-fold stronger acoustic signals than the parent proteins. We anticipate that this and similar approaches will help high-throughput protein engineering play as large a role in the development of acoustic biomolecules as it has for their fluorescent counterparts.

bioengineering↗

Enteric bacterial infection in Drosophila induces whole-body alterations in metabolic gene expression independently of the Immune Deficiency (Imd) signalling pathway.

When infected by intestinal pathogenic bacteria, animals initiate both local and systemic defence responses. These responses are required to reduce pathogen burden and also to alter host physiology and behaviour to promote infection tolerance, and they are often mediated through alterations in host gene expression. Here, we have used transcriptome profiling to examine gene expression changes induced by enteric infection with the gram-negative bacteria Pseudomonas entomophila (P.e) in adult female Drosophila. We find that infection induces a strong upregulation of metabolic gene expression, including gut and fat body-enriched genes involved in lipid transport, lipolysis, and beta-oxidation, as well as glucose and amino acid metabolism genes. Furthermore, we find that the classic innate immune deficiency (Imd)/Relish/NF-KappaB pathway is not required for, and in some cases limits, these infection-mediated increases in metabolic gene expression. We also see that enteric infection with P.e. down regulates the expression of many transcription factors and cell-cell signaling molecules, particularly those previously shown to be involved in gut-to-brain and neuronal signaling. Moreover, as with the metabolic genes, these changes occurred largely independent of the Imd pathway. Together, our study identifies many metabolic, signaling and transcription factor gene expression changes that may contribute to organismal physiological and behavioural responses to enteric pathogen infection.

physiology↗

Comparing host module activation patterns and temporal dynamics in infection by influenza H1N1 viruses

Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.

bioinformatics↗

Genomically Mined Acoustic Reporter Genes Enable On-Demand In Vivo Monitoring of Tumor-Homing Bacteria

A major outstanding challenge in the fields of biological research, synthetic biology and cell-based medicine is visualizing the function of natural and engineered cells noninvasively inside opaque organisms. Ultrasound imaging has the potential to address this challenge as a widely available technique with a tissue penetration of several centimeters and spatial resolution below 100 m. Recently, the first genetically encoded acoustic reporters were developed based on bacterial gas vesicles to link ultrasound signals to molecular and cellular function. However, the properties of these first-generation acoustic reporter genes (ARGs) resulted in limited sensitivity and specificity for imaging gene expression in vivo. Here, we describe second-generation ARGs with greatly improved acoustic properties and expression characteristics, identified through a phylogenetic screen of candidate gene clusters from diverse bacteria and archaea. The resulting constructs offer major qualitative and quantitative improvements, including much stronger ultrasound contrast, the ability to produce nonlinear signals distinguishable from background tissue, and stable long-term expression. We demonstrate the capabilities of these next-generation ARGs by imaging in situ gene expression in mouse models of breast cancer and tumor-homing therapeutic bacteria, noninvasively revealing the unique spatial distributions of tumor growth and colonization by therapeutic cells in living subjects and providing real-time guidance for interventions such as needle biopsies.

bioengineering↗

ATM and PRDM9 Regulate SPO11-bound Recombination Intermediates During Meiosis

Meiotic recombination is initiated by genome-wide SPO11-induced double-strand breaks (DSBs) that are processed by MRE11-mediated release of SPO11. The DSB is then resected and loaded with DMC1/RAD51 filaments that invade homologous chromosome templates. In most mammals, DSB locations ("hotspots") are determined by the DNA sequence specificity of PRDM9. Here, we demonstrate the first direct detection of meiotic DSBs and resection in vertebrates by performing END-seq on mouse spermatocytes using low sample input. We find that DMC1 limits both the minimum and maximum lengths of resected DNA, whereas 53BP1, BRCA1 and EXO1 play surprisingly minimal roles in meiotic resection. Through enzymatic modifications to the END-seq protocol that mimic the in vivo processing of SPO11, we identify a novel meiotic recombination intermediate ("SPO11-RI") present at all hotspots. The SPO11-bound intermediate is dependent on PRDM9 and caps the 3 resected end during engagement with the homologous template. We propose that SPO11-RI is generated because chromatin-bound PRDM9 asymmetrically blocks MRE11 from releasing SPO11. In Atm-/- spermatocytes, SPO11-RI is reduced while unresected DNA-bound SPO11 accumulate because of defective MRE11 initiation. Thus in addition to their global roles in governing SPO11 breakage, ATM and PRDM9 are critical local regulators of mammalian SPO11 processing.

molecular biology↗

DNA-PKcs promotes DNA end processing

The repair of DNA double-strand breaks occurs through non-homologous end joining or homologous recombination in vertebrate cells - a choice that is thought to be decided by a competition between DNA-dependent protein kinase (DNA-PK) and the Mre11/Rad50/Nbs1 (MRN) complex but is not well understood. Using ensemble biochemistry and single-molecule approaches, here we show that the MRN complex is dependent on DNA-PK and phosphorylated CtIP to perform efficient processing and resection of DNA ends in physiological conditions, thus eliminating the competition model. Endonucleolytic removal of DNA-PK-bound DNA ends is also observed at double-strand break sites in human cells. The involvement of DNA-PK in MRN-mediated end processing promotes an efficient and sequential transition from non-homologous end joining to homologous recombination by facilitating DNA-PK removal.\n\nOne Sentence SummaryDNA-dependent protein kinase, an enzyme critical for non-homologous repair of DNA double-strand breaks, also stimulates end processing for homologous recombination.

molecular biology↗

Efficient strategies for screening large-scale genetic interaction networks

Large-scale genetic interaction screening is a powerful approach for unbiased characterization of gene function and understanding systems-level cellular organization. While genome-wide screens are desirable as they provide the most comprehensive interaction profiles, they are resource and time-intensive and sometimes infeasible, depending on the species and experimental platform. For these scenarios, optimal methods for more efficient screening while still producing the maximal amount of information from the resulting profiles are of interest.\n\nTo address this problem, we developed an optimal algorithm, called COMPRESS-GI, which selects a small but informative set of genes that captures most of the functional information contained within genome-wide genetic interaction profiles. The utility of this algorithm is demonstrated through an application of the approach to define a diagnostic mutant set for large-scale chemical genetic screens, where more than 13,000 compound screens were achieved through the increased throughput enabled by the approach. COMPRESS-GI can be broadly applied for directing genetic interaction screens in other contexts, including in species with little or no prior genetic-interaction data.

systems biology↗

Large-scale interpretation of chemical-genetic interaction profiles using a genetic interaction network

Chemical-genetic interactions - observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes - contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a baseline enrichment approach across a variety of benchmarks, achieving similar accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. We applied CG-TARGET to a recent screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. Upon investigation of the compatibility of chemical-genetic and genetic interaction profiles, we observed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We present here a detailed characterization of the CG-TARGET method along with experimental validation of predicted biological process targets, focusing on inhibitors of tubulin polymerization and cell cycle progression. Our approach successfully demonstrates the use of genetic interaction networks in the functional annotation of compounds to biological processes.

systems biology↗

Systematic identification of pleiotropic genes from genetic interactions

Modular structures in biological networks are ubiquitous and well-described, yet this organization does not capture the complexity of genes individually influencing many modules. Pleiotropy, the phenomenon of a single genetic locus with multiple phenotypic effects, has previously been measured according to many definitions, which typically count phenotypes associated with genes. We take the perspective that, because genes work in complex and interconnected modules, pleiotropy can be treated as a network-derived characteristic. Here, we use the complete network of yeast genetic interactions (GI) to measure pleiotropy of nearly 2700 essential and nonessential genes. Our method uses frequent item set mining to discover GI modules, annotates them with high-level processes, and uses entropy to measure the functional spread of each genes set of containing modules. We classify genes whose modules indicate broad functional influence as having high pleiotropy, while genes with focused functional influence have low pleiotropy. These pleiotropy classes differed in a number of ways: high-pleiotropy genes have comparatively higher expression variance, higher protein abundance, more domains, and higher copy number, while low pleiotropy genes are more likely to be in protein complexes and have many curated phenotypes. We discuss the implications of these results regarding the nature and evolution of pleiotropy.

genomics↗

Functional Annotation of Chemical Libraries across Diverse Biological Processes

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compounds mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

systems biology↗

Ras/ERK-signalling promotes tRNA synthesis and growth via the RNA polymerase III repressor Maf1 in Drosophila

The small G-protein Ras is a conserved regulator of cell and tissue growth. These effects of Ras are mediated largely through activation of a canonical RAF-MEK-ERK kinase cascade. An important challenge is to identify how this Ras/ERK pathway alters cellular metabolism to drive growth. Here we report on stimulation of RNA polymerase III (Pol III)-mediated tRNA synthesis as a growth effector of Ras/ERK signalling in Drosophila. We find that activation of Ras/ERK signalling promotes tRNA synthesis both in vivo and in cultured Drosophila S2 cells. We also show that Pol III function is required for Ras/ERK signalling to drive proliferation in both epithelial and stem cells in Drosophila tissues. We find that the transcription factor Myc is required but not sufficient for Ras-mediated stimulation of tRNA synthesis. Instead we show that the main way that Ras promotes Pol III function and tRNA synthesis is by inhibiting the nuclear localization and function of the Pol III repressor Maf1. We propose that inhibition of Maf1 and stimulation of tRNA synthesis is one way by which Ras signalling enhances protein synthesis to promote cell and tissue growth.

developmental biology↗