Biology subjects

Chowdhury, S.

Publications and source records attributed to Chowdhury, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2021

An evolutionary divergent thermodynamic brake in ZAP-70 finetunes the kinetic proofreading in T cell

T cell signaling starts with assembling several tyrosine kinases and adaptor proteins to the T cell receptor (TCR), following the antigen binding. The lifetime of the TCR: antigen complex and the time delay between the recruitment and activation of each kinase determines the T cell response. The mechanism by which the time delays are implemented in TCR signaling is not fully understood. Combining experiments and kinetic modeling, we here report a thermodynamic-brake in the regulatory module of ZAP-70, which determines the ligand selectivity, and may delay the ZAP-70 activation in TCR. Phylogenetic analysis revealed that the evolution of the thermodynamic-brake coincides with the divergence of the adaptive immune system to the cell-mediated and humoral responses. Paralogous kinase Syk expressed in B cells, does not possess such a functional thermodynamic brake, which may explain higher basal activation and lack of ligand selectivity by Syk.

biochemistry↗

bioRxiv2021

The NK receptor NKp46 recognizes ecto-calreticulin on ER-stressed cells

Natural killer cells (NK) are a first line of immune defense to eliminate infected, transformed and stressed cells by releasing cytotoxic granules1. NK activation is controlled by the balance of signals transmitted by activating and inhibitory receptors but activating receptor engagement is required to trigger cytotoxicity. The activating receptor NKp46, encoded by the NCR1 gene, is expressed by virtually all NK cells and is the most evolutionarily ancient NK receptor. NKp46 plays a major role in NK recognition of cancer cells, since NKp46 blocking antibodies potently inhibit NK killing of many cancer targets2,3. Although a few viral, fungal and soluble host ligands4 have been identified, the endogenous cell-surface ligand of this important activating NK receptor is unknown. Here we show that NKp46 recognizes and is activated by the P-domain of externalized calreticulin (ecto-CRT). CRT, normally localized to the ER, translocates to the cell surface during ER stress and is a hallmark of chemotherapy-treated dying cancer cells that induce an immune response (immunogenic cell death, ICD)5. NKp46 caps with ecto-CRT in NK immune synapses formed with ecto-CRT-bearing target cells. ER stress, induced by ZIKV infection, ICD-causing chemotherapy drugs and some senescence activators, externalizes CRT and triggers NKp46 signaling. NKp46-mediated killing is inhibited by CRT knockout or knockdown or anti-CRT antibodies and is enhanced by ectopic expression of GPI-anchored CRT. NCR1/Ncr1-deficient human and mouse NK are impaired in killing ZIKV-infected, ER-stressed, and senescent cells and cancer cells that endogenously or ectopically express ecto-CRT. Importantly, NKp46 recognition of ecto-CRT controls the growth of B16 melanoma and RAS-driven lung cancer in mouse models and enhances tumor-infiltrating NK degranulation and cytokine secretion. Thus, ecto-CRT is a danger-associated molecular pattern (DAMP) that is an endogenous NKp46 ligand that promotes innate immune elimination of ER-stressed cells.

immunology↗

bioRxiv2021

Multiplexed triage of candidate biomarkers in plasma using internal standard triggered-parallel reaction monitoring mass spectrometry

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and downstream costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Here, we demonstrate the capability of internal standard triggered-parallel reaction monitoring (IS-PRM) to prioritize candidate biomarkers for validation studies. A 5,176-plex assay coupling immunodepletion and fractionation with IS-PRM was developed and implemented in human plasma to quantify peptides representing 1,314 breast cancer biomarker candidates. Compared to prior approaches using data-dependent analysis, IS-PRM showed improved sensitivity (912 vs 295 proteins quantified) and precision (CV 0.1 vs 0.27) enabling rank-ordering of candidate biomarkers for validation studies. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

cancer biology↗

bioRxiv2021

NBI-921352, a First-in-Class, Na V 1.6 Selective, Sodium Channel Inhibitor That Prevents Seizures in Scn8a Gain-of- Function Mice, and Wild-Type Mice and Rats

NBI-921352 (formerly XEN901) is a novel sodium channel inhibitor designed to specifically target NaV1.6 channels. Such a molecule provides a precision-medicine approach to target SCN8A-related epilepsy syndromes (SCN8A-RES), where gain-of-function (GoF) mutations lead to excess NaV1.6 sodium current, or other indications where NaV1.6 mediated hyper-excitability contributes to disease (Gardella and Moller, 2019; Johannesen et al., 2019; Veeramah et al., 2012). NBI-921352 is a potent inhibitor of NaV1.6 (IC50 0.051 {micro}M), with exquisite selectivity over other sodium channel isoforms (selectivity ratios of 756X for NaV1.1, 134X for NaV1.2, 276X for NaV1.7, and >583X for NaV1.3, NaV1.4, and NaV1.5). NBI-921352 is a state-dependent inhibitor, preferentially inhibiting activated (inactivated or open) channels. The state dependence leads to potent stabilization of inactivation, inhibiting NaV1.6 currents, including resurgent and persistent NaV1.6 currents, while sparing the closed/rested channels. The isoform-selective profile of NBI-921352 led to a robust inhibition of action-potential firing in glutamatergic excitatory pyramidal neurons, while sparing fast-spiking inhibitory interneurons, where NaV1.1 predominates. Oral administration of NBI-921352 prevented electrically induced seizures in a Scn8a GoF mouse, as well as in wild-type mouse and rat seizure models. NBI-921352 was effective in preventing seizures at lower brain and plasma concentrations than commonly prescribed sodium channel inhibitor antiseizure medicines (ASMs) carbamazepine, phenytoin, and lacosamide. NBI-921352 was well tolerated at higher multiples of the effective plasma and brain concentrations than those ASMs. NBI-921352 is entering phase II proof-of-concept trials for the treatment of SCN8A-developmental epileptic encephalopathy (SCN8A-DEE) and adult focal-onset seizures.

pharmacology and toxicology↗

bioRxiv2021

A programmable probiotic encapsulation system enhances therapeutic delivery in vivo

Recent advances in therapeutic modulation of human microbiota have driven new efforts to engineer living microbial medicines using synthetic biology. However, a long-standing challenge for live bacterial therapies is balancing the high dose required to achieve robust efficacy with the potential for sepsis. Here, we developed a genetically encoded microbial encapsulation system with tunable and dynamic expression of surface capsular polysaccharides to enhance therapeutic delivery. Following a synthetic small RNA knockdown screen of the capsular biosynthesis pathway, we constructed synthetic gene circuits that regulate bacterial encapsulation based on sensing the levels of environmental inducer, bacterial density, and blood pH. The induced encapsulation system enabled tunable immunogenicity and survivability of the probiotic Escherichia coli, resulting in increased maximum tolerated dose and enhanced efficacy in murine cancer models. Furthermore, triggering in situ encapsulation was found to increase microbial translocation between mouse tumors, leading to efficacy in distal tumors. The programmable encapsulation system demonstrates a new approach to control microbial therapeutic profiles in vivo using synthetic biology.

synthetic biology↗

bioRxiv2021

A systems-guided approach to discover the intracellular target of a novel evolution-drug lead

Elucidating intracellular drug targets has been a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we developed a systems-guided hierarchic workflow that utilizes metabolic and structural analysis to narrow in on specific targets suggested by statistical and machine learning analysis of metabolomics data. Utilizing a novel multi-valent DHFR-targeting antibiotic compound, CD15-3, as a case study, we first measured global metabolomics and applied statistics and machine learning to locate broad areas of metabolic perturbation under antibiotic stress. We then tested the ability of suggested compounds to rescue growth and performed metabolic modelling to identify pathways whose inhibition was consistent with growth rescue patterns. Next, we utilized protein structural similarity to further prioritize candidate drug targets within these pathways. Overexpression and in vitro activity assays of a top candidate target, HPPK (folK), showed complete recovery from drug induced growth inhibition and with microscopy. As interest in white-box machine learning methods continues to grow, this study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows.

systems biology↗

bioRxiv2021

Differential expression of single-cell RNA-seq data using Tweedie models

SO_SCPLOWUMMARYC_SCPLOWThe performance of computational methods and software to identify differentially expressed genes in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq data. Here, we propose to use generalized linear models with the Tweedie distribution that can flexibly capture a large dynamic range of observed scRNA-seq data across experimental platforms induced by heavy tails, sparsity, or different count distributions to model the technological variability in scRNA-seq expression profiles. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we performed a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R package) is available at https://github.com/himelmallick/Tweedieverse.

bioinformatics↗

bioRxiv2021

Cell type-specific meQTL extends melanoma GWAS annotation beyond eQTL and informs melanocyte gene regulatory mechanisms

While expression quantitative trait loci (eQTL) have been powerful in identifying susceptibility genes from genome-wide association studies (GWAS) findings, most trait-associated loci are not explained by eQTL alone. Alternative QTLs including DNA methylation QTL (meQTL) are emerging, but cell-type-specific meQTL using cells of disease origin has been lacking. Here we established an meQTL dataset using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization using meQTL, eQTL, and mRNA splice-junction QTL from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified susceptibility genes at 63% of melanoma GWAS loci. Among three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes are preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTL and melanoma meQTL identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTL identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIPseq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTL.

genetics↗

bioRxiv2021

Streptococcus pyogenes forms serotype and local environment-dependent inter-species protein complexes

Streptococcus pyogenes is known to cause both mucosal and systemic infections in humans. In this study, we used a combination of quantitative and structural mass spectrometry techniques to determine the composition and structure of the interaction network formed between human plasma proteins and the surface of different S. pyogenes serotypes. Quantitative network analysis revealed that S. pyogenes form serotype-specific interaction networks that are highly dependent on the domain arrangement of the surface-attached M protein. Subsequent structural mass spectrometry analysis and computational modelling on one of the M proteins, M28 revealed that the network structure changes across different host microenvironments. We report that M28 binds secretory IgA via two separate binding sites with high affinity in saliva. During vascular leakage mimicked by increasing plasma concentrations in saliva, the binding of secretory IgA was replaced by binding of monomeric IgA and C4BP. This indicates that an upsurge of C4BP in the local microenvironment due to damage of the mucosal membrane drives binding of C4BP and monomeric IgA to M28. The results suggest that S. pyogenes has evolved to form microenvironment-dependent host-pathogen protein complexes to combat the human immune surveillance during both mucosal and systemic infections.

microbiology↗

bioRxiv2020

A native chemical chaperone in the human eye lens

Cataract is one of the most prevalent protein aggregation disorders and still the most common cause of vision loss worldwide. The metabolically quiescent core region of the human lens lacks cellular or protein turnover; it has therefore evolved remarkable mechanisms to resist light-scattering protein aggregation for a lifetime. We now report that one such mechanism involves an unusually abundant lens metabolite, myo-inositol, suppressing aggregation of lens crystallins. We quantified aggregation suppression using our previously well-characterized in vitro aggregation assays of oxidation-mimicking human {gamma}D-crystallin variants and investigated myo-inositols molecular mechanism of action using solution NMR, negative-stain TEM, differential scanning fluorometry, thermal scanning Raman spectroscopy, turbidimetry in redox buffers, and free thiol quantitation. Unlike many known chemical chaperones, myo-inositols primary target was neither the native nor the unfolded state of the protein, nor the final aggregated state, but rather the rate-limiting bimolecular step on the aggregation pathway. Given recent metabolomic evidence that it is severely depleted in human cataractous lenses compared to age-matched controls, we suggest that maintaining or restoring healthy levels of myo-inositol in the lens may be a simple, safe, and globally accessible strategy to prevent or delay lens opacification due to age-onset cataract.

biophysics↗

bioRxiv2020

Structural basis for piRNA-targeting

Piwi proteins use PIWI-interacting RNAs (piRNAs) to identify and silence the transposable elements (TEs) pervasively found in animal genomes. The Piwi targeting mechanism is proposed to be similar to targeting by Argonaute proteins, which employ microRNA (miRNA) guides to repress cellular mRNAs, but has not been characterized in detail. We present cryo-EM structures of a Piwi-piRNA complex with and without target RNAs and analysis of target recognition. Resembling Argonaute, Piwi identifies targets using the piRNA seed-region. However, Piwi creates a much weaker seed so that prolonged target association requires further piRNA-target pairing. Beyond the seed, Piwi creates wide central cleft wide for unencumbered piRNA-target pairing, enabling long-lived Piwi-piRNA-target interactions that are tolerant of mismatches. Piwi ensures targeting fidelity by blocking propagation of the piRNA-target duplex in the absence of faithful seed pairing, and by requiring extended piRNA-target pairing to reach an endonucleolytically active conformation. This mechanism allows Piwi to minimize off-targeting cellular mRNAs and adapt piRNA sequences to evolving genomic threats.

molecular biology↗

bioRxiv2020

Development of antibacterial compounds that block evolutionary pathways to resistance

Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC50 50-75 {micro}M against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3 resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC50) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which block evolutionary escape routes in pathogens.

microbiology↗

bioRxiv2020

DAGBagM: Learning directed acyclic graphs of mixed variables with an application to identify prognostic protein biomarkers in ovarian cancer

MotivationDirected gene/protein regulatory networks inferred by applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of clinical outcomes. However, there remain unsolved challenges in DAG learning to jointly model clinical outcome variables, which often take binary values, and biomarker measurements, which usually are continuous variables. Therefore, in this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models for continuous and binary variables, DAGBagM allows for either type of nodes to be parents or children nodes in the learned graph. DAGBagM also employs a bootstrap aggregating strategy to reduce false positives and achieve better estimation accuracy. Moreover, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges for DAG reconstruction. ResultsAs shown by simulation studies, DAGBagM performs better in identifying edges between continuous and binary nodes, as compared to commonly used strategies of either treating binary variables as continuous or discretizing continuous variables. Moreover, DAGBagM outperforms several popular DAG structure learning algorithms including the score-based hill climbing (HC) algorithm, constraint-based PC-algorithm (PC-alg), and the hybrid method max-min hill climbing (MMHC) even for constructing DAG with only continuous nodes. The HC implementation in the R package DAGBagM is much faster than that in a widely used DAG learning R package bnlearn. When applying DAGBagM to proteomics datasets from ovarian cancer studies, we identify potential prognostic protein biomarkers in ovarian cancer. Availability and implementationDAGBagM is made available as a github repository https://github.com/jie108/dagbagM.

systems biology↗

bioRxiv2020

The metal cofactor zinc and interacting membranes modulate SOD1 conformation-aggregation landscape in an in vitro ALS Model

Aggregation of Cu-Zn superoxide dismutase (SOD1) is implicated in the motor neuron disease, ALS. Although more than 140 disease mutations of SOD1 are available, their stability or aggregation behaviors in membrane environment are not correlated with disease pathophysiology. Here, we use multiple mutational variants of SOD1 to show that the absence of Zn, and not Cu, significantly impacts membrane attachment of SOD1 through two loop regions facilitating aggregation driven by lipid induced conformational changes. These loop regions influence both the primary (through Cu intake) and the gain of function (through aggregation) of SOD1 presumably through a shared conformational landscape. Combining experimental and theoretical frameworks using representative ALS disease mutants, we develop a co-factor derived membrane association model wherein mutational stress closer to the Zn (but not to the Cu) pocket is responsible for membrane association mediated toxic aggregation and survival time scale after ALS diagnosis.

biophysics↗

bioRxiv2020

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗

bioRxiv2020

Docosahexaenoic acid (DHA), a nutritional supplement, modulates steroid insensitivity in asthma

Asthmatics with poor steroid responsiveness are now found to use health services at higher frequency and contribute to socio-economic burden disproportionately. We have previously shown that a ω-6 fatty acid metabolite leads to a severe and steroid insensitive asthma-like condition in mice. Here, we investigated the role of retinoid-x-receptor gamma (RXRγ) and Docosahexaenoic acid (DHA), a ω3 fatty acid rexinoid ligand of RXR, on the features of steroid insensitivity in asthmatic condition. RXRγ was found to be reduced in the lungs of human asthmatics and mice with steroid insensitive allergic airway inflammation. RXRγ knockdown in naïve mice led to spontaneous asthma like features whereas RXRγ knockdown in allergic mice led to steroid insensitive asthma features. We observed while RXRγ binds to the glucocorticoid receptor (GR) gene and regulates its transcription, DHA increases the GRα expression in human bronchial epithelial cells and reverses the steroid insensitive features in mice with allergic airway inflammation. Docosahexaenoic acid (DHA), a ligand of RXR, was reduced in the sera of steroid-insensitive asthmatics. We conclude that DHA may prove to be a promising steroid sensitizing agent for the treatment of steroid insensitive asthmatics.Summary The molecular regulation of glucocorticoid receptor by retinoid-x-receptor gamma (RXRgama) has an implication in steroid insensitive asthma as we found that Docosahexaenoic acid (DHA), a nutritional supplement and natural ligand of RXRgamma, improves steroid sensitivity in steroid insensitive mice model of asthma and DHA levels are found to be low in steroid insensitive asthmatic patients.Competing Interest StatementThe authors have declared no competing interest.Abbreviations usedAAIAllergic airway inflammationAHRAirway hyper-responsivenessATRAAll-trans retinoic acidAUArbitrary unitsBALBronchoalveolar lavageBEAS-2BHuman bronchial epithelial cellsCECockroach allergen extractChIPChromatin Immunoprecipitation9CRA9-cis retinoic acidDEXDexamethasoneDHADocosahexaenoic acidDMSODimethyl sulfoxideELISAEnzyme-linked immunosorbent assayGAPDHGlyceraldehyde 3-phosphate dehydrogenaseGLucGaussia luciferaseGRαGlucocorticoid receptor alphaGREGlucocorticoid receptor elementH & EHaematoxylin and eosinIFNγInterferon gammaIgEImmunoglobulinE ILInterleukinKCKeratinocyte chemoattractant15-LOX15-lipoxygenaseMCF-7Human breast adenocarcinoma cell lineMCP1-αMonocyte chemoattractant protein1-αNF-κBNuclear Factor kappa-light-chain-enhancer of activated B cellsO.EOverexpressionOVAOvalbuminp38-MAPKp38 mitogen-activated protein kinasePBSPhosphate buffered salineRARRetinoic acid receptorPDTCPyrrolidinedithiocarbamateRXRERexinoid receptor elementRXRγRetinoid-x-receptor gamma13-S-HODE13-hydroxyoctadecadienoic acidSEMStandard error meansiRNASmall interfering RNASTAT-6Signal transducer and activator of transcription 6ThT helperVEHVehicleView Full Text

immunology↗

bioRxiv2020

An exploration of the SARS-CoV-2 spike receptor binding domain (RBD), a complex palette of evolutionary and structural features

SARS-CoV-2 spike protein (S) is associated with the entry of virus inside the host cell by recruiting its loop dominant receptor binding domain (RBD) and interacting with the host ACE2 receptor. Our study deploying a two-tier approach encompassing evolutionary and structural analysis provides a comprehensive picture of the RBD, which could be of potential use for better understanding the RBD and address its druggability issues. Resorting to an ensemble of sequence space exploratory tools including co-evolutionary analysis and deep mutational scans we provide a quantitative insight into the evolutionarily constrained subspace of the RBD sequence space. Guided by structure network analysis and Monte Carlo simulation we highlight regions inside the RBD, which are critical for providing structural integrity and conformational flexibility of the binding cleft. We further deployed fuzzy C-means clustering by plugging the evolutionary and structural features of discrete structure blocks of RBD to understand which structure blocks share maximum overlap based on their evolutionary and structural features. Deploying this multi-tier interlinked approach, which essentially distilled the evolutionary and structural features of RBD, we highlight discrete region, which could be a potential druggable pocket thereby destabilizing the structure and addressing evolutionary routes.

biophysics↗

bioRxiv2020

The effect of plant-derived carbon on rhizospheric microbial communities decomposing different litter types and nutrient return

Interactions between plants and microorganisms strongly affect ecosystem functioning as processes of plant productivity, litter decomposition and nutrient cycling are controlled by both organisms. Though two-sided interactions between plants and microorganisms and between microorganisms and litter decomposition are areas of major scientific research, our understanding of the three-sided interactions of plant-derived carbon flow into the soil microbial community and their follow-on effects on ecosystem processes like litter decomposition and plant nutrient uptake remains limited. Therefore, we performed a greenhouse experiment with two plant communities differing in their ability to associate with arbuscular mycorrhizal fungi (AMF). By applying a 13CO2 pulse label to the plant communities and adding various 15N labelled substrate types to ingrowth cores, we simultaneously traced the flow of plant-derived carbon into soil microbial communities and the return of mineralized nitrogen back to the plant communities. We observed that net 13C assimilation by the rhizosphere microbial communities and their community composition not only depended on plant-AMF association but also type of substrate being decomposed. AMF-association resulted in lower net 13C investment into the decomposer community than absence of the association for similar 15N uptake. This effect was driven by a reduced carbon flow to fungal and bacterial saprotrophs and a simultaneous increase of carbon flow to AMF. Additionally, in presence of AMF association CN flux also depended on the type of substrate being decomposed. Lower net 13C assimilation was observed for decomposition of plant-derived and microorganism-derived substrates whereas opposite was true for inorganic nitrogen. Interestingly, the decomposer communities assembled in the rhizosphere were structured by both the plant community and substrate amendments which suggests existence of functional overlap between the two soil contexts. Moreover, we present preliminary evidence that AMF association helps plants access nutrients that are locked in bacterial and plant necromass at a lower carbon cost. Therefore, we conclude that a better understanding of ecosystem processes like decomposition can only be achieved when the whole plant-microorganism-litter context is investigated.

microbiology↗