Biology subjects

Mani, D. R.

Publications and source records attributed to Mani, D. R..

At least 19 recordsLinked to original sources

bioRxiv2026

Background proteome correction promotes confident identification of dynamic protein-protein interactions between different biological contexts

Affinity purification-mass spectrometry (AP-MS) enables the characterization of protein-protein interactions (PPIs), and the ease and sensitivity of such experiments has progressively increased. Beyond steady-state interactions of target proteins, a strong interest has emerged in monitoring how PPIs change upon significant biological perturbations, such as in disease contexts or small molecule modulation of the target protein. These perturbations likely not only induce PPI changes but can also lead to altered expression of proteins not of direct interest. Changes in protein abundance may alter which proteins adsorb to the affinity purification matrix, and due to the sensitivity of modern mass spectrometers, these differential ''background binders'' can masquerade as differential interactors. Contemporary approaches often do not account for differences in the background proteome, potentially inflating the number of false positives and negatives reported. Here, we provide technical considerations for the reliable annotation of dynamic PPIs, using the O-GlcNAc transferase (OGT) as a case study. We describe the installation of affinity epitope tags on endogenous OGT in mouse embryonic stem cells (mESCs), which we then apply for OGT interactor identification via AP-MS. We show that accurate representation of the bead background, which depends on the affinity matrix in use, is critical for elimination of false positive and false negative PPIs. This became even more pertinent as OGT PPI dynamics were measured under OGT catalytic inhibition via OSMI-4, which is known to perturb gene expression. The proteomes of OSMI-4-treated and control-treated mESCs differed, leading to distinct bead backgrounds in which the differential background proteins appeared as interaction gains or losses. These false positives were resolved by incorporating straightforward experimental controls through a practical statistical framework, allowing for a direct and confident comparison between treatment conditions. Incorporating these considerations into workflows investigating PPI dynamics will improve data fidelity and reproducibility.

biochemistry↗

bioRxiv2026

Integrative Multi-omics Analysis of the Human Skeletal Muscle Response to Endurance or Resistance Exercise: Findings from the Molecular Transducers of Physical Activity Consortium (MoTrPAC)

The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to systematically characterize the molecular basis of the health benefits of exercise. Here, we present the integrative, multi-omics response of human skeletal muscle to acute endurance (EE) and resistance (RE) exercise. Distinct temporal responses were observed, with changes in ATAC-seq, phosphoproteome, and metabolome occurring before changes in the transcriptome and proteome. These distinct temporal multi-omic dynamics were used to identify transcriptional regulatory hubs converging around MEF2A and NFIC regulation of autophagy, angiogenesis and metabolism. Further, early RE-specific phosphoproteome signatures counteracted epigenetic modifications and downregulated transcripts involved in protein turnover. Additional findings include suppression of HIPK2/3 kinase signatures linked to the acute exercise regulation of sarcomeric proteins TTN, NEB, ANKRD2 and LMOD2. Our data demonstrate distinct temporal regulation across the multi-omic landscape of human skeletal muscle, with EE and RE eliciting common and unique molecular signatures.

systems biology↗

bioRxiv2026

Multi-Omic, Multi-Tissue Responses to Acute Exercise in Sedentary Adults: Findings from the Molecular Transducers of Physical Activity Consortium

Regular physical activity represents one of the greatest mechanisms for maintaining human health, yet the underlying molecular transducers of these benefits remain incompletely understood. Multi-omic assays now provide new opportunities to study the coordinated molecular responses of body tissues to different exercise modalities. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this need by creating a molecular map of the response to physical activity. Described here is the first human cohort of MoTrPAC: sedentary adults enrolled prior to study suspension during the COVID-19 pandemic (N=175) randomized to either endurance or resistance exercise, or non-exercise control. From these participants, we detail their global acute molecular response in skeletal muscle, adipose tissue, and blood, integrated at multiple levels: tissue, exercise modality, timepoint, and omic category. These analyses characterize key molecular pathways, identify central regulators, and implicate novel candidate exerkines in mediating multi-organ exercise effects.

systems biology↗

bioRxiv2025

In-situ glial cell-surface proteomics identifies pro-longevity factors in Drosophila

Much focus has shifted towards understanding how glial dysfunction contributes to age-related neurodegeneration due to the critical roles glial cells play in maintaining healthy brain function. Cell-cell interactions, which are largely mediated by cell-surface proteins, control many critical aspects of development and physiology; as such, dysregulation of glial cell-surface proteins in particular is hypothesized to play an important role in age-related neurodegeneration. However, it remains technically difficult to profile glial cell-surface proteins in intact brains. Here, we applied a cell-surface proteomic profiling method to glial cells from intact brains in Drosophila, which enabled us to fully profile cell-surface proteomes in-situ, preserving native cell-cell interactions that would otherwise be omitted using traditional proteomics methods. Applying this platform to young and old flies, we investigated how glial cell-surface proteomes change during aging. We identified candidate genes predicted to be involved in brain aging, including several associated with neural development and synapse wiring molecules not previously thought to be particularly active in glia. Through a functional genetic screen, we identified one surface protein, DIP-{beta}, which is down-regulated in old flies and can increase fly lifespan when overexpressed in adult glial cells. We further performed whole-head single-nucleus RNA-seq and revealed that DIP-{beta} overexpression mainly impacts glial and fat cells. We also found that glial DIP-{beta} overexpression was associated with improved cell-cell communication, which may contribute to the observed lifespan extension. Our study is the first to apply in-situ cell-surface proteomics to glial cells in Drosophila, and to identify DIP-{beta} as a potential glial regulator of brain aging.

neuroscience↗

bioRxiv2025

Gliomas phenocopy an inborn error of metabolism to drive neuronal activity and tumor growth

The metabolic hallmarks of high-grade glioma (HGG) are not fully understood. Human brain tissue metabolomics revealed that the creatine synthesis pathway intermediate guanidinoacetate (GAA) accumulated [~]100-fold in HGGs relative to controls, which was caused by imbalanced activities of enzymes in this pathway. Glioma cells secreted GAA rather than using it to produce creatine, implicating an unexpected function. GAA accumulates in GAMT deficiency, an inborn error of metabolism, and elevates neuronal excitability. Neuronal excitability is also increased in glioma and drives tumor growth through neuron-glioma interactions. We hypothesized that glioma-generated GAA excites surrounding neurons. Indeed, GAA induced neuronal hyperactivity by activating GABAA receptors and causing depolarizing GABA currents in glioma-associated neurons with dysregulated chloride homeostasis. Depleting tumoral GAA decreased electrochemical activity, neuron-glioma interactions, and tumor aggressiveness. Our findings unveil a new mechanism linking cancer metabolism with cancer neuroscience and leverage human genetics to nominate GAA synthesis as a target in gliomas.

cancer biology↗

bioRxiv2025

Discovery of chromatin-based determinants of azacytidine and decitabine anti-cancer activity

The DNA-incorporating nucleoside analogs azacytidine (AZA) and decitabine (DEC) have clinical efficacy in blood cancers, yet the precise mechanism by which these agents kill cancer cells has remained unresolved - specifically, whether their anti-tumor activity arises from conventional DNA damage or DNA hypomethylation via DNA methyltransferase 1 (DNMT1) inhibition. This incomplete mechanistic understanding has limited their broader therapeutic application, particularly in solid tumors, where early clinical trials showed limited efficacy. Here, through the assessment of drug sensitivity in over 600 human cancer models and comparison to a non-DNA-damaging DNMT1 inhibitor (GSK-3685032), we establish DNA hypomethylation, rather than DNA damage, as the primary killing mechanism of AZA and DEC across diverse cancer types. In further support of an epigenetic killing mechanism, CRISPR drug modifier screens identified a core set of chromatin regulators, most notably the histone deubiquitinase USP48, as AZA and DEC protective factors. We show that USP48 is recruited to newly hypomethylated CpG islands and deubiquitinates non-canonical histones, establishing USP48 as a key molecular link between the two components of epigenetic gene regulation: DNA methylation and chromatin modification. Furthermore, loss of USP48, which occurs naturally through biallelic deletions in human cancers, sensitized both hematologic and solid tumors to DNMT1 inhibition in vitro and in vivo. Our findings elucidate the epigenetic mechanism of action of AZA and DEC and identify a homeostatic link between DNA methylation and chromatin state, revealing new therapeutic opportunities for DNMT1 inhibitors in solid tumors.

cancer biology↗

bioRxiv2024

High-throughput proteomic and phosphoproteomic analysis of formalin-fixed paraffin-embedded tissue

Formalin-fixed, paraffin-embedded (FFPE) patient tissues are a valuable resource for proteomic studies with the potential to associate the derived molecular insights with clinical annotations and outcomes. Here we present an optimized, partially automated workflow for FFPE proteomics combining pathology-guided macro-dissection, Adaptive Focused Acoustics (AFA) sonication for lysis and decrosslinking, S-Trap digestion to peptides, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis using Orbitrap, Astral or timsTOF HT instrumentation. The workflow enables analysis of up to 96 dissected FFPE tissue samples or 10 {micro}m scrolls, identifying 8,000-10,000 unique proteins per sample with median CVs <20%. Key optimizations include improved tissue lysis strategies, protein quantification for normalization, and peptide cleanup prior to LC-MS/MS analysis. Application to lung adenocarcinoma (LUAD) FFPE blocks confirms the platforms effectiveness in processing complex, clinically relevant samples, achieving deep proteome coverage and quantitative robustness comparable to TMT-based methods. Using the newly released Orbitrap Astral with short, 24-minute gradients, the workflow identifies up to [~]10,000 unique proteins and [~]11,000 localized phosphosites in LUAD FFPE tissue. This high-throughput, scalable workflow advances biomarker discovery and proteomic research in archival tissue samples.

systems biology↗

bioRxiv2024

Elevated synaptic PKA activity and abnormal striatal dopamine signaling in Akap11 mutant mice, a genetic model of schizophrenia and bipolar disorder

AbstractLoss-of-function mutations in AKAP11 (a protein kinase A (PKA)-binding protein) greatly increase the risk of bipolar disorder and schizophrenia. To determine the neurobiological functions of AKAP11, we conduct multi-omic and neurobiological analyses of Akap11 mutant mouse brains. We find that AKAP11 is a key regulator of PKA proteostasis in the brain whose loss leads to dramatically increased levels of PKA subunits and phosphorylated PKA substrates, especially in synapses. Akap11 mutant mice show extensive transcriptomic changes throughout the brain, including prominent decreases in synapse-related genes sets. Gene expression is highly impacted in spiny projection neurons of the striatum, a brain region implicated in motivation, cognition and psychotic disorders. Real-time measurements of PKA activity reveal elevated basal PKA activity in the striatum of Akap11-/- mice, with exaggerated additional response to dopamine receptor antagonists. Behaviorally, Akap11 mutant mice show abnormally prolonged locomotor response to amphetamine, deficits in associative learning and contextual discrimination, as well as depression-like behaviors. Our study connects molecular changes to circuit dysfunction and behavioral disturbance in a genetically valid animal model of psychotic disorder.

neuroscience↗

bioRxiv2024

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Kinases play a central role in regulating cellular processes, making their study essential for understanding cellular function and disease mechanisms. To investigate the regulatory state of a kinase, numerous methods have been, and continue to be, developed to infer kinase activities from phosphoproteomics data. These methods usually rely on a set of kinase targets collected from various kinase-substrate libraries. However, only a small percentage of measured phosphorylation sites can usually be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. In addition, the inferred activities from different methods can vary making it crucial to evaluate them for accurate interpretation. Here, we present a comprehensive evaluation of kinase activity inference methods using multiple kinase-substrate libraries combined with different inference algorithms. Additionally, we try to overcome the coverage limitations for measured targets in kinase substrate libraries by adding predicted kinase-substrate interactions for activity inference. For the evaluation, in addition to classical cell-based perturbation experiments, we introduce a tumor-based benchmarking approach that utilizes multi-omics data to identify highly active or inactive kinases per tumor type. We show that while most computational algorithms perform comparably regardless of their complexity, the choice of kinase-substrate library can highly impact the inferred kinase activities. Hereby, manually curated libraries, particularly PhosphoSitePlus, demonstrate superior performance in recapitulating kinase activities from phosphoproteomics data. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance, while normalizing sites to host protein levels reduces kinase activity inference performance. We then showcase how kinase activity inference can help in characterizing the response to kinase inhibitors in different cell lines. Overall, the selection of reliable kinase activity inference methods is important in identifying deregulated kinases and novel drug targets. Finally, to facilitate the evaluation of novel methods in the future, we provide both benchmarking approaches in the R package benchmarKIN. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/601117v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@44de1org.highwire.dtl.DTLVardef@9510a3org.highwire.dtl.DTLVardef@775225org.highwire.dtl.DTLVardef@1b2256f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2024

Molecular and Cellular Mechanisms of Teneurin Signaling in Synaptic Partner Matching

In developing brains, axons exhibit remarkable precision in selecting synaptic partners among many non-partner cells. Evolutionally conserved teneurins were the first identified transmembrane proteins that instruct synaptic partner matching. However, how intracellular signaling pathways execute teneurins functions is unclear. Here, we use in situ proximity labeling to obtain the intracellular interactome of teneurin (Ten-m) in the Drosophila brain. Genetic interaction studies using quantitative partner matching assays in both olfactory receptor neurons (ORNs) and projection neurons (PNs) reveal a common pathway: Ten-m binds to and negatively regulates a RhoGAP, thus activating the Rac1 small GTPases to promote synaptic partner matching. Developmental analyses with single-axon resolution identify the cellular mechanism of synaptic partner matching: Ten-m signaling promotes local F-actin levels and stabilizes ORN axon branches that contact partner PN dendrites. Combining spatial proteomics and high-resolution phenotypic analyses, this study advanced our understanding of both cellular and molecular mechanisms of synaptic partner matching. HIGHLIGHTSO_LIIn situ spatial proteomics reveal the first intracellular interactome of teneurins C_LIO_LITen-m signals via a RhoGAP and Rac1 GTPase to regulate synaptic partner matching C_LIO_LISingle-axon analyses reveal a stabilization-upon-contact model for partner matching C_LIO_LITen-m signaling promotes F-actin in axon branches contacting partner dendrites C_LI

neuroscience↗

bioRxiv2024

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

Mass spectrometry (MS)-based single-cell proteomics (SCP) has gained massive attention as a viable complement to other single cell approaches. The rapid technological and computational advances in the field have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell at reasonable proteome depth to characterize biological phenomena remains a challenge. To address some of those limitations we present a combination of fully automated single cell sample preparation utilizing a dedicated chip within the picolitre dispensing robot, the cellenONE. The proteoCHIP EVO 96 can be directly interfaced with the Evosep One chromatographic system for in-line desalting and highly reproducible separation with a throughput of 80 samples per day. This, in combination with the Bruker timsTOF MS instruments, demonstrates double the identifications without manual sample handling. Moreover, relative to standard high-performance liquid chromatography, the Evosep One separation provides further 2-fold improvement in protein identifications. The implementation of the newest generation timsTOF Ultra with our proteoCHIP EVO 96-based sample preparation workflow reproducibly identifies up to 4,000 proteins per single HEK-293T without a carrier or match-between runs. Our current SCP depth spans over 4 orders of magnitude and identifies over 50 biologically relevant ubiquitin ligases. We complement our highly reproducible single-cell proteomics workflow to profile hundreds of lipopolysaccharide (LPS)-perturbed THP-1 cells and identified key regulatory proteins involved in interleukin and interferon signaling. This study demonstrates that the proteoCHIP EVO 96-based SCP sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.

biochemistry↗

bioRxiv2022

Deep proteomics identifies shared molecular pathway alterations in synapses of schizophrenia and bipolar disorder patients and mouse model

Synaptic dysfunction is implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BP). We used quantitative mass-spectrometry to carry out deep and unbiased profiling of the proteome of synapses purified from the dorsolateral prefrontal cortex of 35 cases of SCZ, 35 cases of BP, and 35 controls. Compared to controls, SCZ and BP synapses showed substantial and similar proteomic alterations. Network and gene set enrichment analyses revealed upregulation of proteins associated with autophagy and certain vesicle transport pathways, and downregulation of proteins related to synaptic, mitochondrial, and ribosomal function in the synapses of individuals with SCZ or BP. Some of the same pathways (e.g., upregulation of vesicle transport, downregulation of mitochondrial and ribosomal proteins) were similarly dysregulated in the synaptic proteome of mutant mice deficient in Akap11, a recently discovered shared risk gene for SCZ and BP. Our work provides novel biological insights into molecular dysfunction at the synapse in SCZ and BP and serves as a resource for understanding the pathophysiology of these debilitating neuropsychiatric disorders.

neuroscience↗

bioRxiv2021

Transcription factor control of dendrite targeting via combinatorial cell-surface codes

Transcription factors specify the fate and connectivity of developing neurons. We investigate how a lineage-specific transcription factor, Acj6, controls the precise dendrite targeting of Drosophila olfactory projection neurons (PNs) by regulating the expression of cell-surface proteins. Quantitative cell-surface proteomic profiling of wild-type and acj6 mutant PNs in intact developing brains and a proteome-informed genetic screen identified PN surface proteins that execute Acj6-regulated wiring decisions. These include canonical cell adhesion molecules and proteins previously not associated with wiring, such as Piezo, whose mechanosensitive ion channel activity is dispensable for its function in PN dendrite targeting. Comprehensive genetic analyses revealed that Acj6 employs unique sets of cell-surface proteins in different PN types for dendrite targeting. Combinatorial expression of Acj6 wiring executors rescued acj6 mutant phenotypes with higher efficacy and breadth than expression of individual executors. Thus, Acj6 controls wiring specificity of different neuron types by specifying distinct combinatorial expression of cell- surface executors.

neuroscience↗

bioRxiv2021

Modeling transcriptional profiles of gene perturbation with deep neural network

BackgroundCell line perturbation data could be utilized as a reference for inferring underlying molecular processes in new gene expression profiles. It is important to develop accurate and computationally efficient algorithms to exploit biological knowledge in the growing compendium of existing perturbation data and harness these for new predictions. ResultsWe reframed the problem of inferring possible gene perturbation based on a reference perturbation database into a classification task and evaluated the application of deep neural network models to address this problem. Our results showed that a fully-connected multi-layer neural network was able to achieve up to 74.9% accuracy in a holdout test set, but the model generalizability was limited by consistency between training and testing data. ConclusionCapacity and flexibility enables neural network models to efficiently represent transcriptomic features associated with single gene knockdown perturbations. With consistent signals between training and testing sets, neural networks may be trained to classify new samples to experimentally confirmed molecular phenotypes.

bioinformatics↗

bioRxiv2020

Highly multiplexed quantitative phosphosite assay for biology and preclinical studies

Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug-treatment in cells and tissues but are presently lacking. Here we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho-signaling in drug-treated cancer cell lines, breast cancer preclinical models and human medulloblastoma tumors. In addition to validating previous findings, SigPath detected and quantified a large number of differentially regulated phosphosites newly associated with disease models and human tumors at baseline or with drug perturbation. Our results highlight the potential of SigPath to monitor phosphoproteomic signaling events and to nominate mechanistic hypotheses regarding oncogenesis, response and resistance to therapy.

cancer biology↗

bioRxiv2020

PANOPLY: A cloud-based platform for automated and reproducible proteogenomic data analysis

Proteogenomics involves the integrative analysis of genomic, transcriptomic, proteomic and post-translational modification data produced by next-generation sequencing and mass spectrometry-based proteomics. Several publications by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and others have highlighted the impact of proteogenomics in enabling deeper insight into the biology of cancer and identification of potential drug targets. In order to encapsulate the complex data processing required for proteogenomics, and provide a simple interface to deploy a range of algorithms developed for data analysis, we have developed PANOPLY--a cloud-based platform for automated and reproducible proteogenomic data analysis. A wide array of algorithms have been implemented, and we highlight the application of PANOPLY to the analysis of cancer proteogenomic data.

bioinformatics↗

bioRxiv2020

Biomarkers to distinguish bacterial from viral pediatric clinical pneumonia in a malaria endemic setting

BACKGROUNDDifferentiating the etiology of acute febrile respiratory illness in children is a challenge in low-income, malaria-endemic settings because the main pathogens responsible (viruses, bacteria, and malaria parasites) overlap in clinical presentation and frequently occur together as mixed infections. The critical task is to rapidly identify bacterial pneumonia to enable appropriate antibiotic treatment, ideally at point of care. Current diagnostic tests are insufficient and there is a need for the discovery and development of new tools. Here we report the identification of a unique biomarker signature that can be identified in blood samples. METHODSBlood samples from 195 pediatric Mozambican patients with clinical pneumonia were analyzed with an aptamer-based high dynamic range assay to quantify [~]1200 proteins. For discovery of new biomarkers, we identified a training set of patient samples in which the underlying etiology of the pneumonia was established as bacterial, viral or malaria. Proteins whose abundances varied significantly between patients with verified etiologies (FDR<0.01) formed the basis for predictive diagnostic models that were created using machine learning techniques (Random Forest, Elastic Net). These models were validated on a dedicated test set of samples. RESULTS219 proteins had significantly different abundances between bacterial and viral infections, and 151 differed between bacterial infections and a mixed pool of viral and malaria infections. Predictive diagnostic models achieved >90% sensitivity and >80% specificity, regardless of whether one or two pathogen classes were present. Bacterial pneumonia was strongly associated with markers of neutrophil activity, in particular neutrophil degranulation. Degranulation markers included HP, LCN2, LTF, MPO, MMP8, PGLYRP1, RETN, SERPINA1, S100A9, and SLPI. CONCLUSIONBlood protein signatures highly associated with neutrophil biology reliably differentiated bacterial pneumonia from other causes. With appropriate technology, these markers could provide the basis for a rapid diagnostic for field-based triage for antibiotic treatment of pediatric pneumonia.

genomics↗

bioRxiv2019

Cell-Surface Proteomic Profiling in the Fly Brain Uncovers New Wiring Regulators

Molecular interactions at the cellular interface mediate organized assembly of single cells into tissues, and thus govern the development and physiology of multicellular organisms. Here, we developed a cell-type-specific, spatiotemporally-resolved approach to profile cell-surface proteomes in intact tissues. Quantitative profiling of cell-surface proteomes of Drosophila olfactory projection neurons (PNs) in pupae and adults revealed a global down-regulation of wiring molecules and an up-regulation of synaptic molecules in the transition from developing to mature PNs. A proteome-instructed in vivo screen identified 20 new cell-surface molecules regulating neural circuit assembly, many of which belong to evolutionarily conserved protein families not previously linked to neural development. Genetic analysis further revealed that the lipoprotein receptor LRP1 cell-autonomously controls PN dendrite targeting, contributing to the formation of a precise olfactory map. These findings highlight the power of temporally-resolved in situ cell-surface proteomic profiling in discovering new regulators of brain wiring.

neuroscience↗