Biology subjects

Mann, M.

Publications and source records attributed to Mann, M..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

A Morpho-Proteomic Atlas of Mitosis at Sub-Minute Resolution

Precise spatiotemporal protein organization is critical for fundamental biological processes including cell division1,2. Indeed, aberrant mitosis and mitotic factors are involved in diverse diseases, including various cancers3,4, Alzheimers disease5, and rare diseases6. During mitosis, complex spatial rearrangements and regulation ensure the accurate separation of replicated sister chromatids to produce genetically identical daughter cells7-9. Previous studies employed high-throughput methodologies to follow specific proteins during mitosis10-15. Still a temporally refined systems-level approach capable of monitoring morphological and proteomic changes throughout mitosis has been lacking. Here, we achieved unprecedented resolution by phenotypically decomposing mitosis into 40 subsections of a regression plane for proteomic analysis using deep learning and regression techniques. Our deep visual proteomics (DVP) workflow16, revealed rapid, dynamic proteomic changes throughout mitosis. We quantified 4,350 proteins with high confidence, demonstrating that 147 show significant dynamic abundance changes during mitotic progression. Clustering revealed coordinated patterns of protein regulation, while network analysis uncovered tight regulation of core cell cycle proteins and a link between cell cycle and cancer-linked mutations. Immunofluorescence validated abundance changes and linked previously uncharacterised proteins, like C19orf53, to mitosis. To facilitate data navigation, we developed Mito-Omix, a user-friendly online platform that integrates intricate morphological and molecular data. Our morphological and proteomic dataset spans mitosis at high resolution, providing a rich resource for understanding healthy and aberrant cell division.

cell biology↗

bioRxiv2025

Differential cell survival outcomes in response to diverseamino acid stress

Amino acid (AA) detection is fundamental for cellular function, balancing translation demands, biochemical pathways, and signaling networks. While the GCN2 and mTORC1 pathways are known to regulate AA sensing, the global cellular response to AA deprivation remains poorly understood, particularly in non-transformed cells, which may exhibit distinct adaptive strategies compared to cancer cells. Here we employed murine pluripotent embryonic stem (ES) cells as a model system to dissect responses to AA stress. Using multi-omics analyses over an extended time course, we examined the effects of arginine (Arg) and leucine (Leu) deprivation and uncovered a broad array of proteomic, phosphoproteomic, transcriptomic, and metabolomic adaptations, including an increase in lysosome production, all occurring without lethality. We found that Arg or Leu starvation induces reversible cell cycle exit, promoting a quiescent state that enhances resistance to cytotoxic stressors. By contrast, cysteine (Cys) and threonine (Thr) deprivation led to cell death via distinct pathways: ferroptosis for Cys starvation, while Thr deprivation triggered a previously uncharacterized form of cell death, which could be entirely suppressed by methionine (Met) co-starvation, mTOR or translational inhibition. These findings suggest that ES cells implement specialized survival strategies in response to different AA limitations, highlighting their ability to reprogram cellular biochemistry under nutrient stress.

cell biology↗

bioRxiv2025

A simplified perchloric acid workflow with neutralization (PCA-N) for democratizing deep plasma proteomics at population scale

Large scale plasma proteomics studies offer tremendous potential for biomarker discovery but face significant challenges in balancing analytical depth, throughput and cost-effectiveness. We present an optimized perchloric acid-based workflow with neutralization - PCA-N - that addresses these limitations. By introducing a neutralization step following protein precipitation, PCA-N enables direct enzymatic digestion without additional purification steps, reducing sample volume requirements to only 5 {micro}L of plasma while maintaining deep plasma proteome coverage. The streamlined protocol allows preparation of over 10,000 samples per day using 384-well formats at costs comparable to undepleted plasma analysis (NEAT). Rigorous validation according to the recently introduced CLSI C64 guideline demonstrated that despite somewhat higher technical variability compared to NEAT, PCA-N maintained excellent biological resolution and reproducibility. We confirmed the workflows exceptional stability through analysis of 1,500 quality control samples systematically interspersed among 36,000 plasma samples measured continuously over 311 days. Technical performance remained consistent across multiple instruments, sample preparation batches and nearly a year of measurements. Compared to NEAT plasma proteomics, PCA-N doubled the proteomic depth while maintaining comparable reagent costs and throughput. The minimal sample reequipments, operational simplicity while using only common laboratory chemical and exceptional scalability positions PCA-N as an attractive approach for population-level plasma proteomics, democratizing access to deep plasma proteome analysis. HighlightsO_LIPerchloric acid with neutralization (PCA-N) allows drastic upscaling of plasma proteomics C_LIO_LIMinimal sample input (5 {micro}L) in 384-well format enables thousands of samples processed daily C_LIO_LIPCA-N democratizes deep plasma proteomics using only common chemicals C_LIO_LIValidation per CLSI C64 demonstrates excellent repeatability and reproducibility C_LIO_LIPCA-N showed exceptional stability across 311 days of continuous MS acquisition C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=106 HEIGHT=200 SRC="FIGDIR/small/645089v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@11eb315org.highwire.dtl.DTLVardef@7f4f41org.highwire.dtl.DTLVardef@2c41feorg.highwire.dtl.DTLVardef@1a09925_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Tree-based quantification infers proteoform regulation in bottom-up proteomics data

Quantitative readout is essential in proteomics, yet current bioinformatics methods lack a framework to handle the inherent multi-level nature of the data (fragments, MS1 isotopes, charge states, modifications, peptides and genes). We present AlphaQuant, which introduces tree-based quantification. This approach organizes quantitative data into a hierarchical tree across levels. It allows differential analyses at fragment and MS1 level, recovering up to 50-fold more regulated proteins compared to a state-of-the-art approach. Using gradient boosting on tree features, we address the largely unsolved challenge of scoring quantification accuracy, as opposed to precision. Our method clusters peptides with similar quantitative behavior, providing a new approach to the protein grouping problem and enabling identification of regulated proteoforms directly from bottom-up data. Combined with deep learning classification, we infer phosphopeptides from proteome data alone, validating our findings with EGFR stimulation data. We then describe proteoform diversity across mouse tissues, revealing distinct patterns of post translational modifications and alternative splicing.

bioinformatics↗

bioRxiv2024

The proteomic landscape of proteotoxic stress in a fibrogenic liver disease

Protein misfolding diseases, including alpha-1 antitrypsin deficiency (AATD), pose significant health challenges, with their cellular progression still poorly understood1-3. We utilize spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis4,5, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudo-time across fibrosis stages. We achieve unprecedented proteome depth of up to 3,800 proteins from a third of a single cell in formalin-fixed, paraffin-embedded (FFPE) tissue. This dataset revealed a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show alpha-1 antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with AI-guided image-based phenotyping across multiple disease stages, revealing a terminal hepatocyte state characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10/TRAIL expression. This phenotype may represent a critical disease progression stage. Our study offers novel insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.

cell biology↗

bioRxiv2024

EasyAb: A High-Throughput Workflow for Antibody-Based PTM Peptide Enrichment Method Coupled to Mass Spectrometry

Reversible post-translational modification (PTMs) is a fundamental mechanism of cellular signal transduction. In vivo and ex vivo studies to profile PTMs have greatly advanced our understanding of the complexities of cellular signaling. However, apart from the most commonly studied PTMs, large-scale analysis is still very challenging, limiting our understanding of various cellular processes. PTM-bearing peptides often enriched by antibodies, followed by unbiased mass spectrometry (MS)-based readout of hundreds or thousands of sites. To extend the reach of this powerful technology to in vivo and ex vivo studies with small protein starting amounts, we here developed EasyAb, a streamlined and high throughput MS workflow for antibody-based PTM profiling. Using epidermal growth factor receptor (EGFR) signaling and acute myeloid leukemia (AML) cell systems, we demonstrate that EasyAb increases sensitivity and enables multiple large-scale systems-level studies. Furthermore, EasyAb resolves in vivo brain G protein-coupled receptor-mediated tyrosine kinase activation and reveals the long elusive hypothalamic neuron-specific leptin signaling architecture. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/620939v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@259537org.highwire.dtl.DTLVardef@1675a43org.highwire.dtl.DTLVardef@570b61org.highwire.dtl.DTLVardef@ac45df_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSEasyAb, a sensitive, rapid and high-throughput method to quantify post translationally modified peptides in diverse cell systems. EasyAb reveals differential tyrosine phosphorylation of mRNA splicing proteins in AML patient samples. Activation of KOR by "G-protein-biased" non-aversive agonist 6GNTI elicits Src kinase activity in mice. Elucidation of in vivo hypothalamic Leptin induced LEPRb-Jak2 phosphotyrosine signaling architecture.

biochemistry↗

bioRxiv2024

A proteome-wide dependency map of protein interaction motifs

Short linear motifs (SLiMs) are the most ubiquitous protein interaction modules within unstructured regions of the human proteome, yet their contribution to cellular homeostasis remains poorly understood. To systematically assess SLiM function, we applied base editing to mutate all reported and a set of computationally predicted SLiMs defined by SLiM-like evolutionary patterns. By screening 7,293 SLiM-containing regions with 80,473 mutations in HAP1 cells, we define a SLiM dependency map identifying 450 known and 264 predicted SLiMs required for normal cell proliferation. Mutational consequences were highly reproducible in RPE1 cells, with differences attributed to cell line-specific gene essentiality. We show that most essential predicted SLiMs belong to novel classes and identify binding partners for several of these, providing mechanistic insight into a disease-associated ANKRD17 mutation. Our study provides a proteome-wide resource on SLiM essentiality and uncovers numerous uncharacterized yet essential SLiMs.

cell biology↗

bioRxiv2024

Targeting Specific Kinase Substrates Rescues Increased Colitis Severity Induced by the Crohn's Disease-Linked LRRK2-N2081D Variant

LRRK2 contains a kinase domain where both the N2081D Crohns disease (CD) risk and the G2019S Parkinsons disease (PD)-pathogenic variants are located. The mechanisms by which the N2081D variant increase CD risk, and how these adjacent mutations result in distinct diseases, remain unclear. To investigate the pathophysiology of the CD-linked LRRK2 N2081D variant, we generated a knock-in (KI) mouse model and compared its effects to those of the LRRK2-G2019S mutation. We find that Lrrk2N2081D KI mice demonstrate heightened sensitivity to induced colitis, resulting in more severe inflammation and intestinal damage than Lrrk2G2019SKI and wild-type mice. Analysis of Colon tissue revealed distinct mutation-dependent LRRK2 RAB substrate phosphorylation, with significantly elevated phosphorylated RAB10 levels in Lrrk2N2081D mice. In cells, we demonstrate that the N2081D mutation activates LRRK2 through a mechanism distinct from that of LRRK2-G2019S. We further find that proinflammatory stimulation enhances LRRK2 kinase activity, leading to mutation-dependent differences in RAB phosphorylation and inflammatory responses in dendritic cells. Finally, we show that genetic knockout of Rab12, but not pharmacological LRRK2 kinase inhibition, significantly reduced colitis severity in Lrrk2N2081D mice. Our study characterizes the pathogenic mechanisms of LRRK2-linked CD, highlights important structural and functional differences between disease-associated LRRK2 variants, and suggests RAB proteins as promising therapeutic targets for modulating LRRK2 activity in CD treatment.

molecular biology↗

bioRxiv2024

Deep Visual Proteomics reveals DNA replication stress as a hallmark of Signet Ring Cell Carcinoma

Signet Ring Cell Carcinoma (SRCC) is a rare and highly malignant form of adenocarcinoma with increasing incidence and poor prognosis due to late diagnosis and limited treatment options. We employed Deep Visual Proteomics (DVP), which combines AI directed cell segmentation and classification with laser microdissection and ultra-high sensitivity mass spectrometry, for cell-type specific proteomic analysis of SRCC across the bladder, prostate, liver, and lymph nodes of a single patient. DVP identified significant alterations in DNA damage response (DDR) proteins, particularly within the ATR and mismatch repair (MMR) pathways, indicating replication stress as a crucial factor in SRCC mutagenicity. Additionally, we observed substantial enrichment of immune-related proteins, reflecting high levels of cytotoxic T lymphocyte infiltration and elevated PD-1 expression. These findings suggest that pembrolizumab immunotherapy may be more effective than conventional chemotherapy for this patient. Our results provide novel insights into the proteomic landscape of SRCC, identifying potential targets and open up for personalized therapeutic strategies in managing SRCC.

cancer biology↗

bioRxiv2024

Proteomic analysis of isolated nerve terminals from NaV1.9 knockout mice reveals pathways relevant for neuropathic pain signalling

Neuropathic pain substantially affects the mental and physical well-being of patients and magnifies the socio-economic burden on the healthcare system. It is important to understand the molecular mechanisms underlying chronic pain to effectively target it. To investigate peripheral mechanisms relevant to pain signaling, we isolated nerve terminals from mouse footpads. The isolated peripheral terminals contain both pre- and post-synaptic proteins and are deficient in keratin and histone in both mice and humans. We detected the protein translational machinery and mitochondria in nerve terminals and observed that they were capable of endocytosis. An unbiased proteomic analysis of nerve terminals from footpads of NaV1.9 knockout mice shows dysregulation of the p38 mitogen-activated protein kinase (MAPK) and extracellular regulated kinase 1/2 (ERK1/2) pathways, and of protein components involved in translation and energy metabolism. Isolation of human nerve terminals from skin punch biopsies, validated by proteomic analysis, highlights the broad and translational value of our approach. Our study thus reveals peripheral signaling mechanisms implicated in pain perception.

neuroscience↗

bioRxiv2024

Deep Visual Proteomics Unveils Precision Medicine Insights in Composite Small Lymphocytic and Classical Hodgkin Lymphoma

Coexistence of two cancer types in the same organ presents challenges for clinical decision-making, calling for personalized treatment strategies. Deep Visual Proteomics (DVP) combines AI driven single cell type analysis with laser microdissection and ultrasensitive mass spectrometry. In a composite case of classical Hodgkin lymphoma (cHL) and small lymphocytic lymphoma (SLL) in a single patient, we investigated the potential of DVP to inform precision oncology. We quantified the proteomic landscapes in the cHL and SLL to a depth of thousands of proteins. Our analysis revealed distinct proteome profiles in cHL and SLL populations, highlighting their clonal unrelatedness. Our data suggested standardized chemotherapy and interleukin-4 inhibition as potential strategies to manage chemo-resistance - instead of bone marrow transplantation. DVP highlighted minichromosome maintenance protein and proteasome inhibitors for cHL and H3K27 methylation and receptor tyrosine kinase inhibitors for SLL as subtype-specific treatments. Thus cell-type specific insights of DVP can guide personalized oncological treatments.

cancer biology↗

bioRxiv2024

AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics

Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and more application areas. The scale of data generated on novel instrumentation and acquisition strategies pose a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm particularly suited for detecting patterns in data produced by sensitive time-of-flight instruments. It naturally adapts to novel, more eTicient scan modes that are not yet accessible to previous algorithms. Rigorous benchmarking demonstrates competitive identification and quantification performance. While supporting empirical spectral libraries, we propose a new search strategy named end-to-end transfer learning using fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine and experiment specific properties, enabling the generic DIA analysis of any post-translational modification (PTM). AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.

bioinformatics↗

bioRxiv2024

A novel hybrid high speed mass spectrometer allows rapid translation from biomarker candidates to targeted clinical tests using 15N labeled proteins

Recent developments in affinity binder or mass spectrometry (MS)-based plasma proteomics are now producing panels of potential biomarker candidates for diagnosis or prognosis. However, clinical validation and implementation of these biomarkers remain limited by the reliance on dated triple quadrupole MS technology. Here, we evaluate a novel hybrid high-speed mass spectrometer, Stellar MS, which integrates the robustness of triple quadrupoles with the enhanced capabilities of an advanced linear ion trap analyzer. This instrument allows for extremely rapid and sensitive parallel reaction monitoring (PRM) and MS3 targeting. The Stellar MS allowed targeting thousands of peptides originally measured on Orbitrap Astral MS, achieving high reproducibility and low coefficients of variation (CV) as well as sensitivity and specificity sufficient for more than the top 1000 plasma proteins. Furthermore, we developed targeted assays for alcohol-related liver disease (ALD) biomarkers, showcasing the potential of Stellar MS in clinical applications. Absolute quantification is typically a requirement for clinical assays and we explore the use of 15 N labeled protein standards in a rapid, streamlined and generic manner. Our results indicate that Stellar MS can bridge the gap between proteomics discovery and routine clinical testing, enhancing the diagnostic and prognostic utility of protein biomarkers.

biochemistry↗

bioRxiv2024

Deciphering functional tumor-immune crosstalk through highly multiplexed imaging and deep visual proteomics

Deciphering the intricate tumor-immune interactions within the microenvironment is crucial for advancing cancer immunotherapy. Here, we developed a novel approach integrating highly multiplexed imaging, laser microdissection, and deep visual proteomics (DVP) to spatially profile the proteomes of 21 distinct cell populations in human colorectal and tonsil cancers with high sensitivity. We selected colorectal tumor as an example of a cold tumor, uncovering an immunosuppressive macrophage barrier impeding T cell infiltration and regionally altering lymphocyte proteomes. Spatial proteomic analysis revealed distinct functional states of T cells in different tumor compartments. In tonsil cancer - a hot tumor, we identified significant heterogeneity of cell proteomes influenced by proximity to cytotoxic T cell subtypes. Tumor-infiltrating T cells exhibited metabolic adaptations and enhanced stress resilience, enabling migration into hypoxic regions. Our spatially-resolved, highly multiplexed strategy deciphers the complex cellular interplay within the tumor microenvironment, with implications for identifying new immunotherapy targets and signatures. HighlightsO_LINovel approach integrates multiplexed imaging and deep proteomics to profile the tumor microenvironment C_LIO_LIIdentified 21 distinct cell populations and thousands of proteins from single cell types C_LIO_LIMacrophage barrier impedes T cell tumor infiltration and T cells adapt to hypoxia C_LIO_LISpatial insights may guide new immunotherapy targets and personalized oncology C_LI

cancer biology↗

bioRxiv2024

Deep Visual Proteomics advances human colon organoid models by revealing a switch to an in vivo-like phenotype upon xenotransplantation

AbstractIntestinal epithelial damage predisposes to chronic disorders like inflammatory bowel disease. The organoid model allows cultivation, expansion and analysis of primary intestinal epithelial cells and has been instrumental in studying epithelial behavior in homeostasis and disease. Recent advances in organoid transplantation allow studying human epithelial cell behavior within the intestinal tissue context. However, it remained unclear how organoid transplantation into the colon affects epithelial phenotypes, which is key to assessing the models suitability to study human epithelial cells. We employed Deep Visual Proteomics, integrating AI-guided cell classification, laser microdissection, and an improved proteomics pipeline to study the human colon. This created an in-depth cell type-resolved proteomics resource of human intestinal epithelial cells within human tissue, in vitro organoids, and the murine colon post-xenotransplantation. Our findings reveal that in vitro conditions induce a proliferative organoid phenotype, which was reversible upon transplantation and adjustment of organoid culturing conditions.

cell biology↗

bioRxiv2024

Capture of Human Neuromesodermal and Posterior Neural Tube Axial Stem Cells

The spinal cord, nerves, and skeletal muscles arise from neuromesodermal progenitors (NMPs). We have developed a growth-factor screening strategy, utilizing ES and iPS cells, facilitating the indefinite self-renewal of two types of human axial stem cells (AxSCs), closely resembling mouse NMPs (NM-AxSCs) and posterior neural tube progenitors (N-AxSCs). Under specific regimens-- Wnt/CHIR99021, FGF2, and TGF-{beta} inhibitor SB431542 (CFS) and excluding FGF2 (CS), respectively--these AxSCs self-renew and sustain telomeres. Single cell transcriptomics and proteomics have revealed expression of posterior growth-zone and dorsoventral neural tube markers in NM-AxSCs, and correspondingly, differentiation to a wide spectrum of neural tube neurons and myocytes. N-AxSCs rapidly matured into dorsal sensory subsets and neural crest. Crucially, neither AxSC type produces teratomas, and analogous mouse NM-AxSCs integrated successfully into the neural tube and somites. Capturing of AxSCs from patient and GMP ES / iPS cells without transgenesis unveils ontogeny and promises modeling and therapy in neuropathies.

developmental biology↗

bioRxiv2024

Systematic mapping of MCU-mediated mitochondrial calcium signaling networks

The Mitochondrial Ca2+ Uniporter Channel (MCUC) allows calcium entry into the mitochondrial matrix to regulate energy metabolism but also cell death. Although, several MCUC components have been identified, the molecular basis of mitochondrial Ca2+ signaling networks and their remodeling upon changes in uniporter activity have not been systematically assessed. Using an unbiased and quantitative proteomic approach, we map the MCUC interactome in HEK293 cells under physiological conditions and upon chronic loss or gain of mitochondrial Ca2+ uptake. Besides all previously known subunits of the uniporter, we identify 89 high-confidence interactors linking MCUC to several mitochondrial complexes and pathways, half of which are currently linked to metabolic, neurological, and immunological diseases. As a proof-of-concept, we validate EFHD1 as a binding partner of MCU, EMRE and MCUB with a MICU1-dependent inhibitory effect on Ca2+ uptake. To investigate compensatory mechanisms and functional consequences of mitochondrial Ca2+ dyshomeostasis, we systematically survey the MCU interactome upon silencing of EMRE, MCUB, MICU1 or MICU2. We observe profound changes in the MCU interconnectivity, whereby downregulation of EMRE reduces the number of MCU interactors of over 10-fold, while silencing of MCUB leads to a wider functional network linking MCU to mitochondrial stress response pathways and cell death. Altogether our study provides a comprehensive map of MCUC protein-protein interactions and a rich, high-confidence resource that can be explored to gain insights into the players and mechanisms involved in calcium signal transduction cascades and their relevance in human diseases.

cell biology↗

bioRxiv2023

Grove-level analysis of titer and prevalence of Candidatus Liberibacter asiaticus and Wolbachia in Diaphorina citri, vector of citrus Huanglongbing

Huanglongbing (HLB, or citrus greening disease) affects all citrus varieties world-wide. In the USA, Asia, and South America the causal agent is "Candidatus Liberibacter asiaticus" (CLas), a phloem-limited, uncultured, alphaproteobacterium. The hemipteran insect vector, Diaphorina citri (Asian citrus psyllid) acquires and transmits CLas in a circulative, propagative manner. In addition to CLas, D. citri hosts multiple symbiotic bacterial species including Wolbachia (wDi). In D. citri, wDi has been sequenced and studied but specific roles in D. citri biology are unknown. Using well established quantitative PCR methods we measured CLas titer in D. citri collected from four groves in central Florida with distinct HLB management strategies and tested whether CLas and wDi titer were correlated in a sub-set of these insects. Grove site had the largest effect on CLas titer. Sex had no effect on CLas titer, while higher wDi titer was correlated with non-infected insects. Our results suggest that more directed follow-up research is necessary and important to clarify whether field management tactics influence CLas titer in D. citri and to better understand gene-by-environment interactions among D. citri, wDi and CLas. Now that millions of trees in Florida have been treated with injectable formulations of oxytetracycline, which is likely to decrease bacterial populations in D. citri, this study may represent the last biologically meaningful snapshot of grove-level vector-pathogen ecology in the state during the HLB epidemic.

plant biology↗