bioRxiv Science⌕ Search

Biology subjects

Salemi, M.

Publications and source records attributed to Salemi, M..

At least 19 recordsLinked to original sources
bioRxiv2026

The increased production of monocytes with immunosuppressive transcriptional profiles predicts the development of SIV encephalitis

HIV-associated neurocognitive disorders (HAND) remain common despite antiretroviral therapy. We previously demonstrated that increased percentages of newly generated BrdU-labeled (BrdU+) monocytes predict AIDS progression and SIV encephalitis (SIVE) as early as 8 and consistently by 21 days post-infection (DPI) in SIV-infected macaques, but their molecular identity remained unknown. We performed Bulk RNA sequencing of FACS-sorted BrdU+ and BrdU- monocytes from uninfected animals and SIV-infected macaques at 21 DPI and necropsies, including SIVE or not (SIVnoE). BrdU+ monocytes underwent progressive transcriptional remodeling during SIV infection. Early SIVE-predictive monocytes exhibited suppressed interferon, TNF-/NF-{kappa}B, antigen-presentation, and antimicrobial pathways, with increased LILRB4 and PARP2 expression. At terminal disease, SIVE BrdU+ monocytes displayed an immunosuppressive profile, reduced HLA-DR expression, and increased structural variants compared with SIVnoE monocytes. Together, newly generated circulating monocytes in animals progressing to SIVE are an immature and immunosuppressive population that emerges before neuropathology, implicating dysregulated monocytopoiesis and pathogenic monocytes in HAND.

immunology↗
bioRxiv2025

Vir2vec: A Genome-Wide Viral Embedding

Genomic language models (gLMs) are powerful numerical surrogates for DNA, but existing architectures focus primarily on human DNA or limited viral sets, and no dedicated benchmark exists for viral genome understanding. Here we introduce Vir2vec (from 17M to 422M parameters), decoder-only gLMs continually pretrained on a curated pan-viral corpus of 565,747 complete genomes across 295 species. We also present the viral Genome Understanding Evaluation (vGUE), a benchmark probing viral representations across broad organism discrimination, evolutionary signatures, intra-genus separation, variant subtyping, and host tropism. Evaluated as frozen feature extractors under nested cross-validation, Vir2vec achieves top balanced accuracy across 5 of 7 vGUE tasks, outperforming human-trained (MistralDNA), viral-specific (ModernBERTvirus), and broad biological foundation (Evo1) model. Furthermore, Vir2vec displays robust zero-shot generalization to unseen viral families. Finally, we introduce an argmax-routed SHAP explainability framework that projects feature attributions directly onto single-codon coordinates, correctly isolating key receptor-binding domain mutations driving mouse host adaptation. Vir2vec and vGUE provide a foundation for viral surveillance and discovery pipelines.

bioinformatics↗
bioRxiv2025

PARylation in Parkinson's disease: a bridge between Lewy body formation and neuronal cell death

Poly-ADP-ribosylation (PARylation), catalyzed by the enzyme PARP1, involves the addition of poly-ADP-ribose polymers (PAR) and has been associated with -synuclein aggregation in Parkinsons disease (PD) models. This study aimed to unravel the role of PARylation in -synuclein aggregation and neuronal cell death in the complex environment of post-mortem human PD brains. Using high-resolution imaging and 3D reconstruction analysis, we observed that PAR accumulate in the cytoplasm in regions affected by PD pathology, preceding the formation of -synuclein oligomers. Additionally, we found that PAR and stress granules contribute to the formation of Lewy bodies. Increased colocalization of PAR with mitochondria in the substantia nigra of PD patients, along with the presence of PAR-positive condensed DNA, further suggests a role in neuronal cell death. Collectively, our findings reveal a critical involvement of PARylation in the pathological mechanisms underlying neurodegeneration in PD and position PARylation as a potential therapeutic target. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/642849v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@64858aorg.highwire.dtl.DTLVardef@17b4706org.highwire.dtl.DTLVardef@73f865org.highwire.dtl.DTLVardef@1b8d619_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗
bioRxiv2025

SEHI-PPI: An End-to-End Sampling-Enhanced Human-Influenza Protein-Protein Interaction Prediction Framework with Double-View Learning

Influenza continues to pose significant global health threats, hijacking host cellular machinery through protein-protein interactions (PPIs), which are fundamental to viral entry, replication, immune evasion, and transmission. Yet, our understanding of these host-virus PPIs remains incomplete due to the vast diversity of viral proteins, their rapid mutation rates, and the limited availability of experimentally validated interaction data. Additionally, existing computational methods often struggle due to the limited availability of high-quality samples and their inability to effectively model the complex nature of host-virus interactions. To address these challenges, we present SEHI-PPI, an end-to-end framework for human-influenza PPI prediction. SEHI-PPI integrates a double-view deep learning architecture that captures both global and local sequence features, coupled with a novel adaptive negative sampling strategy to generate reliable and high-quality negative samples. Our method outperforms multiple benchmarks, including state-of-the-art large language models, achieving a superior performance in sensitivity (0.986) and AUROC (0.987). Notably, in a stringent test involving entirely unseen human and influenza protein families, SEHI-PPI maintains strong performance with an AUROC of 0.837. The model also demonstrates high generalizability across other human-virus PPI datasets, with an average sensitivity of 0.929 and AUROC of 0.928. Furthermore, AlphaFold3-guided case studies reveal that viral proteins predicted to target the same human protein cluster together structurally and functionally, underscoring the biological relevance of our predictions. These discoveries demonstrate the reliability of our SEHI-PPI framework in uncovering biologically meaningful host-virus interactions and potential therapeutic targets.

bioinformatics↗
bioRxiv2024

SARITA: A Large Language Model for Generating the S1 Subunit of the SARS-CoV-2 Spike Protein

The COVID-19 pandemic has profoundly impacted global health, economics, and daily life, with over 776 million cases and 7 million deaths from December 2019 to November 2024. Since the original SARS-CoV-2 Wuhan strain emerged, the virus has evolved into variants such as Alpha, Beta, Gamma, Delta, and Omicron, all characterized by mutations in the Spike glycoprotein, critical for viral entry into human cells via its S1 and S2 subunits. The S1 subunit, binding to the ACE2 receptor and mutating frequently, affects infectivity and immune evasion; the more conserved S2, on the other hand, facilitates membrane fusion. Predicting future mutations is crucial for developing vaccines and treatments adaptable to emerging strains, enhancing preparedness and intervention design. Generative Large Language Models (LLMs) are becoming increasingly common in the field of genomics, given their ability to generate realistic synthetic biological sequences, including applications in protein design and engineering. Here we present SARITA, an LLM with up to 1.2 billion parameters, based on GPT-3 architecture, designed to generate high-quality synthetic SARS-CoV-2 Spike S1 sequences. SARITA is trained via continuous learning on the pre-existing protein model RITA. When trained on Alpha, Beta, and Gamma variants (data up to February 2021 included), SARITA correctly predicts the evolution of future S1 mutations, including characterized mutations of Delta, Omicron and Iota variants. Furthermore, we show how SARITA outperforms alternative approaches, including other LLMs, in terms of sequence quality, realism, and similarity with real-world S1 sequences. These results indicate the potential of SARITA to predict future SARS-CoV-2 S1 evolution, potentially aiding in the development of adaptable vaccines and treatments.

bioinformatics↗
bioRxiv2024

A mechanism that transduces lysosomal damage signals to stress granule formation for cell survival

Lysosomal damage poses a significant threat to cell survival. Our previous work has reported that lysosomal damage induces stress granule (SG) formation. However, the importance of SG formation in determining cell fate and the precise mechanisms through which lysosomal damage triggers SG formation remains unclear. Here, we show that SG formation is initiated via a novel calcium-dependent pathway and plays a protective role in promoting cell survival in response to lysosomal damage. Mechanistically, we demonstrate that during lysosomal damage, ALIX, a calcium-activated protein, transduces lysosomal damage signals by sensing calcium leakage to induce SG formation by controlling the phosphorylation of eIF2. ALIX modulates eIF2 phosphorylation by regulating the association between PKR and its activator PACT, with galectin-3 exerting a negative effect on this process. We also found this regulatory event of SG formation occur on damaged lysosomes. Collectively, these investigations reveal novel insights into the precise regulation of SG formation triggered by lysosomal damage, and shed light on the interaction between damaged lysosomes and SGs. Importantly, SG formation is significant for promoting cell survival in the physiological context of lysosomal damage inflicted by SARS-CoV-2 ORF3a, adenovirus infection, Malaria hemozoin, proteopathic tau as well as environmental hazard silica.

cell biology↗
bioRxiv2023

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, lineages, and sublineages, outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute more than 10% of all the viral sequences added to the GISAID database on a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of about 4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% - 3%), with median lead times of 4-17 weeks, and predicts FDLs [~]5 and [~]25 times better than a baseline approach For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies. Key PointsO_LIIntroduction of DeepAutoCoV: The article introduces DeepAutoCoV, an unsupervised deep learning anomaly detection system designed to predict future dominant lineages (FDLs) of SARS-CoV-2. FDLs are defined as viral (sub)lineages that will constitute more than 10% of all viral sequences added to the GISAID database in a given week; C_LIO_LIPerformance and Predictive Capability: DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% to 3%), with median lead times of 4 to 17 weeks before they become dominant. It predicts FDLs approximately 5 to 25 times better than baseline approaches. For instance, the B.1.617.2 vaccine reference strain was identified when its frequency was only 0.01%, over a year before it was considered for vaccine updates; C_LIO_LIInterpretable Results and Mutation Identification: The system provides interpretable results by pinpointing specific mutations that may be linked to increased fitness, offering insights that can optimize public health interventions. Key FDL mutations, such as those found in Delta and Omicron variants, are identified and analyzed for their potential impact on viral spread and immune escape; C_LIO_LIAdvantages and Applications: DeepAutoCoV is advantageous because it does not require prior assumptions about which protein sites are more likely to mutate. Its application in genomic surveillance systems could significantly reduce the time needed for public health responses to emerging variants, enabling early interventions such as vaccine updates; C_LIO_LIEvaluation and Comparisons: The performance of DeepAutoCoV was tested over four years of global and national surveillance data, demonstrating superior predictive power compared to other supervised and unsupervised methods. The system is periodically updated to adapt to the evolving viral landscape, making it a robust tool for ongoing surveillance efforts. C_LI

bioinformatics↗
bioRxiv2023

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful to reconstruct the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for infection forecasting in addition to backtracking, developing a phylogeny-based deep learning system, called DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, and it is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at: https://github.com/lab-smile/DeepDynaForcast. Author SummaryDuring an outbreak or sustained epidemic, accurate prediction of patterns in transmission risk can reliably inform public health strategies. Projections indicating growth or decline of transmission for specific risk groups can significantly enhance the optimization of interventions, especially when resources are limited. To address this, we present DeepDynaForecast, a cutting-edge deep learning algorithm designed for forecasting pathogen transmission dynamics. Uniquely, DeepDynaForecast was trained on in-depth simulation data and used more information from the phylogenetic tree of pathogen sequence data than any other algorithm in the field to date, allowing classification of samples according to their dynamics (growth, static, or decline) with incredible accuracy. We evaluated the models performance using both simulated outbreak data and empirical, large-scale data from the HIV epidemic in Florida between 2012 and 2020. We conclude DeepDynaForecast represents a significant advancement in genomics-mediated pathogen transmission characterization and has the potential to catalyze new research directions within virology, molecular biology, and public health.

bioinformatics↗
bioRxiv2022

Machine learning prediction and phyloanatomic modeling of viral neuroadaptive signatures in the macaque model of HIV-mediated neuropathology

In human immunodeficiency virus (HIV) infection, virus replication in the central nervous system (CNS) can result in HIV-associated neurocognitive deficits in approximately 25% of patients with unsuppressed viremia and is thought to be characterized by evolutionary adaptation to this unique microenvironment. While no single mutation can be agreed upon as distinguishing the neuroadapted population from virus in patients without neuropathology, earlier studies have demonstrated that a machine learning (ML) approach could be applied to identify a collection of mutational signatures within the envelope glycoprotein (Env Gp120) predictive of disease. The S[imian] IV-infected macaque is a widely used animal model of HIV neuropathology, allowing in-depth tissue sampling infeasible for human patients. Yet, translational impact of the ML approach within the context of the macaque model has not been tested, much less the capacity for early prediction in other, non-invasive tissues. We applied the previously described ML approach to prediction of SIV-mediated encephalitis (SIVE) using gp120 sequences obtained from the CNS of animals with and without SIVE with 73% accuracy. The presence of SIVE signatures at earlier time points of infection in non-CNS tissues in both SIVE and SIVnoE animals indicated these signatures cannot be used in a clinical setting. However, combined with protein structural mapping and statistical phylogenetic inference, results revealed common denominators associated with these signatures, including 2-acetamido-2-deoxy-beta-D-glucopyranose structural interactions and the infection of alveolar macrophages. Alveolar macrophages were demonstrated to harbor a relatively large proportion (35 - 100%) of SIVE-classified sequences and to be the phyloanatomic source of cranial virus in SIVE, but not SIVnoE animals. While this combined approach cannot distinguish the role of this cell population as an indicator of cellular tropism from a source of neuroadapted virus, it provides a key to understanding the function and evolution of the signatures identified as predictive of both HIV and SIV neuropathology. Author summaryHIV-associated neurocognitive disorders remain prevalent among HIV-infected individuals, even in the era of potent antiretroviral therapy, and our understanding of the mechanisms involved in disease pathogenesis, such as virus evolution and adaptation, remains elusive. In this study, we expand on a machine learning method previously used to predict neurocognitive impairment in HIV-infected individuals to the macaque model of AIDS-related neuropathology in order to characterize its translatability and predictive capacity in other sampling tissues and time points. We identified four amino acid and/or biochemical signatures associated with disease that, similar to HIV, demonstrated a proclivity for proximity to aminoglycans in the protein structure. These signatures were not, however, isolated to specific points in time or even to the central nervous system, as they could be observed at low levels during initial infection and from various tissues, most prominently in the lungs. The spatiotemporal patterns observed limit the use of these signatures as an accurate prediction for neuropathogenesis prior to the onset of symptoms, though results from this study warrant further investigation into the role of these signatures, as well as lung tissue, in viral entry to and replication in the brain.

evolutionary biology↗
bioRxiv2022

High-throughput single-cell sequencing for retroviral reservoir characterization

During the course of infection, human immunodeficiency virus (HIV) maintains a stably integrated reservoir of replication-competent viruses within the host genome that are unaffected by antiretroviral therapy. Curative advancements rely heavily on targeting the anatomical reservoirs, though determinants of their evolutionary origins through phyloanatomic inference remain ill-supported through current sequencing and sequence analysis strategies. The vast replication-defective genomic landscape that comprises the HIV DNA population is often discarded in these evolutionary endeavors, despite key information regarding competent ancestry that can be gained from captured genomic regions outside the historically used viral envelope gene. Here, we describe the application of small-amplicon, single-cell DNA sequencing to blood and lymph node samples from a treatment-interrupted S[imian]IV-infected animal model and evaluate the contribution of genome coverage and inclusion on phylogenetic resolution and phyloanatomic inference. Findings from this study point to incomplete genomes as a significant source of phylogenetic information on movement of virus between tissue reservoirs during therapy.

genomics↗
bioRxiv2022

Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics

In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Several approaches exist that utilize the evolutionary information from pathogen genomic data derived from infected individuals to distinguish these groups from the background population, comprised of primarily randomly sampled individuals with undetermined epidemiological linkage. These methods are, however, limited in their ability to characterize the dynamics of these groups, or clusters of transmission. Dynamic transmission patterns within these clusters, whether it be the result of changes at the level of the virus (e.g., infectivity) or host (e.g., vaccination implementation), are critical in strategizing public health interventions, particularly when resources are limited. Phylogenetic trees are widely used not only in the detection of transmission clusters, but the topological shape of the branches within can be useful sources of information regarding the dynamics of the represented population. We evaluate the limitation of existing tree shape statistics when dealing with smaller sub-trees containing transmission clusters and offer instead a phylogeny-based deep learning system -DeepDynaTree- for classification of transmission cluster. Comprehensive experiments carried out on a variety of simulated epidemic growth models indicate that this graph deep learning approach is effective in predicting cluster dynamics (balanced accuracy of 0.826 vs. 0.533 and Brier score of 0.234 vs. 0.466 in independent test set). Our deployment model in DeepDynaTree incorporates a primal-dual graph neural network principle using output from phylogenetic-based cluster identification tools (available from https://github.com/salemilab/DeepDynaTree).

bioinformatics↗
bioRxiv2022

Development of a monoclonal antibody to a vibriophage as a proxy for Vibrio cholerae detection

Cholera is an acute watery diarrheal disease that causes high rates of morbidity and mortality without treatment. Early detection of the etiologic agent of toxigenic Vibrio cholerae is important to mobilize treatment and mitigate outbreaks. Monoclonal antibody (mAb) based rapid diagnostic tests (RDTs) enable early detection in settings without laboratory capacity. However, the odds of an RDT testing positive are reduced by nearly 90% when the common virulent bacteriophage ICP1 is present. We hypothesize that adding a mAb for the common, and specific, virulent bacteriophage ICP1 as a proxy for V. cholerae to an RDT will increase diagnostic sensitivity when virulent ICP1 phage are present. In this study, we used an in-silico approach to identify immunogenic ICP1 protein targets that were conserved across disparate time periods and locations. Specificity of targets to cholera patients with known ICP1 was determined, and specific targets were used to produce mAbs in a murine model. Candidate mAbs to the head protein demonstrated specificity to ICP1 by ELISA and an ICP1 phage neutralization assay. The limit of detection of the final mAb candidate for ICP1 phage particles spiked into cholera stool matrix was 8 x 105 plaque forming units by Western blot analysis. This mAb will be incorporated into a RDT prototype for evaluation in a future diagnostic study to test the guiding hypothesis behind this study.

immunology↗
bioRxiv2021

Infectivity of three Mayaro Virus (Genus Alphavirus, Family Togaviridae) geographic isolates in human cell lines

Mayaro virus (MAYV) is an emergent arthropod-borne virus that causes an acute febrile illness accompanied by arthralgia, similar to chikungunya virus. Increasing urbanization of MAYV outbreaks in the Americas has led to concerns that this virus could further expand its geographic range. Given the potential importance of this pathogen, we sought to fill some critical gaps in knowledge regarding MAYV infectivity and geographic variation. This study describes the cytopathogenicity of MAYV in human dermal fibroblasts, human skeletal muscle satellite cells, human embryonic kidney cells (HEK), peripherally derived human macrophages, and Vero cells. MAYV strain isolated from Bolivia (MAYV-U) infected cells more rapidly compared to MAYV strains isolated in Peru and Brazil (MAYV-P; MAYV-B), with high titers (1x108 pfu/ml) peaking at 37 hours post infection. MAYV-U also caused the most cytopathic effect in a time dependent manner. Furthermore, differently from the other two prototypic strains, MAYV-U harbors unique mutations in the E2 protein, D60G and S205F, likely to interact with the host cell receptor, which may explain the observed differences in infectivity. We further demonstrate that pre-treatment of cells with interferon-{beta} inhibited viral replication in a dose-dependent manner. Together, these findings advance our understanding of MAYV infection of human target cells and provide initial data regarding MAYV phenotypic variation according to geography. Author SummaryArthropod-borne viruses are of great public health concern, causing epidemics worldwide due to climate change, changes in land use, rapid urbanization, and the expanding geographic ranges of suitable vectors. Among these viruses, Mayaro is an emerging virus for which little is currently known. This study aims to answer fundamental questions of Mayaro virus biology using three geographically distinct viral strains to examine variability in infection kinetics and infectivity in susceptible cell types. We found one geographic isolate to have accelerated infection kinetics and increased cell damage because of infection. To better understand what was unique about this isolate, we compared their envelope protein, which is critical for entry into a cell. We found that the isolate with increased replication kinetics possessed mutations at sites that may promote viral entry, which could explain these findings. Together, these findings further our understanding of Mayaro virus biology and provide insight into factors that contribute to Mayaro transmission and infectivity.

microbiology↗
bioRxiv2021

DYNAMITE: a phylogenetic tool for identification of dynamic transmission epicenters

Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree, that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics (e.g., median genetic distance over the clade) would not be expected to capture dynamic clusters of transmission. For this purpose, we have developed DYNAMITE (DYNAMic Identification of Transmission Epicenters), a cluster identification algorithm based on a branch-wise (rather than traditional clade-wise) search for cluster criteria, allowing partial clades to be recognized as clusters. Using simulated viral outbreaks with varying cluster types and dynamics, we show that DYNAMITE is consistently more sensitive than existing tools in detecting both static and dynamic transmission clusters. DYNAMITE has been implemented in R and released as open source at: github.com/ProsperiLab/DYNAMITE.

bioinformatics↗
bioRxiv2020

Differing impacts of global and regional responses on SARS-CoV-2 transmission cluster dynamics

Although the global response to COVID-19 has not been entirely unified, the opportunity arises to assess the impact of regional public health interventions and to classify strategies according to their outcome. Analysis of genetic sequence data gathered over the course of the pandemic allows us to link the dynamics associated with networks of connected individuals with specific interventions. In this study, clusters of transmission were inferred from a phylogenetic tree representing the relationships of patient sequences sampled from December 30, 2019 to April 17, 2020. Metadata comprising sampling time and location were used to define the global behavior of transmission over this earlier sampling period, but also the involvement of individual regions in transmission cluster dynamics. Results demonstrate a positive impact of international travel restrictions and nationwide lockdowns on global cluster dynamics. However, residual, localized clusters displayed a wide range of estimated initial secondary infection rates, for which uniform public health interventions are unlikely to have sustainable effects. Our findings highlight the presence of so-called "super-spreaders", with the propensity to infect a larger-than-average number of people, in countries, such as the USA, for which additional mitigation efforts targeting events surrounding this type of spread are urgently needed to curb further dissemination of SARS-CoV-2.

genomics↗
bioRxiv2020

Deep learning neural network prediction method improves proteome profiling of vascular sap of grapevines during Pierce's disease development

Plant secretome studies have shown the importance of plant defense proteins in the vascular system against pathogens. Studies on Pierces disease of grapevines caused by the xylem-limited bacteria Xylella fastidiosa (Xf) have detected proteins and pathways associated to its pathobiology. Despite the biological importance of the secreted proteins in the extracellular space to plant survival and development, proteome studies are scarce due to technical and technological challenges. Deep learning neural network prediction methods can provide powerful tools for improving proteome profiling by data-independent acquisition (DIA). We aimed to explore the potential of this strategy by combining it with in silico spectral library prediction tool, Prosit, to analyze the proteome of vascular leaf sap of grapevines with Pierces disease. The results demonstrate that the combination of DIA and Prosit increased the total number of identified proteins from 145 to 360 for grapevines and 18 to 90 for Xf. The new proteins increased the range of molecular weight, assisted on the identification of more exclusive peptides per protein, and increased the identification of low abundance proteins. These increases allowed the identification of new functional pathways associated with cellular responses to oxidative stress to be further investigated.

genomics↗