Biology subjects

Mahmoudi, M.

Publications and source records attributed to Mahmoudi, M..

26 records · Page 2Linked to original sources

bioRxiv2024

Mass spectrometry-based top-down proteomics in nanomedicine: proteoform-specific measurement of protein corona

Conventional mass spectrometry (MS)-based bottom-up proteomics (BUP) analysis of protein corona [i.e., an evolving layer of biomolecules, mostly proteins, formed on the surface of nanoparticles (NPs) during their interactions with biomolecular fluids] enabled nanomedicine community to partly identify the biological identity of NPs. Such an approach, however, fails pinpoint the specific proteoforms--distinct molecular variants of proteins, which is essential for prediction of the biological fate and pharmacokinetics of nanomedicines. Recognizing this limitation, this study pioneers a robust and reproducible MS-based top-down proteomics (TDP) technique for precisely characterizing proteoforms in the protein corona. Our TDP approach has successfully identified hundreds of proteoforms in the protein corona of polystyrene NPs, ranging from 3-70 kDa, revealing over 20 protein biomarkers with combinations of post-translational modifications, signal peptide cleavages, and/or truncations--details that BUP could not fully discern. This advancement in MS-based TDP offers a more comprehensive and exact characterization of NP protein coronas, deepening our understanding of NPs biological identities and potentially revolutionizing the field of nanomedicine.

systems biology↗

bioRxiv2024

Deep Plasma Proteome Profiling by Modulating Single Nanoparticle Protein Corona with Small Molecules

The protein corona, a dynamic biomolecular layer that forms on nanoparticle (NP) surfaces upon exposure to biological fluids is emerging as a valuable diagnostic tool for improving plasma proteome coverage analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients (namely, glucose, triglyceride, diglycerol, phosphatidylcholine, phosphatidylethanolamine, L--phosphatidylinositol, inosine 5'-monophosphate, and B complex), into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules (n=10) allowed for detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 {micro}g/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depleted the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. By employing an optimized data-independent acquisition (DIA) approach, the inclusion of phosphatidylcholine led to the detection of 1436 proteins in a single plasma sample. Our molecular dynamic results revealed that phosphatidylcholine interacts with albumin via hydrophobic interactions, h-bonds, and water-bridges. Addition of phosphatidylcholine also enabled the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. These significant achievements are made utilizing only a single NP type and one small molecule to analyze a single plasma sample, setting a new standard in plasma proteome profiling. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate widespread adoption of this methodology for identification and clinical translation of proteomic biomarkers into FDA approved diagnostics.

bioengineering↗

bioRxiv2023

Seasonality adaptation patterns of the natural Arabidopsis leaf microbiome over several plant generations are shaped by environmental factors

Leaf-associated microbial communities can promote plant health and resistance to biotic and abiotic stresses. However, the importance of environmental cues in the assembly of the leaf endo- and epi-microbiota remains elusive. Here we aimed to investigate the impact of seasonal environmental variations, on the establishment of the leaf microbiome, focusing on long-term changes (five years) in bacterial, fungal, and non-fungal eukaryotic communities colonizing the surface and endosphere of six wild Arabidopsis thaliana populations. While leaf-microbial communities were found to be highly stochastic, the leaf niche had a predominant importance with endophytic microbial communities consistently exhibiting a lower diversity and variability. Furthermore, our analyses revealed that among environmental factors, radiation and humidity-related factors are the most important drivers of diversity paderns in the leaf, albeit with stronger effects on epiphytic communities. Using linear models, we further identified 30 important genera whose relative abundance in leaf compartments could be modeled from environmental variables, suggesting specific niche preferences for these taxa. With the hypothesis that these environmental factors could impact interactions within microbial communities, we analyzed the seasonal paderns of microbial interaction networks across leaf compartments. We showed that epiphytic networks are more complex than endophytic, and that the complexity and connectivity of these networks are partially correlated with the mentioned environmental cues. Our results indicate that humidity and solar radiation function as major environmental cues shaping the phyllosphere microbiome at the micro-scale (leaf compartment) and macro-scale (site). These findings could have practical implications for selecting and developing field-adapted microbes in the face of, and for predicting microbial invasions in response to global change.

microbiology↗

bioRxiv2023

Causal inference for multiple risk factors and diseases from genomics data

Statistical causal learning in genome-wide association studies (GWAS) relies on the instrumental variable method of Mendelian Randomization (MR). Currently, an over-whelming number of MR studies purport to show causal relationships among a wide range of risk factors and outcomes. Here, we find that naive application of many recently proposed MR approaches results in numerous null relationships being discovered as highly significant. We show that a well-controlled error rate can be achieved through a graphical inference approach which: (i) selects a set of genetic instrumental variables (IVs) from GWAS summary static controlling for LD, linkage and pleiotropy; (ii) accommodates rare variants and binary outcomes in a principled way; (iii) distinguishes direct from indirect risk factors in very high-dimensional data; and (iv) identifies potential unobserved latent confounding. Only 20 minutes of wall-clock compute time is required for our Causal Inference GWAS (CI-GWAS) approach to jointly analyze a set of 9 common health risk factors, four common complex metabolic disease outcomes and 8.4M genetic variants recorded for 458,747 individuals in the UK Biobank. Genome-wide, we find that very few genetic variants are suitable MR IVs, with only 696 variants remaining when analyzing all traits jointly. While we replicate almost all paths previously found by CAUSE between risk factors and outcomes, we show that only few of these reflect direct adjacencies and that many cannot be distinguished from unmeasured confounding within the UK Biobank data. Our results suggest that well-curated longitudinal records and family data are likely needed to overcome the mixtures of temporal precedence and reverse-causality in biobank data. Our approach provides a first-step toward robust principled screening for potential causal links to understand the underlying nature of phenotypic correlations in biobank data.

genetics↗

bioRxiv2022

Multi-Omics Analysis of Magnetically Levitated Plasma Biomolecules

We recently discovered that superparamagnetic iron oxide nanoparticles (SPIONs) can levitate plasma biomolecules in the magnetic levitation (MagLev) system and cause formation of ellipsoidal biomolecular bands. To better understand the composition of the levitated biomolecules in various bands, we comprehensively characterized them by multi-omics analyses. To probe whether the biomolecular composition of the levitated ellipsoidal bands correlates with the health of plasma donors, we used plasma from individuals who had various types of multiple sclerosis (MS), as a model disease with significant clinical importance. Our findings reveal that, while the composition of proteins does not show much variability, there are significant differences in the lipidome and metabolome profiles of each magnetically levitated ellipsoidal band. By comparing the lipidome and metabolome compositions of various plasma samples, we found that the levitated biomolecular ellipsoidal bands do contain information on the health status of the plasma donors. More specifically, we demonstrate that there are particular lipids and metabolites in various layers of each specific plasma pattern that significantly contribute to the discrimination of different MS subtypes, i.e., relapsing-remitting MS (RRMS), secondary-progressive MS (SPMS), and primary-progressive MS (PPMS). These findings will pave the way for utilization of MagLev of biomolecules in biomarker discovery and diagnosis of this and other complex disorders.

bioengineering↗

bioRxiv2021

Development and characterization of influenza M2 ectodomain and/or HA stalk-based DC-targeting vaccines for different influenza infections

A universal influenza vaccine is required for broad protection against influenza infection. Here, we revealed the efficacy of novel influenza vaccine candidates based on Ebola glycoprotein (EboGP) DC-targeting domain (E{Delta}M) fusion protein technology. We fused influenza hemagglutinin stalk (HAcs) and extracellular matrix protein (M2e) or four copies of M2e (referred to as tetra M2e (tM2e)) with E{Delta}M to generate E{Delta}M-HM2e or E{Delta}M-tM2e, respectively, and revealed that E{Delta}M facilitates DC/macrophage targeting in vitro. In a mouse study, E{Delta}M-HM2e- or E{Delta}M-tM2e-pseudotyped viral particles (PVPs) induced significantly higher titers of anti-HA and/or anti-M2e antibodies. We also developed recombinant vesicular stomatitis virus (rVSV)-E{Delta}M-HM2e and rVSV-E{Delta}M-tM2e vaccines that resulted in rapid and potent induction of HA and/or M2 antibodies in mouse sera and mucosa. Importantly, vaccination protects mice from influenza H1N1 and H3N2 challenges. Taken together, our study suggests that recombinant rVSV-E{Delta}M-HM2e and rVSV-E{Delta}M-tM2e are efficacious and protective universal vaccines against influenza.

microbiology↗

bioRxiv2021

Life on leaves : uncovering temporal dynamics in Arabidopsis' leaf microbiota

Leaves are primarily responsible for the plants photosynthetic activity. Thus, changes in the phyllosphere microbiota, which includes deleterious and beneficial microbes, can have far reaching effects on plant fitness and productivity. In this context, identifying the processes and microorganisms that drive the changes in the leaf microbiota over a plants lifetime is crucial. In this study we analyzed the temporal dynamics in the leaf microbiota of Arabidopsis thaliana, integrating both compositional changes and changes in microbe-microbe interactions via the study of microbial networks. Field-grown Arabidopsis were used to follow leaf bacterial, fungal and oomycete communities, throughout the plants growing season (extending from November to March), over three consecutive years. Our results revealed the existence of conserved time patterns, with microbial communities and networks going through a stabilization phase (decreasing diversity and variability) at the beginning of the plants growing season. Despite a high turnover in these communities, we identified 19 core taxa persisting in Arabidopsis leaves across time and plant generations. With the hypothesis these microbes could be playing key roles in the structuring of leaf microbial communities, we conducted a time-informed microbial network analysis which showed core taxa are not necessarily highly connected network hubs and hubs alternate with time. Our study shows that leaf microbial communities exhibit reproducible dynamics and patterns, suggesting it could be possible to predict and drive these microbial communities to desired states.

microbiology↗

bioRxiv2020

Prunella vulgaris extract and suramin block SARS-coronavirus 2 virus Spike protein D614 and G614 variants mediated receptor association and virus entry in cell culture system

Until now, no approved effective vaccine and antiviral therapeutic are available for treatment or prevention of SARS-coronavirus 2 (SCoV-2) virus infection. In this study, we established a SCoV-2 Spike glycoprotein (SP), including a SP mutant D614G, pseudotyped HIV-1-based vector system and tested their ability to infect ACE2-expressing cells. This study revealed that a C-terminal 17 amino acid deletion in SCoV-2 SP significantly increases the incorporation of SP into the pseudotyped viruses and enhanced its infectivity, which may be helpful in the design of SCoV2-SP-based vaccine strategies. Moreover, based on this system, we have demonstrated that an aqueous extract from the Chinese herb Prunella vulgaris (CHPV) and a compound, suramin, displayed potent inhibitory effects on both wild type and mutant (G614) SCoV-2 SP pseudotyped virus (SCoV-2-SP-PVs)-mediated infection. The 50% inhibitory concentration (IC50) for CHPV and suramin on SCoV-2-SP-PVs are 30, and 40 g/ml, respectively. To define the mechanisms of their actions, we demonstrated that both CHPV and suramin are able to directly interrupt SCoV-2-SP binding to its receptor ACE2 and block the viral entry step. Importantly, our results also showed that CHPV or suramin can efficiently reduce levels of cytopathic effect caused by SARS-CoV-2 virus (hCoV-19/Canada/ON-VIDO-01/2020) infection in Vero cells. Furthermore, our results demonstrated that the combination of CHPV/suramin with an anti-SARS-CoV-2 neutralizing antibody mediated more potent blocking effect against SCoV2-SP-PVs. Overall, this study provides evidence that CHPV and suramin has anti-SARS-CoV-2 activity and may be developed as a novel antiviral approach against SARS-CoV-2 infection.

microbiology↗