Biology subjects

Samra, S.

Publications and source records attributed to Samra, S..

4 recordsLinked to original sources

bioRxiv2026

Deep nanoparticle protein corona plasma proteomics resolves a stage-specific peripheral signature of Alzheimer's disease

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSAlzheimers disease (AD) progresses over decades, yet plasma biomarkers that resolve disease stage rather than simply detect disease remain scarce. This distinction is clinically consequential because effective AD intervention depends on identifying patients before disease biology has progressed beyond a therapeutically responsive stage. METHODSWe used small-molecule-modulated protein corona proteomics to profile plasma from 90 individuals in the Australian Imaging, Biomarker and Lifestyle cohort, stratified by Centiloid (CL) A{beta}-amyloid burden (30 amyloid- negative, CL < 15; 30 moderate-to-high, CL 26 to 100; 30 very high, CL > 100). We quantified 3,176 proteins and applied differential abundance and actual causality analyses to identify stage-specific and candidate causal proteins. RESULTSDifferential protein abundance was exclusively captured during the moderate-to-high AD transition, revealing a discrete proteomic "switch." The switch was marked by accumulation of the autophagy receptor CALCOCO1, together with coordinated depletion of the S100A8/S100A9 calprotectin complex and core erythroid-cytoskeletal network structural markers (e.g., SPTA1, SPTB, ANK1). Adhesion G protein-coupled receptor G6 (ADGRG6) showed a significant moderate positive monotonic association with absolute CL burden, providing a proportional molecular anchor for cumulative disease burden. Actual causality analysis identified COL6A2, FOXRED2, P3H1, PRR4, and GOLGA5 as candidate upstream drivers linking matrix remodeling, Golgi trafficking, and collagen processing to AD progression. DISCUSSIONThese findings suggest a candidate blood-accessible framework for staging AD by active disease biology, which, if replicated in independent cohorts, may have implications for therapeutic selection and mechanism-guided clinical trials. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/740710v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1ead718org.highwire.dtl.DTLVardef@cfa523org.highwire.dtl.DTLVardef@62a367org.highwire.dtl.DTLVardef@1d5c9a5_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

A Single-Aliquot, Enrichment-Free Workflow for High-Throughput Plasma Proteome and N-Glycoproteome Profiling

High cancer mortality rates highlight an urgent need for early detection. Plasma proteomics and glycoproteomics provide minimally invasive routes for biomarker discovery, yet achieving an optimal balance among profiling depth, throughput, and longitudinal reproducibility remains a major challenge for clinical application and translation. To overcome this bottleneck, we present a single-aliquot, enrichment-free, paired-run dual-omics pipeline that concurrently profiles the global plasma proteome and N-glycoproteome from unenriched plasma. Following depletion of top14 abundant plasma proteins, we implemented sequential 23-min narrow-window data-independent acquisition (DIA) and 42-min stepped-collision-energy data-dependent (SCE-DDA) runs from the same plasma digest, delivering a clinical throughput of [~]24 patients/day with deep proteome coverage of 3,756{+/-}413 protein groups (PGs) and 1,226{+/-}78 glycopeptides per sample, including 303 FDA-approved drug targets. Cross-platform benchmarking with a previous generation instrument demonstrated significantly faster (>10-20 fold) profiling speed to achieve 113 PGs/min and high protein abundance reproducibility (Pearson r > 0.9), confirming cross-instrument transferability. Application to a 300-participant lung cohort (cancer, LDCT-detected non-cancer nodules, and controls) revealed differential expression of S100 and annexin family proteins between cancer and nodules. Paired glycoproteomic analysis (n=30) identified site-specific N-glycosylation alterations in FN1, IGHG2, C3, and MET independent of total protein abundance, uncovering additional biomarker candidates for early lung cancer detection. Together, this dual-omics strategy enables deep, scalable, and reproducible plasma analysis, supporting longitudinal biomarker discovery and validation across instruments and laboratories.

biochemistry↗

bioRxiv2025

Whole blood proteome dynamics defines predictive diagnostic and prognostic signatures of cryptococcal infection

Across the globe, fungi are impacting the lives of millions of people through the development of infections ranging from superficial to systemic with limited treatment options. To effectively combat fungal disease, rapid and reliable diagnostic methods are required, including current methodologies using antigen detection, culturing, microscopy, and molecular tools. However, the flexibility of these platforms to diagnose infection using non-invasive methods and predict the outcome of disease are limited. In this study, we apply state-of-the-art mass spectrometry-based proteomics to perform dual perspective (i.e., host and pathogen) profiling of cryptococcal infection. Whole blood collected over a temporal scale following murine model challenged with the human fungal pathogen, Cryptococcus neoformans, detected >3,000 host proteins and 160 fungal proteins. From the host perspective, temporal regulation of known immune-associated proteins, including eosinophil peroxidase and lipocalin-2, along with suppression of lipoproteins, demonstrated infection- and time-dependent host remodeling. Conversely, from the pathogen perspective, known and putative virulence-associated proteins were detected, including proteins associated with fungal extracellular vesicles and host immune modulation. We also observed and validated a new mechanism of immune system response to C. neoformans through modulation of haptoglobin. Further, we assessed the predictive power of dual perspective proteome profiling toward prognostics of cryptococcal infection and report a previously undisclosed integration among virulence factor production, immune system modulation, and individual model survival. Together, our findings pose novel biomarkers of cryptococcal infection from whole blood and highlight the potential of personal proteome profiles to determine the prognosis of cryptococcal infection, a new parameter in fungal disease management.

microbiology↗

bioRxiv2024

Mutations in PSEN1 predispose inflammation in an astrocyte model of familial Alzheimer's disease through disrupted regulated intramembrane proteolysis

Mutations in PSEN1 cause familial Alzheimers disease with almost complete penetrance. Age at onset is highly variable between different PSEN1 mutations and even within families with the same mutation. Current research into late onset Alzheimers disease implicates inflammation in both disease onset and progression. PSEN1 is the catalytic subunit of {gamma}-secretase, responsible for regulated intramembrane proteolysis of numerous substrates that include cytokine receptors. For this reason, we tested the hypothesis that mutations in PSEN1 impact inflammatory responses in astrocytes, thereby contributing to disease progression. Here, using iPSC-astrocytes, we show that PSEN1 is upregulated in response to inflammatory stimuli, and this upregulation is disrupted by pathological PSEN1 mutations. Using transcriptomic analyses, we demonstrate that PSEN1 mutant astrocytes have an augmented inflammatory profile in their basal state, concomitant with an upregulation of genes coding for regulated intramembrane proteolytic and robust activation of JAK-STAT signalling. Using JAK-STAT2 as an example signalling pathway, we show altered phosphorylation cascades in PSEN1 mutant astrocytes, reinforcing the notion of altered cytokine signalling cascades. Finally, we use small molecule modulators of {gamma}-secretase to confirm a role for PSEN1/{gamma}-secretase in regulating the astrocytic response to inflammatory stimuli. Together, these data suggest that mutations in PSEN1 enhance cytokine signalling via impaired regulated intramembrane proteolysis, thereby predisposing astrocytic inflammatory profiles. These findings support a two-hit contribution of PSEN1 mutations to fAD pathogenesis, not only impacting APP and A{beta} processing but also altering the cellular response to inflammation.

neuroscience↗