Increasing and sustaining discharges by noon - a multi-year process improvement project.

High hospital occupancy degrades emergency department performance by increasing wait times, decreasing patient satisfaction, and increasing patient morbidity and mortality. Late discharges contribute to high hospital occupancy by increasing emergency department (ED) patient length of stay (LOS). We share our experience with increasing and sustaining early discharges at a 650-bed academic medical center in the United States. Our process improvement project followed the Institute of Medicine Model for Improvement of successive Plan‒Do‒Study‒Act cycles. We implemented multiple iterative interventions over 41 months. As a result, the proportion of discharge orders before 10 am increased from 8.7% at baseline to 22.2% (p < 0.001), and the proportion of discharges by noon (DBN) increased from 9.5% to 26.8% (p < 0.001). There was no increase in balancing metrics because of our interventions. RA-LOS (Risk Adjusted Length Of Stay) decreased from 1.16 to 1.09 (p = 0.01), RA-Mortality decreased from 0.65 to 0.61 (p = 0.62) and RA-Readmissions decreased from 0.92 to 0.74 (p < 0.001). Our study provides a roadmap to large academic facilities to increase and sustain the proportion of patients discharged by noon without negatively impacting LOS, 30-day readmissions, and mortality. Continuous performance evaluation, adaptability to changing resources, multidisciplinary engagement, and institutional buy-in were crucial drivers of our success.

New C-indices for assessing importance of longitudinal biomarkers in fitting competing risks survival data in the presence of partially masked causes.

Competing risks survival data in the presence of partially masked causes are frequently encountered in medical research or clinical trials. When longitudinal biomarkers are also available, it is of great clinical importance to examine associations between the longitudinal biomarkers and the cause-specific survival outcomes. In this article, we propose a cause-specific C-index for joint models of longitudinal and competing risks survival data accounting for masked causes. We also develop a posterior predictive algorithm for computing the out-of-sample cause-specific C-index using Markov chain Monte Carlo samples from the joint posterior of the in-sample longitudinal and competing risks survival data. We further construct the Δ $$ \Delta $$ C-index to quantify the strength of association between the longitudinal and cause-specific survival data, or between the out-of-sample longitudinal and survival data. Empirical performance of the proposed assessment criteria is examined through an extensive simulation study. An in-depth analysis of the real data from large cancer prevention trials is carried out to demonstrate the usefulness of the proposed methodology.

Prediction of future risk of any and higher-grade prostate cancer based on the PLCO and SELECT trials.

BACKGROUND: A model was built that characterized effects of individual factors on five-year prostate cancer (PCa) risk in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial (PLCO) and the Selenium and Vitamin E Cancer Prevention Trial (SELECT). This model was validated in a third San Antonio Biomarkers of Risk (SABOR) screening cohort. METHODS: A prediction model for 1- to 5-year risk of developing PCa and Gleason > 7 PCa (HG PCa) was built on PLCO and SELECT using the Cox proportional hazards model adjusting for patient baseline characteristics. Random forests and neural networks were compared to Cox proportional hazard survival models, using the trial datasets for model building and the SABOR cohort for model evaluation. The most accurate prediction model is included in an online calculator. RESULTS: The respective rates of PCa were 8.9%, 7.2%, and 11.1% in PLCO (n = 31,495), SELECT (n = 35,507), and SABOR (n = 1790) over median follow-up of 11.7, 8.1 and 9.0 years. The Cox model showed higher prostate-specific antigen (PSA), BMI and age, and African American race to be associated with PCa and HGPCa. Five-year risk predictions from the combined SELECT and PLCO model effectively discriminated risk in the SABOR cohort with C-index 0.76 (95% CI [0.72, 0.79]) for PCa, and 0.74 (95% CI [0.65,0.83]) for HGPCa. CONCLUSIONS: A 1- to 5-year PCa risk prediction model developed from PLCO and SELECT was validated with SABOR and implemented online. This model can individualize and inform shared screening decisions.

Joint modelling of longitudinal and survival data in the presence of competing risks with applications to prostate cancer data.

This research is motivated from the data from a large Selenium and Vitamin E Cancer Prevention Trial (SELECT). The prostate specific antigens (PSAs) were collected longitudinally, and the survival endpoint was the time to low-grade cancer or the time to high-grade cancer (competing risks). In this article, the goal is to model the longitudinal PSA data and the time-to-prostate cancer (PC) due to low- or high-grade. We consider the low-grade and high-grade as two competing causes of developing PC. A joint model for simultaneously analysing longitudinal and time-to-event data in the presence of multiple causes of failure (or competing risk) is proposed within the Bayesian framework. The proposed model allows for handling the missing causes of failure in the SELECT data and implementing an efficient Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via a novel reparameterization technique. Bayesian criteria, ΔDICSurv, and ΔWAICSurv, are introduced to quantify the gain in fit in the survival sub-model due to the inclusion of longitudinal data. A simulation study is conducted to examine the empirical performance of the posterior estimates as well as ΔDICSurv and ΔWAICSurv and a detailed analysis of the SELECT data is also carried out to further demonstrate the proposed methodology.

Joint analysis of single-cell and bulk tissue sequencing data to infer intratumor heterogeneity.

Many computational methods have been developed to discern intratumor heterogeneity (ITH) using DNA sequence data from bulk tumor samples. These methods share an assumption that two mutations arise from the same subclone if they have similar mutant allele-frequencies (MAFs), and thus it is difficult or impossible to distinguish two subclones with similar MAFs. Single-cell DNA sequencing (scDNA-seq) data can be very informative for ITH inference. However, due to the difficulty of DNA amplification, scDNA-seq data are often very noisy. A promising new study design is to collect both bulk and single-cell DNA-seq data and jointly analyze them to mitigate the limitations of each data type. To address the analytic challenges of this new study design, we propose a computational method named BaSiC (Bulk tumor and Single Cell), to discern ITH by jointly analyzing DNA-seq data from bulk tumor and single cells. We demonstrate that BaSiC has comparable or better performance than the methods using either data type. We further evaluate BaSiC using bulk tumor and single-cell DNA-seq data from a breast cancer patient and several leukemia patients.

Growth recovery lines: a specific indicator of child abuse and neglect?

BACKGROUND: Growth recovery lines are radiodense lines in long bones reported to be indicators of stress. OBJECTIVE: The purpose of this study was to understand the distribution, quantity and associations of growth recovery lines in children ages 0-24 months with high and low risk for child maltreatment. MATERIALS AND METHODS: We conducted a retrospective cohort study of children ages 0-24 months who had skeletal surveys and an assessment for maltreatment. Growth recovery lines, fractures and osteopenia were assessed independently by two pediatric radiologists blinded to the abuse likelihood. RESULTS: Of the 135 children in this study, 58 were in the low-risk group, 26 were in the neglect group, and 51 were in the physical abuse group. Children in the neglected and physically abused groups had 1.73 times (95% confidence interval [CI] of 1.16, 2.59), P=0.007) and 1.84 times (95% CI 1.28, 2.63, P<0.001) more growth recovery lines than the low-risk group, respectively. Growth recovery lines occurred at an earlier age in the neglect group (age interaction P=0.03) and abuse group (age interaction P=0.01) compared to the low-risk group. The specificity for maltreatment in children with at least 10 growth recovery lines in the long bones was greater than 84%, while sensitivity was less than 35%. The most common locations for growth recovery lines were distal radius, proximal tibia and distal tibia. CONCLUSION: In the absence of a known major stressor, physical abuse and neglect should be considered in children younger than 24 months with at least 10 growth recovery lines.

Does assisted hatching affect live birth in fresh, first cycle in vitro fertilization in good and poor prognosis patients?

PURPOSE: To assess the effect of assisted hatching (AH) on live birth rate (LBR) in first cycle, fresh in vitro fertilization (IVF) in good and poor prognosis patients. METHODS: Retrospective cohort using cycles reported to the Society for Assisted Reproductive Technology Clinic Outcomes Reporting System. Live birth rate was compared in women who underwent first cycle, autologous, fresh IVF cycles with (n = 48,858) and without (n = 103,413) AH from 2007 to 2015. RESULTS: The propensity-weighted LBR was 39.2% with AH versus 43.9% without AH in all patients. The rate difference (RD) with AH was - 4.7% ([CI - 0.053, - 0.040], P < 0.001) with the calculated number needed to harm being 22. AH affected live birth in both good prognosis and poor prognosis patients. The propensity-weighted monozygotic twinning (MZT) rate was 2.3% in patients treated with AH as compared to 1.2% patients that did not receive AH. The RD with AH on MZT in fresh, first IVF cycles was 1.1% ([0.008, 0.014], P < 0.001). CONCLUSION: AH may affect LBR across all patients and in poor prognosis patients in fresh IVF cycles. Caution should be exercised when applying this technology. More prospective research is needed.

Dynamic macrophage polarization-specific miRNA patterns reveal increased soluble VEGF receptor 1 by miR-125a-5p inhibition.

Dynamic, epigenetic mechanisms can regulate macrophage phenotypes following exposure to different stimulating conditions and environments. However, temporal patterns of microRNAs (miRNAs or miRs) across multiple macrophage polarization phenotypes have not been defined. We determined miRNA expression in bone marrow-derived murine macrophages over multiple time points (0.5, 1, 3, 24 h) following exposure to cytokines and/or LPS. We hypothesized that dynamic changes in miRNAs regulate macrophage phenotypes. Changes in macrophage polarization markers were detected as early as 0.5 and as late as 24 h; however, robust responses for most markers occurred within 3 h. In parallel, many polarization-specific miRNAs were also changed by 3 h and expressed divergent patterns between M1 and M2a conditions, with increased expression in M1 (miR-155, 199a-3p, 214-3p, 455-3p, and 125a) or M2a (miR-511 and 449a). Specifically, miR-125a-5p exhibited divergent patterns: increased at 12-24 h in M1 macrophages and decreasing trend in M2a. VEGF in the culture media of macrophages was dependent upon the polarization state, with greatly diminished VEGF in M2a compared with M1 macrophage culture media despite similar VEGF in cell lysates. Inhibition of miR-125a-5p in media-only controls (MO) and M1 macrophages greatly increased expression and secretion of soluble VEGF receptor-1 (sVEGFR1) leading to diminished VEGF in the culture media, partially converting MO and M1 into an M2a phenotype. Thus, the divergent expression patterns of polarization-specific miRNAs led to the identification and demonstrated the regulation of a specific macrophage polarization phenotype, sVEGFR1 by inhibition of miR-125a-5p.

Homology cluster differential expression analysis for interspecies mRNA-Seq experiments.

There is an increasing demand for exploration of the transcriptomes of multiple species with extraordinary traits such as the naked-mole rat (NMR). The NMR is remarkable because of its longevity and resistance to developing cancer. It is of scientific interest to understand the molecular mechanisms that impart these traits, and RNA-sequencing experiments with comparator species can correlate transcriptome dynamics with these phenotypes. Comparing transcriptome differences requires a homology mapping of each transcript in one species to transcript(s) within the other. Such mappings are necessary, especially if one species does not have well-annotated genome available. Current approaches for this type of analysis typically identify the best match for each transcript, but the best match analysis ignores the inherent risks of mismatch when there are multiple candidate transcripts with similar homology scores. We present a method that treats the set of homologs from a novel species as a cluster corresponding to a single gene in the reference species, and we compare the cluster-based approach to a conventional best-match analysis in both simulated data and a case study with NMR and mouse tissues. We demonstrate that the cluster-based approach has superior power to detect differential expression.

Pharmacokinetic interaction of rifapentine and raltegravir in healthy volunteers.

OBJECTIVES: Latent tuberculosis infection and tuberculosis disease are prevalent worldwide. However, antimycobacterial rifamycins have drug interactions with many antiretroviral drugs. We evaluated the effect of rifapentine on the pharmacokinetic properties of raltegravir. METHODS: In this open-label, fixed-sequence, three-period study, 21 healthy volunteers were given: raltegravir alone (400 mg every 12 h for 4 days) on days 1-4 of Period 1; rifapentine (900 mg once weekly for 3 weeks) on days 1, 8 and 15 of Period 2 and raltegravir (400 mg every 12 h for 4 days) on days 12-15 of Period 2; and rifapentine (600 mg once daily for 10 scheduled doses) on days 1, 4-8 and 11-14 of Period 3 and raltegravir (400 mg every 12 h for 4 days) on days 11-14 of Period 3. Plasma raltegravir concentrations were measured. ClinicalTrials.gov database: NCT00809718. RESULTS: In 16 subjects who completed the study, coadministration of raltegravir with rifapentine (900 mg once weekly; Period 2) compared with raltegravir alone resulted in the geometric mean of the raltegravir AUC from 0 to 12 h (AUC0-12) being increased by 71%; the peak concentration increased by 89% and the trough concentration decreased by 12%. Coadministration of raltegravir with rifapentine in Period 3 did not change the geometric mean of the raltegravir AUC0-12 or the peak concentration, but it decreased the trough concentration by 41%. Raltegravir coadministered with rifapentine was generally well tolerated. CONCLUSIONS: The increased raltegravir exposure observed with once-weekly rifapentine was safe and tolerable. Once-weekly rifapentine can be used with raltegravir to treat latent tuberculosis infection in patients who are infected with HIV.

The utility and impact of digital endpoints for improving breast cancer outcomes.

INTRODUCTION: In breast cancer clinical trials utilizing digital endpoints, wearable sensors record participants' health information during activities of daily living.  These sensors are worn on the wrist or finger, placed as a skin patch or headband, or embedded on clothing. Data collected from wearable sensors form the basis of a digital endpoint, useful for determining effects of novel treatments on health outcomes, particularly quality-of-life outcomes. AREAS COVERED: References for this article were selected from a PubMed search spanning from 1 January 2017,to 1 July 2024, using the terms 'wearable sensors,' 'digital endpoints,' 'virtualtrials,' 'breast cancer.'  Additional articles from the authors' personal collection of papers and reviewers suggestions were also used. EXPERT OPINION: Digital endpoints must be validated as proper surrogate measures for healthcare outcomes, prior to their use in breast cancer trials.  Wearable sensors may introduce biases, such as 'missing not-at-random bias,' and perhaps even exacerbate disparities in healthcare outcomes if patients not comfortable with their use are excluded from clinical trials, or if the accuracy of sensors varies between racial and ethnic groups. Therefore, before embarking on trials with digital endpoints, validation studies are required, and limitations and risks of such trials need to be addressed.

Facilitators of and barriers to gastric cancer and precursor diagnosis among South Texas residents: Social determinants of health.

BACKGROUND: Latinos/Hispanics are at higher risk for developing gastric cancer (GC) compared with non-Hispanic whites, and social determinants of health (SDoH) are thought to contribute. AIMS/MATERIALS AND METHODS: This study addressed SDoH and their interactions contributing to disparities in the testing and treatment of Helicobacter pylori (HP) infection and diagnosis of GC and its known precursors, among Latinos/Hispanics relative to non-Latinos at two affiliated but independent health systems in San Antonio, Texas, using a mixed methods approach. RESULTS: Secondary data abstraction and analysis showed that GCs represented 2.6% (n = 600) of our population. Men and older individuals were at higher GC risk. Individuals with military insurance were 2.7 times as likely to be diagnosed as private insurance. Latinos/Hispanics had significantly (24%) higher GC risk than Whites. Poverty and lack of insurance contributed to GC risk among the minorities classified as other (Asians, Native Americans, Multiracial; all p < 0.01). All SDoH were associated with H. pylori infection (p < 0.001). Qualitative analysis of patient and provider interviews showed providers reporting insurance as a major care barrier; patients reported appointment delays, and lack of clinic staff. Providers universally agreed treatment of H. pylori was necessary, but disagreed on its prevalence. Patients did not report discussing H. pylori or its cancer risk with providers. DISCUSSION/CONCLUSION: These data indicate the importance of considering SDoH in diagnosis and treatment of GC and its precursors, and educating providers and patients on H. pylori risks for GC.

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.

Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

Erratum: Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration - CORRIGENDUM.

[This corrects the article DOI: 10.1017/cts.2024.2.].

Differential expression analysis with global network adjustment.

BACKGROUND: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene's expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments. RESULTS: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient "over-shrinkage" method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods. CONCLUSIONS: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.

The RNA-binding protein Musashi1 affects medulloblastoma growth via a network of cancer-related genes and is an indicator of poor prognosis.

Musashi1 (Msi1) is a highly conserved RNA-binding protein that is required during the development of the nervous system. Msi1 has been characterized as a stem cell marker, controlling the balance between self-renewal and differentiation, and has also been implicated in tumorigenesis, being highly expressed in multiple tumor types. We analyzed Msi1 expression in a large cohort of medulloblastoma samples and found that Msi1 is highly expressed in tumor tissue compared with normal cerebellum. Notably, high Msi1 expression levels proved to be a sign of poor prognosis. Msi1 expression was determined to be particularly high in molecular subgroups 3 and 4 of medulloblastoma. We determined that Msi1 is required for tumorigenesis because inhibition of Msi1 expression by small-interfering RNAs reduced the growth of Daoy medulloblastoma cells in xenografts. To characterize the participation of Msi1 in medulloblastoma, we conducted different high-throughput analyses. Ribonucleoprotein immunoprecipitation followed by microarray analysis (RIP-chip) was used to identify mRNA species preferentially associated with Msi1 protein in Daoy cells. We also used cluster analysis to identify genes with similar or opposite expression patterns to Msi1 in our medulloblastoma cohort. A network study identified RAC1, CTGF, SDCBP, SRC, PRL, and SHC1 as major nodes of an Msi1-associated network. Our results suggest that Msi1 functions as a regulator of multiple processes in medulloblastoma formation and could become an important therapeutic target.

Mood disorders in individuals with distal 18q deletions.

We examined 36 participants at least 4 years old with hemizygous distal deletions of the long arm of Chromosome 18 (18q-) for histories of mood disorders and to characterize these disorders clinically. Since each participant had a different region of 18q hemizygosity, our goal was also to identify their common region of hemizygosity associated with mood disorders; thereby identifying candidate causal genes in that region. Lifetime mood and other psychiatric disorders were determined by semi-structured interviews of patients and parents, supplemented by reviews of medical and psychiatric records, and norm-referenced psychological assessment instruments, for psychiatric symptoms, cognitive problems, and adaptive functioning. Sixteen participants were identified with lifetime mood disorders (ages 12-42 years, 71% female, 14 having had unipolar depression and 2 with bipolar disorders). From the group of 20 who did not meet criteria for a mood disorder; a comparison group of 6 participants were identified who were matched for age range and deletion size. Mood-disordered patients had high rates of anxiety (75%) and externalizing behavior disorders (44%), and significant mean differences from comparison patients (P < 0.05), including higher overall and verbal IQs and lower autistic symptoms. A critical region was defined in the mood-disordered group that included a hypothetical gene, C18orf62, and two known genes, ZADH2 and TSHZ1. We conclude that patients having terminal deletions of this critical region of the long arm of Chromosome 18 are highly likely to have mood disorders, which are often comorbid with anxiety and to a lesser extent with externalizing disorders.

MiR-351 transiently increases during muscle regeneration and promotes progenitor cell proliferation and survival upon differentiation.

MicroRNAs (miRNAs) regulate many biological processes including muscle development. However, little is known regarding miRNA regulation of muscle regeneration. Murine tibialis anterior muscle was evaluated after cardiotoxin-induced injury and used for global miRNA expression analysis. From day 1 through day 21 following injury, 298 miRNAs were significantly changed at least at one time point, including 86 miRNAs that were altered >10-fold compared with uninjured skeletal muscle. Temporal miRNA expression patterns included inflammation-related miRNAs (miR-223 and -147) that increased immediately after injury; this pattern contrasted to that of mature muscle-specific miRNAs (miR-1, -133a, and -499) that abruptly decreased following injury followed by upregulation in later regenerative events. Another cluster of miRNAs were transiently increased in the early days of muscle regeneration including miR-351, a miRNA that was also transiently expressed during myogenic progenitor cell (MPC) differentiation in vitro. Based on computational predictions, further studies demonstrated that E2f3 was a target of miR-351 in myoblasts. Moreover, knockdown of miR-351 expression inhibited MPC proliferation and promoted apoptosis during MPC differentiation, whereas miR-351 overexpression protected MPC from apoptosis during differentiation. Collectively, these observations suggest that miR-351 is involved in both the maintenance of MPC proliferation and the transition into differentiated myotubes. Thus, a novel, time-dependent sequence of molecular events during muscle regeneration has been identified; miR-351 inhibits E2f3 expression, a key regulator of cell cycle progression and proliferation, and promotes MPC proliferation and protects early differentiating MPC from apoptosis, important events in the hostile tissue environment after acute muscle injury.

Immunoenrichment microwave and magnetic proteomics for quantifying CD47 in the experimental autoimmune encephalomyelitis model of multiple sclerosis.

We hypothesized that quantitative MS/MS-based proteomics at multiple time points, incorporating immunoenrichment prior to rapid microwave and magnetic (IM(2) ) sample preparation, might enable correlation of the relative expression of CD47 and other low abundance proteins to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, anti-CD47 antibodies were used to enrich for low abundance CD47 prior to microwave and magnetic proteomics in EAE. Decoding protein expression at each time point, with CD47-immunoenriched samples and targeted proteomic analysis, enabled peptides from the low abundance proteins to be precisely quantified throughout disease progression, including: CD47: 86-99, corresponding to the "marker of self" overexpressed by myelin that prevents phagocytosis, or "cellular devouring," by microglia and macrophages; myelin basic protein: 223-228, corresponding to myelin basic protein; and migration inhibitory factor: 79-87, corresponding to a proinflammatory cytokine that inhibits macrophage migration. While validation in a larger cohort is underway, we conclude that IM(2) proteomics is a rapid method to precisely quantify peptides from CD47 and other low abundance proteins throughout disease progression in EAE. This is likely due to improvements in selectivity and sensitivity, necessary to partially overcome masking of low abundance proteins by high abundance proteins and improve dynamic range.

Microwave and magnetic (M(2) ) proteomics of the experimental autoimmune encephalomyelitis animal model of multiple sclerosis.

We hypothesized that quantitative MS/MS-based proteomics at multiple time points, incorporating rapid microwave and magnetic (M(2) ) sample preparation, could enable relative protein expression to be correlated to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, microwave-assisted reduction/alkylation/digestion of proteins from brain tissue lysates bound to C8 magnetic beads and microwave-assisted isobaric chemical labeling were performed of released peptides, in 90 s prior to unbiased proteomic analysis. Disease progression in EAE was assessed by scoring clinical EAE disease severity and confirmed by histopathologic evaluation for central nervous system inflammation. Decoding the expression of 283 top-ranked proteins (p <0.05) at each time point relative to their expression at the peak of disease, from a total of 1191 proteins observed in four technical replicates, revealed a strong statistical correlation to EAE disease score, particularly for the following four proteins that closely mirror disease progression: 14-3-3ε (p = 3.4E-6); GPI (p = 2.1E-5); PLP1 (p = 8.0E-4); PRX1 (p = 1.7E-4). These results were confirmed by Western blotting, signaling pathway analysis, and hierarchical clustering of EAE risk groups. While validation in a larger cohort is underway, we conclude that M(2) proteomics is a rapid method to quantify putative prognostic/predictive protein biomarkers and therapeutic targets of disease progression in the EAE animal model of multiple sclerosis.