Microwave & magnetic (M2) proteomics reveals CNS-specific protein expression waves that precede clinical symptoms of experimental autoimmune encephalomyelitis

CNS Resident Innate Immune Cells: Guardians of CNS Homeostasis

Although the CNS has been considered for a long time an immune-privileged organ, it is now well known that both the parenchyma and non-parenchymal tissue (meninges, perivascular space, and choroid plexus) are richly populated in resident immune cells. The advent of more powerful tools for multiplex immunophenotyping, such as single-cell RNA sequencing technique and upscale multiparametric flow and mass spectrometry, helped in discriminating between resident and infiltrating cells and, above all, the different spectrum of phenotypes distinguishing border-associated macrophages. Here, we focus our attention on resident innate immune players and their primary role in both CNS homeostasis and pathological neuroinflammation and neurodegeneration, two key interconnected aspects of the immunopathology of multiple sclerosis.

Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis

Multiple sclerosis (MS) is a neuroinflammatory disorder characterized by autoimmune-mediated inflammatory lesions in CNS leading to myelin damage and axonal loss. MS is a heterogenous disease with variable and unpredictable disease course. Due to its complex nature, MS is difficult to diagnose and responses to specific treatments may vary between individuals. Therefore, there is an indisputable need for biomarkers for early diagnosis, prediction of disease exacerbations, monitoring the progression of disease, and for measuring responses to therapy. Genomic and proteomic studies have sought to understand the molecular basis of MS and find biomarker candidates. Advances in next-generation sequencing and mass-spectrometry techniques have yielded an unprecedented amount of genomic and proteomic data; yet, translation of the results into the clinic has been underwhelming. This has prompted the development of novel data science techniques for exploring these large datasets to identify biologically relevant relationships and ultimately point towards useful biomarkers. Herein we discuss optimization of omics study designs, advances in the generation of omics data, and systems biology approaches aimed at improving biomarker discovery and translation to the clinic for MS.

Microwave & magnetic proteomics of macrophages from patients with HIV-associated cognitive impairment

OBJECTIVE

HIV-infected monocytes can infiltrate the blood brain barrier as differentiated macrophages to the central nervous system, becoming the primary source of viral and cellular neurotoxins. The final outcome is HIV-associated cognitive impairment (HACI), which remain prevalent today, possibly due to the longer life-span of the patients treated with combined anti-retroviral therapy. Our main goal was to characterize the proteome of monocyte-derived macrophages (MDM) from HACI patients, and its association with their cognitive status, to find novel targets for therapy.

METHODS

MDM were isolated from the peripheral blood of 14 HIV-seropositive women characterized for neurocognitive function, including: four normal cognition (NC), five asymptomatic (A), and five with cognitive impaired (CI). Proteins from macrophage lysates were isobaric-labeled with the microwave and magnetic (M2) sample preparation method followed by liquid chromatography-tandem mass spectrometry-based protein identification and quantification. Differences in protein abundance across groups classified by HACI status were determined using analysis of variance.

RESULTS

A total of 2,519 proteins were identified with 2 or more peptides and 28 proteins were quantified as differentially expressed. Statistical analysis revealed increased abundance of 17 proteins in patients with HACI (p<0.05), including several enzymes associated to the glucose metabolism. Western blot confirmed increased expression of 6-Phosphogluconate dehydrogenase and L-Plastin in A and CI patients over NC and HIV seronegatives.

CONCLUSIONS

This is the first quantitative proteomics study exploring the changes in protein abundance of macrophages isolated from patients with HACI. Further studies are warranted to determine if these proteins may be target candidates for therapy development against HACI.

Serum Neuroinflammatory Disease-Induced Central Nervous System Proteins Predict Clinical Onset of Experimental Autoimmune Encephalomyelitis

There is an urgent need in multiple sclerosis (MS) patients to develop biomarkers and laboratory tests to improve early diagnosis, predict clinical relapses, and optimize treatment responses. In healthy individuals, the transport of proteins across the blood–brain barrier (BBB) is tightly regulated, whereas, in MS, central nervous system (CNS) inflammation results in damage to neuronal tissues, disruption of BBB integrity, and potential release of neuroinflammatory disease-induced CNS proteins (NDICPs) into CSF and serum. Therefore, changes in serum NDICP abundance could serve as biomarkers of MS. Here, we sought to determine if changes in serum NDICPs are detectable prior to clinical onset of experimental autoimmune encephalomyelitis (EAE) and, therefore, enable prediction of disease onset. Importantly, we show in longitudinal serum specimens from individual mice with EAE that pre-onset expression waves of synapsin-2, glutamine synthetase, enolase-2, and synaptotagmin-1 enable the prediction of clinical disease with high sensitivity and specificity. Moreover, we observed differences in serum NDICPs between active and passive immunization in EAE, suggesting hitherto not appreciated differences for disease induction mechanisms. Our studies provide the first evidence for enabling the prediction of clinical disease using serum NDICPs. The results provide proof-of-concept for the development of high-confidence serum NDICP expression waves and protein biomarker candidates for MS.

Early response index: a statistic to discover potential early stage disease biomarkers

Background

Identifying disease correlated features early before large number of molecules are impacted by disease progression with significant abundance change is very advantageous to biologists for developing early disease diagnosis biomarkers. Disease correlated features have relatively low level of abundance change at early stages. Finding them using existing bioinformatic tools in high throughput data is a challenging task since the technology suffers from limited dynamic range and significant noise. Most existing biomarker discovery algorithms can only detect molecules with high abundance changes, frequently missing early disease diagnostic markers.

Results

We present a new statistic called early response index (ERI) to prioritize disease correlated molecules as potential early biomarkers. Instead of classification accuracy, ERI measures the average classification accuracy improvement attainable by a feature when it is united with other counterparts for classification. ERI is more sensitive to abundance changes than other ranking statistics. We have shown that ERI significantly outperforms SAM and Localfdr in detecting early responding molecules in a proteomics study of a mouse model of multiple sclerosis. Importantly, ERI was able to detect many disease relevant proteins before those algorithms detect them at a later time point.

Conclusions

ERI method is more sensitive for significant feature detection during early stage of disease development. It potentially has a higher specificity for biomarker discovery, and can be used to identify critical time frame for disease intervention.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1712-y) contains supplementary material, which is available to authorized users.

Cytotoxicity and Genotoxicity Assessment of Sandalwood Essential Oil in Human Breast Cell Lines MCF-7 and MCF-10A

Sandalwood essential oil (SEO) is extracted from Santalum trees. Although α-santalol, a main constituent of SEO, has been studied as a chemopreventive agent, the genotoxic activity of the whole oil in human breast cell lines is still unknown. The main objective of this study was to assess the cytotoxic and genotoxic effects of SEO in breast adenocarcinoma (MCF-7) and nontumorigenic breast epithelial (MCF-10A) cells. Proteins associated with SEO genotoxicity were identified using a proteomics approach. Commercially available, high-purity, GC/MS characterized SEO was used to perform the experiments. The main constituents reported in the oil were (Z)-α-santalol (25.34%), (Z)-nuciferol (18.34%), (E)-β-santalol (10.97%), and (E)-nuciferol (10.46%). Upon exposure to SEO (2-8 μg/mL) for 24 hours, cell proliferation was determined by the MTT assay. Alkaline and neutral comet assays were used to assess genotoxicity. SEO exposure induced single- and double-strand breaks selectively in the DNA of MCF-7 cells. Quantitative LC/MS-based proteomics allowed identification of candidate proteins involved in this response: Ku70 (p = 1.37E - 2), Ku80 (p = 5.8E - 3), EPHX1 (p = 3.3E - 3), and 14-3-3ζ (p = 4.0E - 4). These results provide the first evidence that SEO is genotoxic and capable of inducing DNA single- and double-strand breaks in MCF-7 cells.

Body fluid biomarkers in multiple sclerosis: how far we have come and how they could affect the clinic now and in the future

Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system, which affects over 2.5 million people worldwide. Although MS has been extensively studied, many challenges still remain in regards to treatment, diagnosis and prognosis. Typically, prognosis and individual responses to treatment are evaluated by clinical tests such as the expanded disability status scale, MRI and presence of oligoclonal bands in the cerebrospinal fluid. However, none of these measures correlates strongly with treatment efficacy or disease progression across heterogeneous patient populations and subtypes of MS. Numerous studies over the past decades have attempted to identify sensitive and specific biomarkers for diagnosis, prognosis and treatment efficacy of MS. The objective of this article is to review and discuss the current literature on body fluid biomarkers in MS, including research on potential biomarker candidates in the areas of miRNA, mRNA, lipids and proteins.