Bakshi R, Yeste A, Patel B, Tauhid S, Tummala S, Rahbari R, Chu R, Regev K, Kivisäkk P, Weiner HL, Quintana FJ.
Neurol Neuroimmunol Neuroinflamm. 2016 ;3(2):e200. doi: 10.1212/NXI.0000000000000200. eCollection 2016 Apr.
To determine whether peripheral immune responses as measured by serum antigen arrays are linked to cerebral MRI measures of disease severity in multiple sclerosis (MS).
In this cross-sectional study, serum samples were obtained from patients with relapsing-remitting MS (n = 21) and assayed using antigen arrays that contained 420 antigens including CNS-related autoantigens, lipids, and heat shock proteins. Normalized compartment-specific global brain volumes were obtained from 3-tesla MRI as surrogates of atrophy, including gray matter fraction (GMF), white matter fraction (WMF), and total brain parenchymal fraction (BPF). Total brain T2 hyperintense lesion volume (T2LV) was quantified from fluid-attenuated inversion recovery images.
We found serum antibody patterns uniquely correlated with BPF, GMF, WMF, and T2LV. Furthermore, we identified immune signatures linked to MRI markers of neurodegeneration (BPF, GMF, WMF) that differentiated those linked to T2LV. Each MRI measure was correlated with a specific set of antibodies. Strikingly, immunoglobulin G (IgG) antibodies to lipids were linked to brain MRI measures. Based on the association between IgG antibody reactivity and each unique MRI measure, we developed a lipid index. This comprised the reactivity directed against all of the lipids associated with each specific MRI measure. We validated these findings in an additional independent set of patients with MS (n = 14) and detected a similar trend for the correlations between BPF, GMF, and T2LV vs their respective lipid indexes.
We propose serum antibody repertoires that are associated with MRI measures of cerebral MS involvement. Such antibodies may serve as biomarkers for monitoring disease pathology and progression.
Figure: Association of serum IgG reactivity with MRI measures of disease severity (BPF=total brain parencyhmal fraction; measure of brain atrophy, GMF=gray matter fraction; grey matter portion of the brain, WMF=white matter fraction; white matter portion of the brain, T2LV=total brain T2 hyperintense lesion volume; MS lesion volumes).
This article is a bioinformatics exercise, what I mean by this is, applying statistics to understand nature’s elegant but innately complex language of life (scientific translation – molecular biology). It is not the first time I’ve blogged on this approach, and generally I keep it at arm’s length; primarily because of its low reproducibility. Searching for autoantigens recognized by OCBs (or immunoglobulin IgG antibodies) in a majority of instances is like searching for a needle in a haystack. But this is not exactly what this group is doing, they’re instead producing a lipid index (involving more than one antigen/signature), whereby by the individual errors from each signature hopefully cancel each other out by chance alone, or conversely produces complete rubbish. But, by linking it to MRI you can be a bit more specific, unloading more of the proverbial haystack.
So what do the authors imply is going on in this data? In MS we expect BPF, GMF, WMF to decrease with disease progression, whilst T2LV increases with disease progression. They found that IgG reactivity in blood is associated with increased tissue destruction as demonstrated by decrease in BPF values (blue colour) and increase in T2LV values (red colour)[see Figure above]. Moreover, antibodies linked to disease severity appear to be enriched for lipid-reactive antibodies. They conclude that we may be able to use this panel to determine disease progression in MS.
This panel, however, is far from finished product and not the first of its kind. In fact, there are similar commercial assays stating to do the same. Many of them have data on blood biomarkers, and some have even been validated data on CSF. Time will tell.