From progression to progress: The future of multiple sclerosis

Significant advances have been made in the diagnosis and treatment of multiple sclerosis in recent years yet challenges remain. The current classification of MS phenotypes according to disease activity and progression, for example, does not adequately reflect the underlying pathophysiological mechanisms that may be acting in an individual with MS at different time points. Thus, there is a need for clinicians to transition to a management approach based on the underlying pathophysiological mechanisms that drive disability in MS. A Canadian expert panel convened in January 2023 to discuss priorities for clinical discovery and scientific exploration that would help advance the field. Five key areas of focus included: identifying a mechanism-based disease classification system; developing biomarkers (imaging, fluid, digital) to identify pathologic processes; implementing a data-driven approach to integrate genetic/environmental risk factors, clinical findings, imaging and biomarker data, and patient-reported outcomes to better characterize the many factors associated with disability progression; utilizing precision-based treatment strategies to target different disease processes; and potentially preventing disease through Epstein-Barr virus (EBV) vaccination, counselling about environmental risk factors (e.g. obesity, exercise, vitamin D/sun exposure, smoking) and other measures. Many of the tools needed to meet these needs are currently available. Further work is required to validate emerging biomarkers and tailor treatment strategies to the needs of individual patients. The hope is that a more complete view of the individual's pathobiology will enable clinicians to usher in an era of truly personalized medicine, in which more informed treatment decisions throughout the disease course achieve better long-term outcomes.

Deciphering Protein Secretion from the Brain to Cerebrospinal Fluid for Biomarker Discovery

Cerebrospinal fluid (CSF) is an essential matrix for the discovery of neurological disease biomarkers. However, the high dynamic range of protein concentrations in CSF hinders the detection of the least abundant protein biomarkers by untargeted mass spectrometry. It is thus beneficial to gain a deeper understanding of the secretion processes within the brain. Here, we aim to explore if and how the secretion of brain proteins to the CSF can be predicted. By combining a curated CSF proteome and the brain elevated proteome of the Human Protein Atlas, brain proteins were classified as CSF or non-CSF secreted. A machine learning model was trained on a range of sequence-based features to differentiate between CSF and non-CSF groups and effectively predict the brain origin of proteins. The classification model achieves an area under the curve of 0.89 if using high confidence CSF proteins. The most important prediction features include the subcellular localization, signal peptides, and transmembrane regions. The classifier generalized well to the larger brain detected proteome and is able to correctly predict novel CSF proteins identified by affinity proteomics. In addition to elucidating the underlying mechanisms of protein secretion, the trained classification model can support biomarker candidate selection.

Neurofilament light chain: A possible fluid biomarker in the intrahippocampal kainic acid mouse model for chronic epilepsy?

OBJECTIVE

In the management of epilepsy, there is an ongoing quest to discover new biomarkers to improve the diagnostic process, the monitoring of disease progression, and the evaluation of treatment responsiveness. In this regard, biochemical traceability in biofluids is notably absent in contrast to other diseases. In the present preclinical study, we investigated the potential of neurofilament light chain (NfL) as a possible diagnostic and response fluid biomarker for epilepsy.

METHODS

We gained insights into NfL levels during the various phases of the intrahippocampal kainic acid mouse model of temporal lobe epilepsy-namely, the status epilepticus (SE) and the chronic phase with spontaneous seizures. To this end, NfL levels were determined directly in the cerebral interstitial fluid (ISF) with cerebral open flow microperfusion as sampling technique, as well as in cerebrospinal fluid (CSF) and plasma. Lastly, we assessed whether NfL levels diminished upon curtailing SE with diazepam and ketamine.

RESULTS

NfL levels are higher during SE in both cerebral ISF and plasma in kainic acid-treated mice compared to sham-injected mice. Additionally, ISF and plasma NfL levels are lower in mice treated with diazepam and ketamine to stop SE compared with the vehicle-treated mice. In the chronic phase with spontaneous seizures, higher NfL levels could only be detected in ISF and CSF samples, and not in plasma. No correlations could be found between NfL levels and seizure burden, nor with immunohistological markers for neurodegeneration/inflammation.

SIGNIFICANCE

Our findings demonstrate the translational potential of NfL as a blood-based fluid biomarker for SE. This is less evident for chronic epilepsy, as in this case higher NfL levels could only be detected in ISF and CSF, and not in plasma, acknowledging the invasive nature of CSF sampling in chronic epilepsy follow-up.

Brain-derived neurotrophic factor (BDNF) in perinatal depression: Side show or pivotal factor?

Perinatal depression is the most common psychiatric complication of pregnancy, with its detrimental effects on maternal and infant health widely underrated. There is a pressing need for specific molecular biomarkers, with pregnancy-related decline in brain-derived neurotrophic factor (BDNF) in the blood and downregulation of TrkB receptor in the brain reported in clinical and preclinical studies. In this review, we explore the emerging role of BDNF in reproductive biology and discuss evidence suggesting its deficiency as a risk factor for perinatal depression. With the increasing evidence for restoration of serum BDNF levels by antidepressant therapy, the strengthening association of perinatal depression with deficiency of BDNF supports its potential as a surrogate endpoint for preclinical and clinical studies.

A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study

INTRODUCTION

To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort.

METHODS

A total of 411 participants were selected, including 96 cognitively normal individuals, 94 patients with mild cognitive impairment (MCI) patients, 173 patients with AD, and 48 patients with non-AD dementia. Fluid biomarkers were measured with single molecule array. Amyloid beta (Aβ) deposition was determined by 18 F-Flobetapir positron emission tomography (PET), and brain atrophy was quantified using magnetic resonance imaging (MRI).

RESULTS

Aβ42/Aβ40 was decreased, whereas levels of phosphorylated tau (p-tau) were increased in cerebrospinal fluid (CSF) and plasma from patients with AD. CSF Aβ42/Aβ40, CSF p-tau, and plasma p-tau showed a high concordance in discriminating between AD and non-AD dementia or elderly controls. A combination of plasma p-tau, apolipoprotein E (APOE) genotype, and MRI measures accurately predicted amyloid PET status.

DISCUSSION

These results revealed a universal applicability of the "A/T/N" framework in a Chinese population and established an optimal diagnostic model consisting of cost-effective and non-invasive approaches for diagnosing AD.