Selective vulnerability of different brain regions is seen in many neurodegenerative disorders. The hippocampus and cortex are selectively vulnerable in Alzheimer’s disease (AD), however the degree of involvement of the different brain regions differs among patients. We classified corticolimbic patterns of neurofibrillary tangles in postmortem tissue to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. We identified hippocampal gene expression changes associated with hippocampal vulnerability and used machine learning to identify genes that were associated with AD neuropathology, including SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Further histologic and biochemical analyses suggested SERPINA5 expression is associated with tau expression in the brain. Our study highlights the importance of embracing heterogeneity of the human brain in disease to identify disease-relevant gene expression.

Guggulsterone [4, 17(20)-pregnadiene-3, 16-dione] is a plant sterol derived from the gum resin of the tree Commiphora wightii. The gum resin of the guggul tree has been used in traditional medicine for centuries to treat obesity, liver disorders, internal tumors, malignant sores, ulcers, urinary complaints, intestinal worms, leucoderma, sinus, edema and sudden paralytic seizures. Guggulsterone has been shown to modulate the nuclear receptors, farnesoid X receptor, pregnane X receptor, CYP 2b10 gene expression, and the bile salt export pump for cholesterol elimination. Recent research indicates that the active components of gum guggul, E- and Zguggulsterone have the potential to both prevent and treat cancers. Guggulsterone inhibits the growth of a wide variety of tumor cells and induces apoptosis through down regulation of antiapoptotic gene products (IAP1, xIAP, Bfl-1/A1, Bcl-2, cFLIP, and survivin), modulation of cell cycle proteins (cyclin D1 and c-Myc), activation of caspases, inhibition of Akt, and activation of JNK. Guggulsterone modulates the expression of gene products involved in metastasis (MMP-9, COX-2, and VEGF) of tumor cells. Guggulsterone mediates gene expression through the modulation of several transcription factors, including NF-κB, STAT3, C/EBPα, androgen receptor, and glucocorticoid receptors. This review describes the anti-cancer properties, molecular targets, and the apoptotic effects of guggulsterone.

SummarySelective vulnerability is a central concept to the myriad of devastating neurodegenerative disorders. Although hippocampus and cortex are selectively vulnerable in Alzheimer’s disease (AD), the degree of involvement lies along a spectrum that we previously defined as AD subtypes revealing distinct clinical correlates. To operationalize heterogeneity of disease spectrum, we classified corticolimbic patterns of neurofibrillary tangles to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. Using a multidisciplinary approach, we uncovered disease-relevant hippocampal gene expression changes. Biological relevance was prioritized using machine learning and several levels of human validation. This resulted in five genes highly predictive of neuropathologically diagnosed AD: SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Deeper investigation revealed SERPINA5 to be a novel tau binding partner that may represent a “tipping point” in the dynamic maturity of neurofibrillary tangles. Our study highlights the importance of embracing heterogeneity of the human brain to yield promising gene candidates as exampled by SERPINA5.

Background Using an objective mathematical algorithm to assess corticolimbic involvement of neurofibrillary tangle pathology, we identified three AD subtypes: hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. Typical AD brains were representative of the expected patterns of hippocampal and cortical involvement as outlined by Braak tangle stage. In contrast, we discovered an extreme phenotype that exists outside of the Braakian‐concept of neurofibrillary tangle patterns. Hippocampal sparing AD cases demonstrate unexpected sparing of the hippocampus relative to severely involved association cortices and limbic predominant AD cases demonstrate inundation of the hippocampus relative to mildly involved association cortices. Using these AD subtypes, our objective was to leverage our understanding of disease spectrum to uncover transcriptomic changes that underlie selective vulnerability of the hippocampus in AD. Method We performed RNA‐sequencing in 40 neuropathologically diagnosed AD cases (10 HpSp, 20 Typical and 10 Limbic) and 15 controls to uncover gene expression changes associated with phenotype differences allowing us to prioritize 44 genes for further exploration. Validation with NanoString and quantification with digital pathology were performed in an expanded cohort of 158 AD cases (36 HpSp, 79 Typical and 35 Limbic) and 32 controls. We quantified digital pathology measures of early tangle pathology (CP13), mature tangle pathology (Ab39), amyloid‐β (33.1.1). In addition, we measured cellular markers for microglia (CD68), endothelia (CD34), and astroglia (GFAP). Deep learning based prediction models were employed, which nominated the SLC38A2 gene as a predictor of AD. Result The SLC38A2 gene was originally prioritized in our RNA‐sequencing data based upon differential expression between controls and typical AD. We observed upregulation of SLC38A2 in all AD subtypes. RNA‐seq gene expression measures validated well with NanoString for SLC38A2 (R=0.97, p<0.001). Regression analysis identified early tangle (p=0.011) and late tangle measures (p=0.026) as significant predictors of gene expression, but not amyloid measures (p=0.74). Microgliosis approached significance (p=0.052), but with no contribution from endothelial cell burden (p=0.17) or astrogliosis (p=0.58). Age approached significance (p=0.06), but not sex (p=0.88) or APOE‐ε4 status (p=0.10). Conclusion Our data supports consideration of intra‐disease divergence with regard to case stratification and may reveal genes previously masked by heterogeneity of cohorts.