Expression quantitative trait loci mapping identified PtrXB38 as a key hub gene in adventitious root development in Populus.

Plant establishment requires the formation and development of an extensive root system with architecture modulated by complex genetic networks. Here, we report the identification of the PtrXB38 gene as an expression quantitative trait loci (eQTL) hotspot, mapped using 390 leaf and 444 xylem Populus trichocarpa transcriptomes. Among predicted targets of this trans-eQTL were genes involved in plant hormone responses and root development. Overexpression of PtrXB38 in Populus led to significant increases in callusing and formation of both stem-born roots and base-born adventitious roots. Omics studies revealed that genes and proteins controlling auxin transport and signaling were involved in PtrXB38-mediated adventitious root formation. Protein-protein interaction assays indicated that PtrXB38 interacts with components of endosomal sorting complexes required for transport machinery, implying that PtrXB38-regulated root development may be mediated by regulating endocytosis pathway. Taken together, this work identified a crucial root development regulator and sheds light on the discovery of other plant developmental regulators through combining eQTL mapping and omics approaches.

Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology.

Late-Onset Alzheimer's disease (LOAD) is a common, complex genetic disorder well-known for its heterogeneous pathology. The genetic heterogeneity underlying common, complex diseases poses a major challenge for targeted therapies and the identification of novel disease-associated variants. Case-control approaches are often limited to examining a specific outcome in a group of heterogenous patients with different clinical characteristics. Here, we developed a novel approach to define relevant transcriptomic endophenotypes and stratify decedents based on molecular profiles in three independent human LOAD cohorts. By integrating post-mortem brain gene co-expression data from 2114 human samples with LOAD, we developed a novel quantitative, composite phenotype that can better account for the heterogeneity in genetic architecture underlying the disease. We used iterative weighted gene co-expression network analysis (WGCNA) to reduce data dimensionality and to isolate gene sets that are highly co-expressed within disease subtypes and represent specific molecular pathways. We then performed single variant association testing using whole genome-sequencing data for the novel composite phenotype in order to identify genetic loci that contribute to disease heterogeneity. Distinct LOAD subtypes were identified for all three study cohorts (two in ROSMAP, three in Mayo Clinic, and two in Mount Sinai Brain Bank). Single variant association analysis identified a genome-wide significant variant in TMEM106B (p-value < 5×10-8, rs1990620G) in the ROSMAP cohort that confers protection from the inflammatory LOAD subtype. Taken together, our novel approach can be used to stratify LOAD into distinct molecular subtypes based on affected disease pathways.

Transfer learning-trained convolutional neural networks identify novel MRI biomarkers of Alzheimer's disease progression.

INTRODUCTION: Genome-wide association studies (GWAS) for late onset Alzheimer's disease (AD) may miss genetic variants relevant for delineating disease stages when using clinically defined case/control as a phenotype due to its loose definition and heterogeneity. METHODS: We use a transfer learning technique to train three-dimensional convolutional neural network (CNN) models based on structural magnetic resonance imaging (MRI) from the screening stage in the Alzheimer's Disease Neuroimaging Initiative consortium to derive image features that reflect AD progression. RESULTS: CNN-derived image phenotypes are significantly associated with fasting metabolites related to early lipid metabolic changes as well as insulin resistance and with genetic variants mapped to candidate genes enriched for amyloid beta degradation, tau phosphorylation, calcium ion binding-dependent synaptic loss, APP-regulated inflammation response, and insulin resistance. DISCUSSION: This is the first attempt to show that non-invasive MRI biomarkers are linked to AD progression characteristics, reinforcing their use in early AD diagnosis and monitoring.

DBA/2J mice are susceptible to diabetic nephropathy and diabetic exacerbation of IOP elevation.

Some pathological manifestations of diabetes in the eye include retinopathy, cataracts and elevated intraocular pressure (IOP). Loss of retinal ganglion cells (RGCs) in non-proliferative stages of diabetic retinopathy and small increases in IOP in diabetic patients has raised the possibility that diabetes affects the development and progression of ocular hypertension and glaucoma. The Ins2Akita mutation is known to cause diabetes and retinopathy on a C57BL/6J (B6) background by as early as 3 months of age. Here, the impact of the Akita mutation on glaucoma was assessed using DBA/2J (D2) mice, a widely used mouse model of ocular hypertension induced glaucoma. In D2.Ins2Akita/+ mice, the contribution of diabetes to vascular permeability, IOP elevation, RGC loss, and glaucoma development was assessed. D2.Ins2Akita/+ mice developed a severe diabetic nephropathy and early mortality between 6-8 months of age. This agrees with previous reports showing that the D2 background is more susceptible to diabetes than the B6 background. In addition, D2.Ins2Akita/+ mice had vascular leakage, astrocyte reactivity and a significant increase in IOP. However no RGC loss and no anterograde axonal transport dysfunction were found at 8.5 months of age. Therefore, our data show that despite severe diabetes and an increased IOP compared to controls, RGCs do not lose axon transport or degenerate. This may be due to a DBA/2J-specific genetic modifier(s) that could provide novel and important avenues for developing new therapies for diabetic retinopathy and possibly glaucoma.