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Vascular damage in Alzheimer's disease (AD) has shown conflicting findings particularly when analyzing longitudinal data. We introduce white matter hyperintensity (WMH) longitudinal morphometric analysis (WLMA) that quantifies WMH expansion as the distance from lesion voxels to a region of interest boundary.

WMH segmentation maps were derived from 270 longitudinal fluid‐attenuated inversion recovery (FLAIR) ADNI images. WLMA was performed on five data‐driven WMH patterns with distinct spatial distributions. Amyloid accumulation was evaluated with WMH expansion across the five WMH patterns.

The preclinical group had significantly greater expansion in the posterior ventricular WM compared to controls. Amyloid significantly associated with frontal WMH expansion primarily within AD individuals. WLMA outperformed WMH volume changes for classifying AD from controls primarily in periventricular and posterior WMH.

These data support the concept that localized WMH expansion continues to proliferate with amyloid accumulation throughout the entirety of the disease in distinct spatial locations.

 

INTRODUCTION Genome-wide association studies (GWASs) have identified thousands of human genetic variants associated with diverse diseases and traits, and most of these variants map to noncoding loci with unknown target genes and function. Current approaches to understand which GWAS loci harbor causal variants and to map these noncoding regulators to target genes suffer from low throughput. With newer multiancestry GWASs from individuals of diverse ancestries, there is a pressing and growing need to scale experimental assays to connect GWAS variants with molecular mechanisms. Here, we combined biobank-scale GWASs, massively parallel CRISPR screens, and single-cell sequencing to discover target genes of noncoding variants for blood trait loci with systematic targeting and inhibition of noncoding GWAS loci with single-cell sequencing (STING-seq). RATIONALE Blood traits are highly polygenic, and GWASs have identified thousands of noncoding loci that map to candidate cis -regulatory elements (CREs). By combining CRE-silencing CRISPR perturbations and single-cell readouts, we targeted hundreds of GWAS loci in a single assay, revealing target genes in cis and in trans . For select CREs that regulate target genes, we performed direct variant insertion. Although silencing the CRE can identify the target gene, direct variant insertion can identify magnitude and direction of effect on gene expression for the GWAS variant. In select cases in which the target gene was a transcription factor or microRNA, we also investigated the gene-regulatory networks altered upon CRE perturbation and how these networks differ across blood cell types. RESULTS We inhibited candidate CREs from fine-mapped blood trait GWAS variants (from ~750,000 individual of diverse ancestries) in human erythroid progenitors. In total, we targeted 543 variants (254 loci) mapping to candidate CREs, generating multimodal single-cell data including transcriptome, direct CRISPR gRNA capture, and cell surface proteins. We identified target genes in cis (within 500 kb) for 134 CREs. In most cases, we found that the target gene was the closest gene and that specific enhancer-associated biochemical hallmarks (H3K27ac and accessible chromatin) are essential for CRE function. Using multiple perturbations at the same locus, we were able to distinguished between causal variants from noncausal variants in linkage disequilibrium. For a subset of validated CREs, we also inserted specific GWAS variants using base-editing STING-seq (beeSTING-seq) and quantified the effect size and direction of GWAS variants on gene expression. Given our transcriptome-wide data, we examined dosage effects in cis and trans in cases in which the cis target is a transcription factor or microRNA. We found that trans target genes are also enriched for GWAS loci, and identified gene clusters within trans gene networks with distinct biological functions and expression patterns in primary human blood cells. CONCLUSION In this work, we investigated noncoding GWAS variants at scale, identifying target genes in single cells. These methods can help to address the variant-to-function challenges that are a barrier for translation of GWAS findings (e.g., drug targets for diseases with a genetic basis) and greatly expand our ability to understand mechanisms underlying GWAS loci. Identifying causal variants and their target genes with STING-seq. Uncovering causal variants and their target genes or function are a major challenge for GWASs. STING-seq combines perturbation of noncoding loci with multimodal single-cell sequencing to profile hundreds of GWAS loci in parallel. This approach can identify target genes in cis and trans , measure dosage effects, and decipher gene-regulatory networks. 

Blinking, the transient occlusion of the eye by one or more membranes, serves several functions including wetting, protecting, and cleaning the eye. This behavior is seen in nearly all living tetrapods and absent in other extant sarcopterygian lineages suggesting that it might have arisen during the water-to-land transition. Unfortunately, our understanding of the origin of blinking has been limited by a lack of known anatomical correlates of the behavior in the fossil record and a paucity of comparative functional studies. To understand how and why blinking originates, we leverage mudskippers (Oxudercinae), a clade of amphibious fishes that have convergently evolved blinking. Using microcomputed tomography and histology, we analyzed two mudskipper species, Periophthalmus barbarus and Periophthalmodon septemradiatus , and compared them to the fully aquatic round goby, Neogobius melanostomus . Study of gross anatomy and epithelial microstructure shows that mudskippers have not evolved novel musculature or glands to blink. Behavioral analyses show the blinks of mudskippers are functionally convergent with those of tetrapods: P. barbarus blinks more often under high-evaporation conditions to wet the eye, a blink reflex protects the eye from physical insult, and a single blink can fully clean the cornea of particulates. Thus, eye retraction in concert with a passive occlusal membrane can achieve functions associated with life on land. Osteological correlates of eye retraction are present in the earliest limbed vertebrates, suggesting blinking capability. In both mudskippers and tetrapods, therefore, the origin of this multifunctional innovation is likely explained by selection for increasingly terrestrial lifestyles. 

Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information.

In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a ‘parent table’ of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts.

The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu.

Supplementary data are available at Bioinformatics online.

 

Abstract The genetic prehistory of human populations in Central America is largely unexplored leaving an important gap in our knowledge of the global expansion of humans. We report genome-wide ancient DNA data for a transect of twenty individuals from two Belize rock-shelters dating between 9,600-3,700 calibrated radiocarbon years before present (cal. BP). The oldest individuals (9,600-7,300 cal. BP) descend from an Early Holocene Native American lineage with only distant relatedness to present-day Mesoamericans, including Mayan-speaking populations. After ~5,600 cal. BP a previously unknown human dispersal from the south made a major demographic impact on the region, contributing more than 50% of the ancestry of all later individuals. This new ancestry derived from a source related to present-day Chibchan speakers living from Costa Rica to Colombia. Its arrival corresponds to the first clear evidence for forest clearing and maize horticulture in what later became the Maya region. 

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

Knowledge representation and reasoning (KR&R) has been successfully implemented in many fields to enable computers to solve complex problems with AI methods. However, its application to biomedicine has been lagging in part due to the daunting complexity of molecular and cellular pathways that govern human physiology and pathology. In this article, we describe concrete uses of Scalable PrecisiOn Medicine Knowledge Engine (SPOKE), an open knowledge network that connects curated information from thirty‐seven specialized and human‐curated databases into a single property graph, with 3 million nodes and 15 million edges to date. Applications discussed in this article include drug discovery, COVID‐19 research and chronic disease diagnosis, and management.