Organization of thalamocortical structural covariance and a corresponding 3D atlas of the mouse thalamus.

For information from sensory organs to be processed by the brain, it is usually passed to appropriate areas of the cerebral cortex. Almost all of this information passes through the thalamus, a relay structure that reciprocally connects to the vast majority of the cortex. The thalamus facilitates this information transfer through a set of thalamocortical connections that vary in cellular structure, molecular profiles, innervation patterns, and firing rates. Additionally, corticothalamic connections allow for intracortical information transfer through the thalamus. These efferent and afferent connections between the thalamus and cortex have been the focus of many studies, and the importance of cortical connectivity in defining thalamus anatomy is demonstrated by multiple studies that parcellate the thalamus based on cortical connectivity profiles. Here, we examine correlated morphological variation between the thalamus and cortex, or thalamocortical structural covariance. For each voxel in the thalamus as a seed, we construct a cortical structural covariance map that represents correlated cortical volume variation, and examine whether high structural covariance is observed in cortical areas that are functionally relevant to the seed. Then, using these cortical structural covariance maps as features, we subdivide the thalamus into six non-overlapping regions (clusters of voxels), and assess whether cortical structural covariance is associated with cortical connectivity that specifically originates from these regions. We show that cortical structural covariance is high in areas of the cortex that are functionally related to the seed voxel, cortical structural covariance varies along cortical depth, and sharp transitions in cortical structural covariance profiles are observed when varying seed locations in the thalamus. Subdividing the thalamus based on structural covariance, we additionally demonstrate that the six thalamic clusters of voxels stratify cortical structural covariance along the dorsal-ventral, medial-lateral, and anterior-posterior axes. These cluster-associated structural covariance patterns are prominently detected in cortical regions innervated by fibers projecting out of their related thalamic subdivisions. Together, these results advance our understanding of how the thalamus and the cortex couple in their volumes. Our results indicate that these volume correlations reflect functional organization and structural connectivity, and further provides a novel segmentation of the mouse thalamus that can be used to examine thalamic structural variation and thalamocortical structural covariation in disease models.

The Caribbean-Hispanic Alzheimer's disease brain transcriptome reveals ancestry-specific disease mechanisms.

Identifying ancestry-specific molecular profiles of late-onset Alzheimer's Disease (LOAD) in brain tissue is crucial to understand novel mechanisms and develop effective interventions in non-European, high-risk populations. We performed gene differential expression (DE) and consensus network-based analyses in RNA-sequencing data of postmortem brain tissue from 39 Caribbean Hispanics (CH). To identify ancestry-concordant and -discordant expression profiles, we compared our results to those from two independent non-Hispanic White (NHW) samples (n = 731). In CH, we identified 2802 significant DE genes, including several LOAD known-loci. DE effects were highly concordant across ethnicities, with 373 genes transcriptome-wide significant in all three cohorts. Cross-ancestry meta-analysis found NPNT to be the top DE gene. We replicated over 82% of meta-analyses genome-wide signals in single-nucleus RNA-seq data (including NPNT and LOAD known-genes SORL1, FBXL7, CLU, ABCA7). Increasing representation in genetic studies will allow for deeper understanding of ancestry-specific mechanisms and improving precision treatment options in understudied groups.

Corticosteroids and Regional Variations in Thickness of the Human Cerebral Cortex across the Lifespan.

Exposures to life stressors accumulate across the lifespan, with possible impact on brain health. Little is known, however, about the mechanisms mediating age-related changes in brain structure. We use a lifespan sample of participants (n = 21 251; 4-97 years) to investigate the relationship between the thickness of cerebral cortex and the expression of the glucocorticoid- and the mineralocorticoid-receptor genes (NR3C1 and NR3C2, respectively), obtained from the Allen Human Brain Atlas. In all participants, cortical thickness correlated negatively with the expression of both NR3C1 and NR3C2 across 34 cortical regions. The magnitude of this correlation varied across the lifespan. From childhood through early adulthood, the profile similarity (between NR3C1/NR3C2 expression and thickness) increased with age. Conversely, both profile similarities decreased with age in late life. These variations do not reflect age-related changes in NR3C1 and NR3C2 expression, as observed in 5 databases of gene expression in the human cerebral cortex (502 donors). Based on the co-expression of NR3C1 (and NR3C2) with genes specific to neural cell types, we determine the potential involvement of microglia, astrocytes, and CA1 pyramidal cells in mediating the relationship between corticosteroid exposure and cortical thickness. Therefore, corticosteroids may influence brain structure to a variable degree throughout life.

Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study.

BACKGROUND: Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods. OBJECTIVE: This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response. METHODS: We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts. RESULTS: The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers. CONCLUSIONS: In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available.

Inter-Regional Variations in Gene Expression and Age-Related Cortical Thinning in the Adolescent Brain.

Age-related decreases in cortical thickness observed during adolescence may be related to fluctuations in sex and stress hormones. We examine this possibility by relating inter-regional variations in age-related cortical thinning (data from the Saguenay Youth Study) to inter-regional variations in expression levels of relevant genes (data from the Allen Human Brain Atlas); we focus on genes coding for glucocorticoid receptor (NR3C1), androgen receptor (AR), progesterone receptor (PGR), and estrogen receptors (ESR1 and ESR2). Across 34 cortical regions (Desikan-Killiany parcellation), age-related cortical thinning varied as a function of mRNA expression levels of NR3C1 in males (R2 = 0.46) and females (R2 = 0.30) and AR in males only (R2 = 0.25). Cortical thinning did not vary as a function of expression levels of PGR, ESR1, or ESR2 in either sex; this might be due to the observed low consistency of expression profiles of these 3 genes across donors. Inter-regional levels of the NR3C1 and AR expression interacted with each other vis-à-vis cortical thinning: age-related cortical thinning varied as a function of NR3C1 mRNA expression in brain regions with low (males: R2 = 0.64; females: R2 = 0.58) but not high (males: R2 = 0.0045; females: R2 = 0.15) levels of AR mRNA expression. These results suggest that glucocorticoid and androgen receptors contribute to cortical maturation during adolescence.

Cell-Specific Gene-Expression Profiles and Cortical Thickness in the Human Brain.

Neurobiological underpinnings of cortical thickness in the human brain are largely unknown. Here we use cell-type-specific gene markers to evaluate the contribution of 9 neural cell-types in explaining inter-regional variations in cortical thickness and age-related cortical thinning in the adolescent brain. Gene-expression data were derived from the Allen Human Brain Atlas (and validated using the BrainSpan Atlas). Values of cortical thickness/thinning were obtained with magnetic resonance imaging in a sample of 987 adolescents. We show that inter-regional profiles in cortical thickness relate to those in the expression of genes marking CA1 pyramidal cells, astrocytes, and microglia; taken together, the 3 cell types explain 70% of regional variation in cortical thickness. We also show that inter-regional profiles in cortical thinning relate to those in the expression of genes marking CA1 and S1 pyramidal cells, astrocytes and microglia. Using Gene Ontology analysis, we demonstrate that the difference in the contribution of CA1 and S1 pyramidal cells may relate to biological processes such as neuronal plasticity and potassium channel activity, respectively. This "virtual histology" approach (scripts provided) can be used to examine neurobiological underpinnings of cortical profiles associated with development, aging, and various disorders.

Transcriptomic characterization of MRI contrast with focus on the T1-w/T2-w ratio in the cerebral cortex.

Magnetic resonance (MR) images of the brain are of immense clinical and research utility. At the atomic and subatomic levels, the sources of MR signals are well understood. However, we lack a comprehensive understanding of the macromolecular correlates of MR signal contrast. To address this gap, we used genome-wide measurements to correlate gene expression with MR signal intensity across the cerebral cortex in the Allen Human Brain Atlas (AHBA). We focused on the ratio of T1-weighted and T2-weighted intensities (T1-w/T2-w ratio image), which is considered to be a useful proxy for myelin content. As expected, we found enrichment of positive correlations between myelin-associated genes and the ratio image, supporting its use as a myelin marker. Genome-wide, there was an association with protein mass, with genes coding for heavier proteins expressed in regions with high T1-w/T2-w values. Oligodendrocyte gene markers were strongly correlated with the T1-w/T2-w ratio, but this was not driven by myelin-associated genes. Mitochondrial genes exhibit the strongest relationship, showing higher expression in regions with low T1-w/T2-w ratio. This may be due to the pH gradient in mitochondria as genes up-regulated by pH in the brain were also highly correlated with the ratio. While we corroborate associations with myelin and synaptic plasticity, differences in the T1-w/T2-w ratio across the cortex are more strongly linked to molecule size, oligodendrocyte markers, mitochondria, and pH. We evaluate correlations between AHBA transcriptomic measurements and a group averaged T1-w/T2-w ratio image, showing agreement with in-sample results. Expanding our analysis to the whole brain results in strong positive T1-w/T2-w correlations for immune system, inflammatory disease, and microglia marker genes. Genes with negative correlations were enriched for neuron markers and synaptic plasticity genes. Lastly, our findings are similar when performed on T1-w or inverted T2-w intensities alone. These results provide a molecular characterization of MR contrast that will aid interpretation of future MR studies of the brain.

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity.

An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings. Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed. We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.

Global genetic variations predict brain response to faces.

Face expressions are a rich source of social signals. Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼ 500,000 single nucleotide polymorphisms. Using genomic-relationship-matrix restricted maximum likelihood (GREML), we related this global genetic variance to that in the brain response to facial expressions, as assessed with functional magnetic resonance imaging (fMRI) in a community-based sample of adolescents (n = 1,620). Brain response to facial expressions was measured in 25 regions constituting a face network, as defined previously. In 9 out of these 25 regions, common genetic variance explained a significant proportion of phenotypic variance (40-50%) in their response to ambiguous facial expressions; this was not the case for angry facial expressions. Across the network, the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region (R(2) = 0.38, p<0.001). Furthermore, this variability showed an inverted U relationship with both the number of observed connections (R2 = 0.48, p<0.001) and the magnitude of brain response (R(2) = 0.32, p<0.001). Thus, a significant proportion of the brain response to facial expressions is predicted by common genetic variance in a subset of regions constituting the face network. These regions show the highest inter-individual variability in the number of connections with other network nodes, suggesting that the genetic model captures variations across the adolescent brains in co-opting these regions into the face network.

Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text.

MOTIVATION: Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large-scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching and integration of the reports. We annotated a set of 1377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features. RESULTS: Co-occurrence based methods provided high recall with weak precision. The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision. Owing to its speed and simplicity, we applied the shallow linguistic kernel to a large set of new abstracts. To evaluate the results, we compared 2688 extracted connections with the Brain Architecture Management System (an existing database of rat connectivity). The extracted connections were connected in the Brain Architecture Management System at a rate of 63.5%, compared with 51.1% for co-occurring brain region pairs. We found that precision increases with the recency and frequency of the extracted relationships. AVAILABILITY AND IMPLEMENTATION: The source code, evaluations, documentation and other supplementary materials are available at http://www.chibi.ubc.ca/WhiteText. CONTACT: paul@chibi.ubc.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

The transcriptional legacy of developmental stochasticity.

Genetic and environmental variation are key contributors during organism development, but the influence of minor perturbations or noise is difficult to assess. This study focuses on the stochastic variation in allele-specific expression that persists through cell divisions in the nine-banded armadillo (Dasypus novemcinctus). We investigated the blood transcriptome of five wild monozygotic quadruplets over time to explore the influence of developmental stochasticity on gene expression. We identify an enduring signal of autosomal allelic variability that distinguishes individuals within a quadruplet despite their genetic similarity. This stochastic allelic variation, akin to X-inactivation but broader, provides insight into non-genetic influences on phenotype. The presence of stochastically canalized allelic signatures represents a novel axis for characterizing organismal variability, complementing traditional approaches based on genetic and environmental factors. We also developed a model to explain the inconsistent penetrance associated with these stochastically canalized allelic expressions. By elucidating mechanisms underlying the persistence of allele-specific expression, we enhance understanding of development's role in shaping organismal diversity.

Relationships between gene expression and brain wiring in the adult rodent brain.

We studied the global relationship between gene expression and neuroanatomical connectivity in the adult rodent brain. We utilized a large data set of the rat brain "connectome" from the Brain Architecture Management System (942 brain regions and over 5000 connections) and used statistical approaches to relate the data to the gene expression signatures of 17,530 genes in 142 anatomical regions from the Allen Brain Atlas. Our analysis shows that adult gene expression signatures have a statistically significant relationship to connectivity. In particular, brain regions that have similar expression profiles tend to have similar connectivity profiles, and this effect is not entirely attributable to spatial correlations. In addition, brain regions which are connected have more similar expression patterns. Using a simple optimization approach, we identified a set of genes most correlated with neuroanatomical connectivity, and find that this set is enriched for genes involved in neuronal development and axon guidance. A number of the genes have been implicated in neurodevelopmental disorders such as autistic spectrum disorder. Our results have the potential to shed light on the role of gene expression patterns in influencing neuronal activity and connectivity, with potential applications to our understanding of brain disorders. Supplementary data are available at http://www.chibi.ubc.ca/ABAMS.

Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation.

High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring and informatics pipelines have been applied to ISH images to determine expression intensity and pattern. To better capture the complex patterns of gene expression in the human cerebral cortex, we applied a machine learning approach. We propose gene re-identification as a contrastive learning task to compute representations of ISH images. We train our model on an ISH dataset of ~1,000 genes obtained from postmortem samples from 42 individuals. This model reaches a gene re-identification rate of 38.3%, a 13x improvement over random chance. We find that the learned embeddings predict expression intensity and pattern. To test generalization, we generated embeddings in a second dataset that assayed the expression of 78 genes in 53 individuals. In this set of images, 60.2% of genes are re-identified, suggesting the model is robust. Importantly, this dataset assayed expression in individuals diagnosed with schizophrenia. Gene and donor-specific embeddings from the model predict schizophrenia diagnosis at levels similar to that reached with demographic information. Mutations in the most discriminative gene, Sodium Voltage-Gated Channel Beta Subunit 4 (SCN4B), may help understand cardiovascular associations with schizophrenia and its treatment. We have publicly released our source code, embeddings, and models to spur further application to spatial transcriptomics. In summary, we propose and evaluate gene re-identification as a machine learning task to represent ISH gene expression images.

LaminaRGeneVis: A Tool to Visualize Gene Expression Across the Laminar Architecture of the Human Neocortex.

The application of RNA sequencing has enabled the characterization of genome-wide gene expression in the human brain, including distinct layers of the neocortex. Neuroanatomically, the molecular patterns that underlie the laminar organization of the neocortex can help link structure to circuitry and function. To advance our understanding of cortical architecture, we created LaminaRGeneVis, a web application that displays across-layer cortical gene expression from multiple datasets. These datasets were collected using bulk, single-nucleus, and spatial RNA sequencing methodologies and were normalized to facilitate comparisons between datasets. The online resource performs single- and multi-gene analyses to provide figures and statistics for user-friendly assessment of laminar gene expression patterns in the adult human neocortex. The web application is available at https://ethanhkim.shinyapps.io/laminargenevis/.

BDNF controls GABAAR trafficking and related cognitive processes via autophagic regulation of p62.

Reduced brain-derived neurotrophic factor (BDNF) and gamma-aminobutyric acid (GABA) neurotransmission co-occur in brain conditions (depression, schizophrenia and age-related disorders) and are associated with symptomatology. Rodent studies show they are causally linked, suggesting the presence of biological pathways mediating this link. Here we first show that reduced BDNF and GABA also co-occur with attenuated autophagy in human depression. Using mice, we then show that reducing Bdnf levels (Bdnf+/-) leads to upregulated sequestosome-1/p62, a key autophagy-associated adaptor protein, whose levels are inversely correlated with autophagic activity. Reduced Bdnf levels also caused reduced surface presentation of α5 subunit-containing GABAA receptor (α5-GABAAR) in prefrontal cortex (PFC) pyramidal neurons. Reducing p62 gene dosage restored α5-GABAAR surface expression and rescued PFC-relevant behavioral deficits of Bdnf+/- mice, including cognitive inflexibility and reduced sensorimotor gating. Increasing p62 levels was sufficient to recreate the molecular and behavioral profiles of Bdnf+/- mice. Collectively, the data reveal a novel mechanism by which deficient BDNF leads to targeted reduced GABAergic signaling through autophagic dysregulation of p62, potentially underlying cognitive impairment across brain conditions.

Microstructural imaging and transcriptomics of the basal forebrain in first-episode psychosis.

Cholinergic dysfunction has been implicated in the pathophysiology of psychosis and psychiatric disorders such as schizophrenia, depression, and bipolar disorder. The basal forebrain (BF) cholinergic nuclei, defined as cholinergic cell groups Ch1-3 and Ch4 (Nucleus Basalis of Meynert; NBM), provide extensive cholinergic projections to the rest of the brain. Here, we examined microstructural neuroimaging measures of the cholinergic nuclei in patients with untreated psychosis (~31 weeks of psychosis, <2 defined daily dose of antipsychotics) and used magnetic resonance spectroscopy (MRS) and transcriptomic data to support our findings. We used a cytoarchitectonic atlas of the BF to map the nuclei and obtained measures of myelin (quantitative T1, or qT1 as myelin surrogate) and microstructure (axial diffusion; AxD). In a clinical sample (n = 85; 29 healthy controls, 56 first-episode psychosis), we found significant correlations between qT1 of Ch1-3, left NBM and MRS-based dorsal anterior cingulate choline in healthy controls while this relationship was disrupted in FEP (p > 0.05). Case-control differences in qT1 and AxD were observed in the Ch1-3, with increased qT1 (reflecting reduced myelin content) and AxD (reflecting reduced axonal integrity). We found clinical correlates between left NBM qT1 with manic symptom severity, and AxD with negative symptom burden in FEP. Intracortical and subcortical myelin maps were derived and correlated with BF myelin. BF-cortical and BF-subcortical myelin correlations demonstrate known projection patterns from the BF. Using data from the Allen Human Brain Atlas, cholinergic nuclei showed significant enrichment for schizophrenia and depression-related genes. Cell-type specific enrichment indicated enrichment for cholinergic neuron markers as expected. Further relating the neuroimaging correlations to transcriptomics demonstrated links with cholinergic receptor genes and cell type markers of oligodendrocytes and cholinergic neurons, providing biological validity to the measures. These results provide genetic, neuroimaging, and clinical evidence for cholinergic dysfunction in schizophrenia.

Providing Care Beyond Therapy Sessions With a Natural Language Processing-Based Recommender System That Identifies Cancer Patients Who Experience Psychosocial Challenges and Provides Self-care Support: Pilot Study.

BACKGROUND: The negative psychosocial impacts of cancer diagnoses and treatments are well documented. Virtual care has become an essential mode of care delivery during the COVID-19 pandemic, and online support groups (OSGs) have been shown to improve accessibility to psychosocial and supportive care. de Souza Institute offers CancerChatCanada, a therapist-led OSG service where sessions are monitored by an artificial intelligence-based co-facilitator (AICF). The AICF is equipped with a recommender system that uses natural language processing to tailor online resources to patients according to their psychosocial needs. OBJECTIVE: We aimed to outline the development protocol and evaluate the AICF on its precision and recall in recommending resources to cancer OSG members. METHODS: Human input informed the design and evaluation of the AICF on its ability to (1) appropriately identify keywords indicating a psychosocial concern and (2) recommend the most appropriate online resource to the OSG member expressing each concern. Three rounds of human evaluation and algorithm improvement were performed iteratively. RESULTS: We evaluated 7190 outputs and achieved a precision of 0.797, a recall of 0.981, and an F1 score of 0.880 by the third round of evaluation. Resources were recommended to 48 patients, and 25 (52%) accessed at least one resource. Of those who accessed the resources, 19 (75%) found them useful. CONCLUSIONS: The preliminary findings suggest that the AICF can help provide tailored support for cancer OSG members with high precision, recall, and satisfaction. The AICF has undergone rigorous human evaluation, and the results provide much-needed evidence, while outlining potential strengths and weaknesses for future applications in supportive care.

Differential and spatial expression meta-analysis of genes identified in genome-wide association studies of depression.

Major depressive disorder (MDD) is the most prevalent psychiatric disorder worldwide and affects individuals of all ages. It causes significant psychosocial impairments and is a major cause of disability. A recent consortium study identified 102 genetic variants and 269 genes associated with depression. To provide targets for future depression research, we prioritized these recently identified genes using expression data. We examined the differential expression of these genes in three studies that profiled gene expression of MDD cases and controls across multiple brain regions. In addition, we integrated anatomical expression information to determine which brain regions and transcriptomic cell types highly express the candidate genes. We highlight 12 of the 269 genes with the most consistent differential expression: MANEA, UBE2M, CKB, ITPR3, SPRY2, SAMD5, TMEM106B, ZC3H7B, LST1, ASXL3, ZNF184 and HSPA1A. The majority of these top genes were found to have sex-specific differential expression. We place greater emphasis on ZNF184 as it is the top gene in a more conservative analysis of the 269. Specifically, the differential expression of ZNF184 was strongest in subcortical regions in males and females. Anatomically, our results suggest the importance of the dorsal lateral geniculate nucleus, cholinergic, monoaminergic and enteric neurons. These findings provide a guide for targeted experiments to advance our understanding of the genetic underpinnings of depression.

A pH-eQTL Interaction at the RIT2-SYT4 Parkinson's Disease Risk Locus in the Substantia Nigra.

Parkinson's disease causes severe motor and cognitive disabilities that result from the progressive loss of dopamine neurons in the substantia nigra. The rs12456492 variant in the RIT2 gene has been repeatedly associated with increased risk for Parkinson's disease. From a transcriptomic perspective, a meta-analysis found that RIT2 gene expression is correlated with pH in the human brain. To assess these pH associations in relation to Parkinson's disease risk, we examined the two datasets that assayed rs12456492, gene expression, and pH in the postmortem human brain. Using the BrainEAC dataset, we replicate the positive correlation between RIT2 gene expression and pH in the human brain (n = 100). Furthermore, we found that the relationship between expression and pH is influenced by rs12456492. When tested across ten brain regions, this interaction is specifically found in the substantia nigra. A similar association was found for the co-localized SYT4 gene. In addition, SYT4 associations are stronger in a combined model with both genes, and the SYT4 interaction appears to be specific to males. In the Genotype-Tissue Expression (GTEx) dataset, the pH associations involving rs12456492 and expression of either SYT4 and RIT2 were not seen. This null finding may be due to the short postmortem intervals of the GTEx tissue samples. In the BrainEAC data, we tested the effect of postmortem interval and only observed the interactions in samples with the longer intervals. These previously unknown associations suggest novel roles for rs12456492, RIT2, and SYT4 in the regulation and response to pH in the substantia nigra.

Characterization of Human Genes Modulated by Porphyromonas gingivalis Highlights the Ribosome, Hypothalamus, and Cholinergic Neurons.

Porphyromonas gingivalis, a bacterium associated with periodontal disease, is a suspected cause of Alzheimer's disease. This bacterium is reliant on gingipain proteases, which cleave host proteins after arginine and lysine residues. To characterize gingipain susceptibility, we performed enrichment analyses of arginine and lysine proportion proteome-wide. Genes differentially expressed in brain samples with detected P. gingivalis reads were also examined. Genes from these analyses were tested for functional enrichment and specific neuroanatomical expression patterns. Proteins in the SRP-dependent cotranslational protein targeting to membrane pathway were enriched for these residues and previously associated with periodontal and Alzheimer's disease. These ribosomal genes are up-regulated in prefrontal cortex samples with detected P. gingivalis sequences. Other differentially expressed genes have been previously associated with dementia (ITM2B, MAPT, ZNF267, and DHX37). For an anatomical perspective, we characterized the expression of the P. gingivalis associated genes in the mouse and human brain. This analysis highlighted the hypothalamus, cholinergic neurons, and the basal forebrain. Our results suggest markers of neural P. gingivalis infection and link the cholinergic and gingipain hypotheses of Alzheimer's disease.