Dissociable Roles of Pallidal Neuron Subtypes in Regulating Motor Patterns.

We have previously established that PV+ neurons and Npas1+ neurons are distinct neuron classes in the external globus pallidus (GPe): they have different topographical, electrophysiological, circuit, and functional properties. Aside from Foxp2+ neurons, which are a unique subclass within the Npas1+ class, we lack driver lines that effectively capture other GPe neuron subclasses. In this study, we examined the utility of Kcng4-Cre, Npr3-Cre, and Npy2r-Cre mouse lines (both males and females) for the delineation of GPe neuron subtypes. By using these novel driver lines, we have provided the most exhaustive investigation of electrophysiological studies of GPe neuron subtypes to date. Corroborating our prior studies, GPe neurons can be divided into two statistically distinct clusters that map onto PV+ and Npas1+ classes. By combining optogenetics and machine learning-based tracking, we showed that optogenetic perturbation of GPe neuron subtypes generated unique behavioral structures. Our findings further highlighted the dissociable roles of GPe neurons in regulating movement and anxiety-like behavior. We concluded that Npr3+ neurons and Kcng4+ neurons are distinct subclasses of Npas1+ neurons and PV+ neurons, respectively. Finally, by examining local collateral connectivity, we inferred the circuit mechanisms involved in the motor patterns observed with optogenetic perturbations. In summary, by identifying mouse lines that allow for manipulations of GPe neuron subtypes, we created new opportunities for interrogations of cellular and circuit substrates that can be important for motor function and dysfunction.SIGNIFICANCE STATEMENT Within the basal ganglia, the external globus pallidus (GPe) has long been recognized for its involvement in motor control. However, we lacked an understanding of precisely how movement is controlled at the GPe level as a result of its cellular complexity. In this study, by using transgenic and cell-specific approaches, we showed that genetically-defined GPe neuron subtypes have distinct roles in regulating motor patterns. In addition, the in vivo contributions of these neuron subtypes are in part shaped by the local, inhibitory connections within the GPe. In sum, we have established the foundation for future investigations of motor function and disease pathophysiology.

Parvalbumin+ and Npas1+ Pallidal Neurons Have Distinct Circuit Topology and Function.

The external globus pallidus (GPe) is a critical node within the basal ganglia circuit. Phasic changes in the activity of GPe neurons during movement and their alterations in Parkinson's disease (PD) argue that the GPe is important in motor control. Parvalbumin-positive (PV+) neurons and Npas1+ neurons are the two principal neuron classes in the GPe. The distinct electrophysiological properties and axonal projection patterns argue that these two neuron classes serve different roles in regulating motor output. However, the causal relationship between GPe neuron classes and movement remains to be established. Here, by using optogenetic approaches in mice (both males and females), we showed that PV+ neurons and Npas1+ neurons promoted and suppressed locomotion, respectively. Moreover, PV+ neurons and Npas1+ neurons are under different synaptic influences from the subthalamic nucleus (STN). Additionally, we found a selective weakening of STN inputs to PV+ neurons in the chronic 6-hydroxydopamine lesion model of PD. This finding reinforces the idea that the reciprocally connected GPe-STN network plays a key role in disease symptomatology and thus provides the basis for future circuit-based therapies.SIGNIFICANCE STATEMENT The external pallidum is a key, yet an understudied component of the basal ganglia. Neural activity in the pallidum goes awry in neurologic diseases, such as Parkinson's disease. While this strongly argues that the pallidum plays a critical role in motor control, it has been difficult to establish the causal relationship between pallidal activity and motor function/dysfunction. This was in part because of the cellular complexity of the pallidum. Here, we showed that the two principal neuron types in the pallidum have opposing roles in motor control. In addition, we described the differences in their synaptic influence. Importantly, our research provides new insights into the cellular and circuit mechanisms that explain the hypokinetic features of Parkinson's disease.

Npas1+-Nkx2.1+ Neurons Are an Integral Part of the Cortico-pallido-cortical Loop.

Within the basal ganglia circuit, the external globus pallidus (GPe) is critically involved in motor control. Aside from Foxp2+ neurons and ChAT+ neurons that have been established as unique neuron types, there is little consensus on the classification of GPe neurons. Properties of the remaining neuron types are poorly defined. In this study, we leverage new mouse lines, viral tools, and molecular markers to better define GPe neuron subtypes. We found that Sox6 represents a novel, defining marker for GPe neuron subtypes. Lhx6+ neurons that lack the expression of Sox6 were devoid of both parvalbumin and Npas1. This result confirms previous assertions of the existence of a unique Lhx6+ population. Neurons that arise from the Dbx1+ lineage were similarly abundant in the GPe and displayed a heterogeneous makeup. Importantly, tracing experiments revealed that Npas1+-Nkx2.1+ neurons represent the principal noncholinergic, cortically-projecting neurons. In other words, they form the pallido-cortical arm of the cortico-pallido-cortical loop. Our data further show that pyramidal-tract neurons in the cortex collateralized within the GPe, forming a closed-loop system between the two brain structures. Overall, our findings reconcile some of the discrepancies that arose from differences in techniques or the reliance on preexisting tools. Although spatial distribution and electrophysiological properties of GPe neurons reaffirm the diversification of GPe subtypes, statistical analyses strongly support the notion that these neuron subtypes can be categorized under the two principal neuron classes: PV+ neurons and Npas1+ neurons.SIGNIFICANCE STATEMENT The poor understanding of the neuronal composition in the external globus pallidus (GPe) undermines our ability to interrogate its precise behavioral and disease involvements. In this study, 12 different genetic crosses were used, hundreds of neurons were electrophysiologically characterized, and >100,000 neurons were histologically- and/or anatomically-profiled. Our current study further establishes the segregation of GPe neuron classes and illustrates the complexity of GPe neurons in adult mice. Our results support the idea that Npas1+-Nkx2.1+ neurons are a distinct GPe neuron subclass. By providing a detailed analysis of the organization of the cortico-pallidal-cortical projection, our findings establish the cellular and circuit substrates that can be important for motor function and dysfunction.

Group testing in mediation analysis.

We consider the scenario where there is an exposure, multiple biologically defined sets of biomarkers, and an outcome. We propose a new two-step procedure that tests if any of the sets of biomarkers mediate the exposure/outcome relationship, while maintaining a prespecified familywise error rate. The first step of the proposed procedure is a screening step that removes all groups that are unlikely to be strongly associated with both the exposure and the outcome. The second step adapts recent advances in postselection inference to test if there are true mediators in each of the remaining candidate sets. We use simulation to show that this simple two-step procedure has higher statistical power to detect true mediating sets when compared with existing procedures. We then use our two-step procedure to identify a set of Lysine-related metabolites that potentially mediate the known relationship between increased body mass index and the increased risk of estrogen-receptor positive breast cancer in postmenopausal women.

On the heterozygosity of an admixed population.

In this study, we consider admixed populations through their expected heterozygosity, a measure of genetic diversity. A population is termed admixed if its members possess recent ancestry from two or more separate sources. As a result of the fusion of source populations with different genetic variants, admixed populations can exhibit high levels of genetic diversity, reflecting contributions of their multiple ancestral groups. For a model of an admixed population derived from K source populations, we obtain a relationship between its heterozygosity and its proportions of admixture from the various source populations. We show that the heterozygosity of the admixed population is at least as great as that of the least heterozygous source population, and that it potentially exceeds the heterozygosities of all of the source populations. The admixture proportions that maximize the heterozygosity possible for an admixed population formed from a specified set of source populations are also obtained under specific conditions. We examine the special case of [Formula: see text] source populations in detail, characterizing the maximal admixture in terms of the heterozygosities of the two source populations and the value of [Formula: see text] between them. In this case, the heterozygosity of the admixed population exceeds the maximal heterozygosity of the source groups if the divergence between them, measured by [Formula: see text], is large enough, namely above a certain bound that is a function of the heterozygosities of the source groups. We present applications to simulated data as well as to data from human admixture scenarios, providing results useful for interpreting the properties of genetic variability in admixed populations.

FWER and FDR control when testing multiple mediators.

Motivation: The biological pathways linking exposures and disease risk are often poorly understood. To gain insight into these pathways, studies may try to identify biomarkers that mediate the exposure/disease relationship. Such studies often simultaneously test hundreds or thousands of biomarkers. Results: We consider a set of m biomarkers and a corresponding set of null hypotheses, where the jth null hypothesis states that biomarker j does not mediate the exposure/disease relationship. We propose a Multiple Comparison Procedure (MCP) that rejects a set of null hypotheses or, equivalently, identifies a set of mediators, while asymptotically controlling the Family-Wise Error Rate (FWER) or False Discovery Rate (FDR). We use simulations to show that, compared to currently available methods, our proposed method has higher statistical power to detect true mediators. We then apply our method to a breast cancer study and identify nine metabolites that may mediate the known relationship between an increased BMI and an increased risk of breast cancer. Availability and implementation: R package MultiMed on https://github.com/SiminaB/MultiMed. Supplementary information: Supplementary data are available at Bioinformatics online.

Adapting crowdsourced clinical cancer curation in CIViC to the ClinGen minimum variant level data community-driven standards.

Harmonization of cancer variant representation, efficient communication, and free distribution of clinical variant-associated knowledge are central problems that arise with increased usage of clinical next-generation sequencing. The Clinical Genome Resource (ClinGen) Somatic Working Group (WG) developed a minimal variant level data (MVLD) representation of cancer variants, and has an ongoing collaboration with Clinical Interpretations of Variants in Cancer (CIViC), an open-source platform supporting crowdsourced and expert-moderated cancer variant curation. Harmonization between MVLD and CIViC variant formats was assessed by formal field-by-field analysis. Adjustments to the CIViC format were made to harmonize with MVLD and support ClinGen Somatic WG curation activities, including four new features in CIViC: (1) introduction of an assertions feature for clinical variant assessment following the Association of Molecular Pathologists (AMP) guidelines, (2) group-level curation tracking for organizations, enabling member transparency, and curation effort summaries, (3) introduction of ClinGen Allele Registry IDs to CIViC, and (4) mapping of CIViC assertions into ClinVar submission with automated submissions. A generalizable workflow utilizing MVLD and new CIViC features is outlined for use by ClinGen Somatic WG task teams for curation and submission to ClinVar, and provides a model for promoting harmonization of cancer variant representation and efficient distribution of this information.

Multivariate meta-analysis with an increasing number of parameters.

Meta-analysis can average estimates of multiple parameters, such as a treatment's effect on multiple outcomes, across studies. Univariate meta-analysis (UVMA) considers each parameter individually, while multivariate meta-analysis (MVMA) considers the parameters jointly and accounts for the correlation between their estimates. The performance of MVMA and UVMA has been extensively compared in scenarios with two parameters. Our objective is to compare the performance of MVMA and UVMA as the number of parameters, p, increases. Specifically, we show that (i) for fixed-effect (FE) meta-analysis, the benefit from using MVMA can substantially increase as p increases; (ii) for random effects (RE) meta-analysis, the benefit from MVMA can increase as p increases, but the potential improvement is modest in the presence of high between-study variability and the actual improvement is further reduced by the need to estimate an increasingly large between study covariance matrix; and (iii) when there is little to no between-study variability, the loss of efficiency due to choosing RE MVMA over FE MVMA increases as p increases. We demonstrate these three features through theory, simulation, and a meta-analysis of risk factors for non-Hodgkin lymphoma.

A Dynamic Model for the Evolution of Protein Structure.

Domains are folded structures and evolutionary building blocks of protein molecules. Their three-dimensional atomic conformations, which define biological functions, can be coarse-grained into levels of a hierarchy. Here we build global dynamical models for the evolution of domains at fold and fold superfamily (FSF) levels. We fit the models with data from phylogenomic trees of domain structures and evaluate the distributions of the resulting parameters and their implications. The trees were inferred from a census of domain structures in hundreds of genomes from all three superkingdoms of life. The models used birth-death differential equations with the global abundances of structures as state variables, with one set of equations for folds and another for FSFs. Only the transitions present in the tree are assumed possible. Each fold or FSF diversifies in variants, eventually producing a new fold or FSF. The parameters specify rates of generation of variants and of new folds or FSFs. The equations were solved for the parameters by simplifying the trees to a comb-like topology, treating branches as emerging directly from a trunk. We found that the rate constants for folds and FSFs evolved similarly. These parameters showed a sharp transient change at about 1.5 Gyrs ago. This time coincides with a period in which domains massively combined in proteins and their arrangements distributed in novel lineages during the rise of organismal diversification. Our simulations suggest that exploration of protein structure space occurs through coarse-grained discoveries that undergo fine-grained elaboration.

Testing multiple biological mediators simultaneously.

MOTIVATION: Modern biomedical and epidemiological studies often measure hundreds or thousands of biomarkers, such as gene expression or metabolite levels. Although there is an extensive statistical literature on adjusting for 'multiple comparisons' when testing whether these biomarkers are directly associated with a disease, testing whether they are biological mediators between a known risk factor and a disease requires a more complex null hypothesis, thus offering additional methodological challenges. RESULTS: We propose a permutation approach that tests multiple putative mediators and controls the family wise error rate. We demonstrate that, unlike when testing direct associations, replacing the Bonferroni correction with a permutation approach that focuses on the maximum of the test statistics can significantly improve the power to detect mediators even when all biomarkers are independent. Through simulations, we show the power of our method is 2-5× larger than the power achieved by Bonferroni correction. Finally, we apply our permutation test to a case-control study of dietary risk factors and colorectal adenoma to show that, of 149 test metabolites, docosahexaenoate is a possible mediator between fish consumption and decreased colorectal adenoma risk. AVAILABILITY AND IMPLEMENTATION: R-package included in online Supplementary Material.

Metabolites of tobacco smoking and colorectal cancer risk.

Colorectal cancer is not strictly considered a tobacco-related malignancy, but modest associations have emerged from large meta-analyses. Most studies, however, use self-reported data, which are subject to misclassification. Biomarkers of tobacco exposure may reduce misclassification and provide insight into metabolic variability that potentially influences carcinogenesis. Our aim was to identify metabolites that represent smoking habits and individual variation in tobacco metabolism, and investigate their association with colorectal cancer. In a nested case-control study of 255 colorectal cancers and 254 matched controls identified in the Prostate, Lung, Colorectal and Ovarian cancer screening trial, baseline serum was used to identify metabolites by ultra-high-performance liquid-phase chromatography and mass spectrometry, as well as gas chromatography with tandem mass spectrometry. Odds ratios (OR) and 95% confidence intervals (CI) were estimated by logistic regression. Self-reported current smoking was associated with serum cotinine, O-cresol sulfate and hydroxycotinine. Self-reported current smoking of any tobacco (OR = 1.90, 95% CI: 1.02-3.54) and current cigarette smoking (OR = 1.51, 95% CI: 0.75-3.04) were associated with elevated colorectal cancer risks, although the latter was not statistically significant. Individuals with detectable levels of hydroxycotinine had an increased colorectal cancer risk compared with those with undetectable levels (OR = 2.68, 95% CI: 1.33-5.40). Although those with detectable levels of cotinine had a suggestive elevated risk of this malignancy (OR = 1.81, 95% CI: 0.98-3.33), those with detectable levels of O-cresol sulfate did not (OR = 1.16, 95% CI: 0.57-2.37). Biomarkers capturing smoking behavior and metabolic variation exhibit stronger associations with colorectal cancer than self-report, providing additional evidence for a role for tobacco in this malignancy.

A prospective study of serum metabolites and colorectal cancer risk.

BACKGROUND: Colorectal cancer is highly prevalent, and the vast majority of cases are thought to be sporadic, although few risk factors have been identified. Using metabolomics technology, our aim was to identify biomarkers prospectively associated with colorectal cancer. METHODS: This study included 254 incident colorectal cancers and 254 matched controls nested in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Serum samples were collected at baseline, and the mean length of follow-up was 8 years. Serum metabolites were analyzed by ultra-high performance liquid-phase chromatography with tandem mass spectrometry, and gas chromatography coupled with mass spectrometry. Conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI) for metabolites above the limit of detection and present in at least 80% of participants. RESULTS: A total of 676 serum metabolites were measured; of these, 447 were of known identity and 278 of these were present in >80% of individuals. Overall, there was no association between serum metabolites and colorectal cancer; however, some suggestive associations were observed between individual metabolites and colorectal cancer but none reached statistical significance after Bonferroni correction for multiple comparisons. For example, leucyl-leucine was inversely associated (OR comparing the 90th to the 10th percentile = 0.50; 95% CI = 0.32-0.80; P = .003). In sex-stratified analyses, serum glycochenodeoxycholate was positively associated with colorectal cancer among women (OR(90th vs.10th percentile) = 5.34; 95% CI = 2.09-13.68; P = .0001). CONCLUSIONS: No overall associations were observed between serum metabolites and colorectal cancer, but serum glycochenodeoxycholate, a bile acid metabolite, was positively associated with colorectal cancer among women.

A decision-theory approach to interpretable set analysis for high-dimensional data.

A key problem in high-dimensional significance analysis is to find pre-defined sets that show enrichment for a statistical signal of interest; the classic example is the enrichment of gene sets for differentially expressed genes. Here, we propose a new decision-theory approach to the analysis of gene sets which focuses on estimating the fraction of non-null variables in a set. We introduce the idea of "atoms," non-overlapping sets based on the original pre-defined set annotations. Our approach focuses on finding the union of atoms that minimizes a weighted average of the number of false discoveries and missed discoveries. We introduce a new false discovery rate for sets, called the atomic false discovery rate (afdr), and prove that the optimal estimator in our decision-theory framework is to threshold the afdr. These results provide a coherent and interpretable framework for the analysis of sets that addresses the key issues of overlapping annotations and difficulty in interpreting p values in both competitive and self-contained tests. We illustrate our method and compare it to a popular existing method using simulated examples, as well as gene-set and brain ROI data analyses.

In Vitro and In Vivo Drug-Response Profiling Using Patient-Derived High-Grade Glioma.

BACKGROUND: Genomic profiling cannot solely predict the complexity of how tumor cells behave in their in vivo microenvironment and their susceptibility to therapies. The aim of the study was to establish a functional drug prediction model utilizing patient-derived GBM tumor samples for in vitro testing of drug efficacy followed by in vivo validation to overcome the disadvantages of a strict pharmacogenomics approach. METHODS: High-throughput in vitro pharmacologic testing of patient-derived GBM tumors cultured as 3D organoids offered a cost-effective, clinically and phenotypically relevant model, inclusive of tumor plasticity and stroma. RNAseq analysis supplemented this 128-compound screening to predict more efficacious and patient-specific drug combinations with additional tumor stemness evaluated using flow cytometry. In vivo PDX mouse models rapidly validated (50 days) and determined mutational influence alongside of drug efficacy. We present a representative GBM case of three tumors resected at initial presentation, at first recurrence without any treatment, and at a second recurrence following radiation and chemotherapy, all from the same patient. RESULTS: Molecular and in vitro screening helped identify effective drug targets against several pathways as well as synergistic drug combinations of cobimetinib and vemurafenib for this patient, supported in part by in vivo tumor growth assessment. Each tumor iteration showed significantly varying stemness and drug resistance. CONCLUSIONS: Our integrative model utilizing molecular, in vitro, and in vivo approaches provides direct evidence of a patient's tumor response drifting with treatment and time, as demonstrated by dynamic changes in their tumor profile, which may affect how one would address that drift pharmacologically.

Understanding bias when estimating life expectancy from age at death: a simulation approach applied to Morquio syndrome A.

OBJECTIVE: Life expectancy can be estimated accurately from a cohort of individuals born in the same year and followed from birth to death. However, due to the resource-consuming nature of following a cohort prospectively, life expectancy is often assessed based upon retrospective death record reviews. This conventional approach may lead to potentially biased estimates, in particular when estimating life expectancy of rare diseases such as Morquio syndrome A. We investigated the accuracy of life expectancy estimation using death records by simulating the survival of individuals with Morquio syndrome A under four different scenarios. RESULTS: When life expectancy was constant during the entire period, using death data did not result in a biased estimate. However, when life expectancy increased over time, as is often expected to be the case in rare diseases, using only death data led to a substantial underestimation of life expectancy. We emphasize that it is therefore crucial to understand how estimates of life expectancy are obtained, to interpret them in an appropriate context, and to assess estimation methods within a sensitivity analysis framework, similar to the simulations performed herein.

Real-World Studies Link Nonsteroidal Anti-inflammatory Drug Use to Improved Overall Lung Cancer Survival.

Inflammation is a cancer hallmark. Nonsteroidal anti-inflammatory drugs (NSAIDs) improve overall survival (OS) in certain cancers. Real-world studies explored here if NSAIDs improve non-small cell lung cancer (NSCLC) OS. Analyses independently interrogated clinical databases from The University of Texas MD Anderson Cancer Center (MDACC cohort, 1987 to 2015; 33,162 NSCLCs and 3,033 NSAID users) and Georgetown-MedStar health system (Georgetown cohort, 2000 to 2019; 4,497 NSCLCs and 1,993 NSAID users). Structured and unstructured clinical data were extracted from electronic health records (EHRs) using natural language processing (NLP). Associations were made between NSAID use and NSCLC prognostic features (tobacco use, gender, race, and body mass index, BMI). NSAIDs were statistically-significantly (P < 0.0001) associated with increased NSCLC survival (5-year OS 29.7% for NSAID users versus 13.1% for non-users) in the MDACC cohort. NSAID users gained 11.6 months over nonusers in 5-year restricted mean survival time. Stratified analysis by stage, histopathology and multicovariable assessment substantiated benefits. NSAID users were pooled independent of NSAID type and by NSAID type. Landmark analysis excluded immortal time bias. Survival improvements (P < 0.0001) were confirmed in the Georgetown cohort. Thus, real-world NSAID usage was independently associated with increased NSCLC survival in the MDACC and Georgetown cohorts. Findings were confirmed by landmark analyses and NSAID type. The OS benefits persisted despite tobacco use and did not depend on gender, race, or BMI (MDACC cohort, P < 0.0001). These real-world findings could guide future NSAID lung cancer randomized trials.

Mathematical properties of Fst between admixed populations and their parental source populations.

We consider the properties of the F(st) measure of genetic divergence between an admixed population and its parental source populations. Among all possible populations admixed among an arbitrary set of parental populations, we show that the value of F(st) between an admixed population and a specific source population is maximized when the admixed population is simply the most distant of the other source populations. For the case with only two parental populations, as a function of the admixture fraction, we further demonstrate that this F(st) value is monotonic and convex, so that F(st) is informative about the admixture fraction. We illustrate our results using example human population-genetic data, showing how they provide a framework in which to interpret the features of F(st) in admixed populations.

Integrated analysis of homozygous deletions, focal amplifications, and sequence alterations in breast and colorectal cancers.

We have performed a genome-wide analysis of copy number changes in breast and colorectal tumors using approaches that can reliably detect homozygous deletions and amplifications. We found that the number of genes altered by major copy number changes, deletion of all copies or amplification to at least 12 copies per cell, averaged 17 per tumor. We have integrated these data with previous mutation analyses of the Reference Sequence genes in these same tumor types and have identified genes and cellular pathways affected by both copy number changes and point alterations. Pathways enriched for genetic alterations included those controlling cell adhesion, intracellular signaling, DNA topological change, and cell cycle control. These analyses provide an integrated view of copy number and sequencing alterations on a genome-wide scale and identify genes and pathways that could prove useful for cancer diagnosis and therapy.

An Open-Publishing Response to the COVID-19 Infodemic.

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript's figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis.