Biomarkers play critical roles in early detection, diagnosis and monitoring of therapeutic outcome and recurrence of cancer. Previous biomarker research on ovarian cancer (OC) has mostly focused on the discovery and validation of diagnostic biomarkers. The primary purpose of this study is to identify serum biomarkers for prognosis and therapeutic outcomes of ovarian cancer.

Dual Energy X-ray Absorptiometry (DXA) is effective in measuring bone mineral density (BMD) in mice for early detection of osteoporosis. However, scanners designed for use with small animals (i.e. PIXImus) are very expensive. Used human DXA machines are cheaper to obtain, but analysis of scans from these instruments is operator-dependent. Obtaining reliable data depends on having a single operator analyze the scans in a blinded fashion. Scan quality is improved by excising the bone prior to scanning, which does not allow serial measurements. This study describes a novel method of analyzing lumbar spine BMD in mice using whole body DXA. This non-invasive technique has a high degree of precision and reproducibility, with good correlation between multiple observers. Inter-observer variability (0.063 +/- 0.00317 g/cm(2) [mean +/- SD], 5.05 [% coefficient of variation (CV)], repeat scan variability (0.063 +/- 0.00364 g/cm(2) [mean +/- SD], 5.94 [%CV]) were very low compared to variability between different animals (0.063 +/- 0.00588 g/cm(2) [mean +/- SD], 9.64 [%CV]) and variability seen in same animal over time (0.011 +/- 0.00885 g/cm(2) [mean +/- SD], 80.68 [%CV]). The measurement error is thus smaller than the biological variation. Accuracy was determined by comparing average peak BMD from two scans per mouse in-vivo (0.066 g/cm(2)) versus excised spine (0.065 g/cm(2)). Furthermore, correlation between bone ash weights and whole body lumbar spine BMD measurements (p < 0.0001) was highly significant. This technique thus shows a high degree of precision and accuracy, even with multiple observers, for measuring BMD in mice using a DXA machine designed for clinical use.

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what "mental processes" exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain.

Bone marrow lesions (BMLs), common osteoarthritis-related magnetic resonance imaging findings, are associated with osteoarthritis progression and pain. However, there are no articles describing the use of 3-dimensional quantitative assessments to explore the longitudinal relationship between BMLs and hyaline cartilage loss. The purpose of this study was to assess the cross-sectional and longitudinal descriptive characteristics of BMLs with a simple measurement of approximate BML volume, and describe the cross-sectional and longitudinal relationships between BML size and the extent of hyaline cartilage damage.

The advancement of the computational biology field hinges on progress in three fundamental directions--the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources--data, software tools and web-services. The iTools design, implementation and resource meta-data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource management. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu.

KLHL family genes are noted for their involvement in the E3 ligase ubiquitination pathway through binding with Cullin-3 (CUL3) resulting in degradation of specific binding partners. KLHLs are thus intriguing genes for cancer as they can directly influence the degradation of therapeutically relevant cell cycle regulators such as Aurora Kinase, PLK1, or CDK1. However, most KLHL family members remain understudied within the literature. This study explores the relationship of expression of KLHL member, KLHL5, with the pharmacologic effect of anti-cancer drugs. KLHL5 knockdown decreased the proliferation and viability of cancer cells and sensitized cancer cells to numerous anti-cancer drugs. Drugs related to cell cycle including Akt/PI3K/mTOR inhibitors were especially sensitized by KLHL5 knockdown. The potential of KLHL5 as a prognostic or diagnostic cancer marker was compared to other KLHLs through a pan-cancer study of The Cancer Genome Atlas (TCGA) tumor groups. While KLHL5 expression shows marginal dysregulation in cancer, other KLHLs exhibit significant dysregulation in all cancer types, and exceptionally in renal carcinomas. This study advocates for further study of KLHLs as potential alternative therapeutic targets, since while KLHL5 is a novel gene impacting anticancer drug effects, others may have a similar impact on drug effect while having greater potential as diagnostic or prognostic markers.

The molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu.

The heterogeneity of the breast tumor microenvironment (TME) may contribute to the lack of durable responses to immune checkpoint blockade (ICB); however, mouse models to test this are currently lacking. Proper selection and use of preclinical models are necessary for rigorous, preclinical studies to rapidly move laboratory findings into the clinic.

The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.

SARS-CoV-2, the coronavirus that causes the disease COVID-19, has claimed millions of lives over the past 2 years. This demands rapid development of effective therapeutic agents that target various phases of the viral replication cycle. The interaction between host transmembrane serine protease 2 (TMPRSS2) and viral SPIKE protein is an important initial step in SARS-CoV-2 infection, offering an opportunity for therapeutic development of viral entry inhibitors. Here, we report the development of a time-resolved fluorescence/Förster resonance energy transfer (TR-FRET) assay for monitoring the TMPRSS2-SPIKE interaction in lysate from cells co-expressing these proteins. The assay was configured in a 384-well-plate format for high-throughput screening with robust assay performance. To enable large-scale compound screening, we further miniaturized the assay into 1536-well ultrahigh-throughput screening (uHTS) format. A pilot screen demonstrated the utilization of the assay for uHTS. Our optimized TR-FRET uHTS assay provides an enabling platform for expanded screening campaigns to discover new classes of small-molecule inhibitors that target the SPIKE and TMPRSS2 protein-protein interaction.

The disruption of neuron arrangement is associated with several pathologies. In contrast to action potentials, the role of resting potential (Vmem) in regulating connectivity remains unknown.

Phenomics is an emerging transdiscipline dedicated to the systematic study of phenotypes on a genome-wide scale. New methods for high-throughput genotyping have changed the priority for biomedical research to phenotyping, but the human phenome is vast and its dimensionality remains unknown. Phenomics research strategies capable of linking genetic variation to public health concerns need to prioritize development of mechanistic frameworks that relate neural systems functioning to human behavior. New approaches to phenotype definition will benefit from crossing neuropsychiatric syndromal boundaries, and defining phenotypic features across multiple levels of expression from proteome to syndrome. The demand for high throughput phenotyping may stimulate a migration from conventional laboratory to web-based assessment of behavior, and this offers the promise of dynamic phenotyping-the iterative refinement of phenotype assays based on prior genotype-phenotype associations. Phenotypes that can be studied across species may provide greatest traction, particularly given rapid development in transgenic modeling. Phenomics research demands vertically integrated research teams, novel analytic strategies and informatics infrastructure to help manage complexity. The Consortium for Neuropsychiatric Phenomics at UCLA has been supported by the National Institutes of Health Roadmap Initiative to illustrate these principles, and is developing applications that may help investigators assemble, visualize, and ultimately test multi-level phenomics hypotheses. As the transdiscipline of phenomics matures, and work is extended to large-scale international collaborations, there is promise that systematic new knowledge bases will help fulfill the promise of personalized medicine and the rational diagnosis and treatment of neuropsychiatric syndromes.

Our study aimed to investigate the short-term effect of combination antiretroviral therapy (cART) on cognitive performance and functional and structural connectivity and their relationship to plasma levels of antiretroviral (ARV) drugs. Seventeen ARV treatment-naïve HIV-infected individuals (baseline mean CD4 cell count, 479 ± 48 cells/mm3) were age matched with 17 HIV-uninfected individuals. All subjects underwent a detailed neurocognitive and functional assessment and magnetic resonance imaging. HIV-infected subjects were scanned before starting cART and 12 weeks after initiation of treatment. Uninfected subjects were assessed once at baseline. Functional connectivity (FC) was assessed within the default mode network while structural connectivity was assessed by voxel-wise analysis using tract-based spatial statistics (TBSS) and probabilistic tractography within the DMN. Tenofovir and emtricitabine blood concentration were measured at week 12 of cART. Prior to cART, HIV-infected individuals had significantly lower cognitive performance than control subjects as measured by the total Z-score from the neuropsychological tests assessing six cognitive domains (p = 0.020). After 12 weeks of cART treatment, there remained only a weak cognitive difference between HIV-infected and HIV-uninfected subjects (p = 0.057). Mean FC was lower in HIV-infected individuals compared with those uninfected (p = 0.008), but FC differences became non-significant after treatment (p = 0.197). There were no differences in DTI metrics between HIV-infected and HIV-uninfected individuals using the TBSS approach and limited evidence of decreased structural connectivity within the DMN in HIV-infected individuals. Tenofovir and emtricitabine plasma concentrations did not correlate with either cognitive performance or imaging metrics.

The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial is a prospective cohort study of nearly 155,000 U.S. volunteers aged 55-74 at enrollment in 1993-2001. We developed the PLCO Atlas Project, a large resource for multi-trait genome-wide association studies (GWAS), by genotyping participants with available DNA and genomic consent. Genotyping on high-density arrays and imputation was performed, and GWAS were conducted using a custom semi-automated pipeline. Association summary statistics were generated from a total of 110,562 participants of European, African and Asian ancestry. Application programming interfaces (APIs) and open-source software development kits (SKDs) enable exploring, visualizing and open data access through the PLCO Atlas GWAS Explorer website, promoting Findable, Accessible, Interoperable, and Re-usable (FAIR) principles. Currently the GWAS Explorer hosts association data for 90 traits and >78,000,000 genomic markers, focusing on cancer and cancer-related phenotypes. New traits will be posted as association data becomes available. The PLCO Atlas is a FAIR resource of high-quality genetic and phenotypic data with many potential reuse opportunities for cancer research and genetic epidemiology.

Pneumococcal bacteriocins (pneumocins) are antibacterial toxins that mediate intra-species competition within the human host. However, the triggers of pneumocin expression are poorly understood. Using RNA-sequencing, we mapped the regulon of the pneumocin cluster (blp) of Streptococcus pneumoniae D39. Furthermore, by analogy with pneumococcal competence, we show that several antibiotics activate the blp-genes. Using real-time gene expression measurements we show that while the promoter driving expression of the two-component regulatory system blpR/H is constitutive, the remaining blp-promoters that control pneumocin expression, immunity and the inducer peptide BlpC, are pH-dependent and induced in the late exponential phase. Intriguingly, competence for genetic transformation, mediated by the paralogous ComD/E two-component quorum system, is induced by the same environmental cues. To test for interplay between these regulatory systems, we quantified the regulatory response to the addition of synthetic BlpC and competence-stimulating peptide (CSP). Supporting the idea of such interplay, we found that immediately upon addition of CSP, the blp-promoters were activated in a comD/E-dependent manner. After a delay, blp-expression was highly induced and was strictly dependent on blpRH and blpC. This raised the question of the mechanism of BlpC export, since bioinformatic analysis showed that the genes encoding the putative exporter for BlpC, blpAB, are not intact in strain D39 and most other strains. By contrast, all sequenced pneumococcal strains contain intact comAB genes, encoding the transport system for CSP. Consistent with the idea that comAB mediate BlpC export, we finally show that high-level expression of the blp-genes requires comAB. Together, our results demonstrate that regulation of pneumocin expression is intertwined with competence, explaining why certain antibiotics induce blp-expression. Antibiotic-induced pneumocin expression might therefore have unpredictable consequences on pneumococcal colonization dynamics by activating genes that mediate intra-specific interference competition.

Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examining the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of voxelwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent component analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research.

Mutation rates vary both within and between bacterial species, and understanding what drives this variation is essential for understanding the evolutionary dynamics of bacterial populations. In this study, we investigate two factors that are predicted to influence the mutation rate: ecology and genome size. We conducted mutation accumulation experiments on eight strains of the emerging zoonotic pathogen Streptococcus suis. Natural variation within this species allows us to compare tonsil carriage and invasive disease isolates, from both more and less pathogenic populations, with a wide range of genome sizes. We find that invasive disease isolates have repeatedly evolved mutation rates that are higher than those of closely related carriage isolates, regardless of variation in genome size. Independent of this variation in overall rate, we also observe a stronger bias towards G/C to A/T mutations in isolates from more pathogenic populations, whose genomes tend to be smaller and more AT-rich. Our results suggest that ecology is a stronger correlate of mutation rate than genome size over these timescales, and that transitions to invasive disease are consistently accompanied by rapid increases in mutation rate. These results shed light on the impact that ecology can have on the adaptive potential of bacterial pathogens.