Biologically consistent annotation of CHO cell culture metabolomics data

Metabolomics represents the effort to understand the role of metabolites in a biological system. Unfortunately, unambiguous metabolite identification represents a major bottleneck in liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics. A widely used approach is to search spectral (MS/MS) libraries in reference databases for matching metabolites; however, this approach is limited by incomplete coverage. An alternative approach is to match detected features to candidate chemical structures based on their mass and computationally predicted fragmentation pattern. Both approaches often return too many possible matches; moreover, the results from different annotation tools rarely agree. This presentation describes a novel annotation tool that combines search results from several MS/MS libraries and computational fragmentation tools, and evaluates these results based on the content of a metabolic model. This captures the relevant biological context to determine the most likely identity for a given LC-MS data feature. This workflow, termed Biologically Consistent Annotation (BioCAn), improves on other publicly available annotation tools, achieving superior accuracy and sensitivity, while reducing the false discovery rate. The utility of this tool for investigating metabolic inefficiencies in cell culture processes is demonstrated by identifying novel CHO cell metabolites associated with enhanced or reduced cell growth and monoclonal antibody production. The function of these metabolites was evaluated in shake flask and controlled bioreactor experiments

Understanding and overcoming process insults through application of ‘omics technologies

Modern industrial process development, at both small and large corporations, usually consists of applying a well-characterized and established cell culture platform. Despite the high productivity available from these process platforms, difficult challenges remain, including with respect to the ability of the process to endure insults or disruptions. We previously demonstrated that overfeeding resulted in an undesirable increase in lactate production late in fed batch culture, which decreased productivity[i]. Here we report on metabolic flux analysis performed utilizing this process and isotopically labeling with multiple tracers (glucose and glutamate) delivered at five distinct time points of the cell culture process. Notably, we identified unexpected behavior within the tricarboxylic acid (TCA) cycle. The corresponding labeling data indicated a significant redistribution of the fluxes in and around the TCA cycle. Understanding the intracellular changes occurring when cells are challenged with a process insult, such as overfeeding, should lead to enhanced process development. Consequently metabolic flux analysis is only the first step in improving the process. We have identified two medium supplements which each independently permit the cell culture to endure overfeeding and result in maintaining or increasing titer despite the process insult. The overfed process and the supplemented processes were utilized to evaluate changes in the cellular metabolism with an untargeted metabolomics approach. Novel findings from the untargeted metabolomics approach when combined with metabolic flux analysis give a complete picture of the cellular metabolism as both reaction rates and relative concentrations are known over the full process duration. With this knowledge in hand, the platform process can evolve to routinely overcome process insults such as overfeeding

China, Europe, and the Pandemic Recession: Beijing’s Investments and Transatlantic Security

Given the depth and breadth of the pandemic-induced recession in Europe, private companies in need of capital and governments looking to shed state-owned enterprises may be tempted to sell shares, assets, or outright ownership to investors with liquidity to spare. Of greatest concern is the role that China might play in Europe, building Beijing’s soft power, weakening allied geopolitical solidarity, and potentially reprising the role it played in the 2010s, when its investments in Europe expanded dramatically. More specifically, there is concern over China’s investments in infrastructure and sensitive technologies relevant to American and allied military operations and capabilities. Whether Europe is prepared and able to parry Beijing’s economic statecraft is somewhat unclear, given varied attitudes toward China and the patchwork of investment screening mechanisms across the continent. Regardless, the outcomes will have significant implications for US security and for the Defense Department specifically. In support of US European Command (EUCOM) and the Department of Homeland Security (DHS), the U.S. Army War College’s Strategic Studies Institute (SSI) assembled an interdisciplinary team to examine these issues and offer actionable policy recommendations for military leaders and decisionmakers on both sides of the Atlantic. Study sponsors (nonfunding): United States European Command, United States Department of Homeland Securityhttps://press.armywarcollege.edu/monographs/1945/thumbnail.jp

Synthesis and evaluation of human phosphodiesterases (PDE) 5 inhibitor analogs as trypanosomal PDE inhibitors. 2. Tadalafil analogs

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Bioorganic & Medicinal Chemistry Letters 22 (2012): 2582-2584, doi:10.1016/j.bmcl.2012.01.118.In this report we describe our ongoing target repurposing efforts focused on discovery of inhibitors of the essential trypanosomal phosphodiesterase TbrPDEB1. This enzyme has been implicated in virulence of Trypanosoma brucei, the causative agent of human African trypanosomiasis (HAT). We outline the synthesis and biological evaluation of analogs of tadalafil, a human PDE5 inhibitor currently utilized for treatment of erectile dysfunction, and report that these analogs are weak inhibitors of TbrPDEB1.This work was supported by the National Institutes of Health (R01AI082577), Boston University and Northeastern University

Water Rights on Community Lands:LandMark’s Findings from 100 Countries

This paper analyzes whether national laws acknowledge indigenous peoples and other rural communities in 100 countries as owners of waters that arise within their lands. Results derive from information collected by LandMark to score the legal status of community land tenure. Findings are positive; half of all countries recognize communities as lawful possessors of water on their lands. Three quarters permit communities to manage the distribution and use of water on their lands. While 71 percent of countries declare water to be a public resource, this belies the substantial existence of privately owned water. In 29 percent of countries, private water is an identified legal category, and in many other countries obtainable rights to water are sufficiently substantial to imply lawful possession. Communities are beneficiaries mainly where customary rights are accorded status as property rights, or where ownership of public lands and water are devolved to rural collectives. However, opposite trends of nationalization and regulation of water suggest that while legal recognition of community land ownership may rise in the future, this will not necessarily include waters on the land. Irrespective of tenure, rural communities in 72 of 77 countries (93.5 percent) are legally assured access to water for domestic purposes. This is consistent with the rising definition of safe drinking water as a human right, although access does not necessarily come free of cost

Synthesis and evaluation of human phosphodiesterases (PDE) 5 inhibitor analogs as trypanosomal PDE inhibitors. 1. Sildenafil analogs

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Bioorganic & Medicinal Chemistry Letters 22 (2012): 2579-2581, doi:10.1016/j.bmcl.2012.01.119.Parasitic diseases, such as African sleeping sickness, have a significant impact on the health and well-being in the poorest regions of the world. Pragmatic drug discovery efforts are needed to find new therapeutic agents. In this report we describe target repurposing efforts focused on trypanosomal phosphodiesterases. We outline the synthesis and biological evaluation of analogs of sildenafil (1), a human PDE5 inhibitor, for activities against trypanosomal PDEB1 (TbrPDEB1). We find that, while low potency analogs can be prepared, this chemical class is a sub-optimal starting point for further development of TbrPDE inhibitors.This work was supported by the National Institutes of Health (R01AI082577), Boston University and Northeastern University

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks