Stacking the Deck against Suspected Terrorists: The Dwindling Procedural Limits on the Government\u27s Power to Indefinitely Detain United States Citizens as Enemy Combatants

This Note examines Padilla v. Bush as an example of the contemporary application of enemy combatant law. This Note argues that in present and future applications of enemy combatant law, courts should treat Padilla as the preferred model of application because Padilla preserves more Constitutional protections, specifically the right to counsel in bringing a habeas petition, than do Hamdi or Quirin. The Padilla decision is preferable to Hamdi because Padilla restricts the movement of enemy combatant law away from the ex- press criminal protections of the Constitution. In contrast, Hamdi greatly accelerates such movement

Stacking the Deck against Suspected Terrorists: The Dwindling Procedural Limits on the Government\u27s Power to Indefinitely Detain United States Citizens as Enemy Combatants

This Note examines Padilla v. Bush as an example of the contemporary application of enemy combatant law. This Note argues that in present and future applications of enemy combatant law, courts should treat Padilla as the preferred model of application because Padilla preserves more Constitutional protections, specifically the right to counsel in bringing a habeas petition, than do Hamdi or Quirin. The Padilla decision is preferable to Hamdi because Padilla restricts the movement of enemy combatant law away from the ex- press criminal protections of the Constitution. In contrast, Hamdi greatly accelerates such movement

Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality

Genome-wide association studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metabolism. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 associated loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted 1H NMR analysis of urine samples from 3,861 participants of the SHIP-0 cohort and 1,691 subjects of the KORA F4 cohort. We identified and replicated 22 loci with significant associations with urinary traits, 15 of which are new (HIBCH, CPS1, AGXT, XYLB, TKT, ETNPPL, SLC6A19, DMGDH, SLC36A2, GLDC, SLC6A13, ACSM3, SLC5A11, PNMT, SLC13A3). Two-thirds of the urinary loci also have a metabolite association in blood. For all but one of the 6 loci where significant associations target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the number of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease (CPS1, SLC6A13), pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about molecular mechanisms involved in the etiology of diseases

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis

Gene transcripts associated with muscle strength: a CHARGE meta-analysis of 7,781 persons

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Background: Lower muscle strength in midlife predicts disability and mortality in later life. Bloodborne factors, including growth differentiation factor 11 (GDF11), have been linked to muscle regeneration in animal models. We aimed to identify gene transcripts associated with muscle strength in adults. Methods: Meta-analysis of whole blood gene expression (overall 17,534 unique genes measured by microarray) and hand-grip strength in four independent cohorts (n=7,781, ages: 20-104 years, weighted mean=56), adjusted for age, sex, height, weight, and leukocyte subtypes. Separate analyses were performed in subsets (older/younger than 60, male/female). Results: Expression levels of 221 genes were associated with strength after adjustment for cofactors and for multiple statistical testing, including ALAS2 (rate limiting enzyme in heme synthesis), PRF1 (perforin, a cytotoxic protein associated with inflammation), IGF1R and IGF2BP2 (both insulin like growth factor related). We identified statistical enrichment for hemoglobin biosynthesis, innate immune activation and the stress response. Ten genes were only associated in younger individuals, four in males only and one in females only. For example PIK3R2 (a negative regulator of PI3K/AKT growth pathway) was negatively associated with muscle strength in younger (=60 years). We also show that 115 genes (52%) have not previously been linked to muscle in NCBI PubMed abstracts Conclusions: This first large-scale transcriptome study of muscle strength in human adults confirmed associations with known pathways and provides new evidence for over half of the genes identified. There may be age and sex specific gene expression signatures in blood for muscle strength.Wellcome TrustFHS gene expression profiling was funded through the Division of Intramural Research (Principal Investigator, Daniel Levy), National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD. Dr. Murabito is supported by NIH grant R01AG029451. Dr. Kiel is supported by NIH R01 AR41398. The Framingham Heart Study is supported by National Heart, Lung, and Blood Institute contract N01-HC-25195.The InCHIANTI study was supported in part by the Intramural Research Program, National Institute on Aging, NIH, Baltimore MD USA. D.M. and L.W.H. were generously supported by a Wellcome Trust Institutional Strategic Support Award (WT097835MF). W.E.H. was funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for the South West Peninsula. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health in EnglandThe infrastructure for the NESDA study (www.nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant number 10-000-1002) and is supported by participating universities and mental health care organizations (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Scientific Institute for Quality of Healthcare (IQ healthcare), Netherlands Institute for Health Services Research (NIVEL) and Netherlands Institute of Mental Health and Addiction (Trimbos Institute).The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Research Institute for Diseases in the Elderly (014-93- 28 015; RIDE2), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of RNA-expression array data for the Rotterdam Study was executed and funded by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Netherlands. We thank Marjolein Peters, MSc, Ms. Mila Jhamai, Ms. Jeannette M. Vergeer-Drop, Ms. Bernadette van Ast-Copier, Mr. Marijn Verkerk and Jeroen van Rooij, BSc for their help in creating the RNA array expression databaseSHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the network ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ funded by the Federal Ministry of Education and Research (grant 03IS2061A). The University of Greifswald is a member of the 'Center of Knowledge Interchange' program of the Siemens AG and the Caché Campus program of the InterSystems GmbH

Network analysis methods for studying microbial communities:A mini review

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.</p

Alternative splicing impacts microRNA regulation within coding regions

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation. To check this hypothesis, we studied the role of miRNA target sites in both coding and non-coding regions using six cancer data sets from The Cancer Genome Atlas (TCGA) and Parkinson's disease data from PPMI. First, we predicted miRNA target sites on mRNAs from their sequence using TarPmiR. To check whether alternative splicing interferes with this regulation, we trained linear regression models to predict miRNA expression from transcript expression. Using nested models, we compared the predictive power of transcripts with miRNA target sites in the coding regions to that of transcripts without target sites. Models containing transcripts with target sites perform significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.</p

The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping

Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three, four or five subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of four established signatures for PDAC stratification across nine publicly available datasets. Clustering revealed inconsistency of subtypes across independent datasets and in some cases a different number of PDAC subgroups than in the original study, casting doubt on the actual number of existing subtypes. Next, we built sixteen classification models to investigate the ability of the signatures for tumor subtype prediction. The overall classification performance ranged from ∼35% to ∼90% accuracy, suggesting instability of the signatures. Notably, permuted subtypes and random gene sets achieved very similar performance. Cellular decomposition and functional pathway enrichment analysis revealed strong tissue-specificity of the predicted classes. Our study highlights severe limitations and inconsistencies that can be attributed to technical biases in sample preparation and tumor purity, suggesting that PDAC molecular signatures do not generalize across datasets. How stromal heterogeneity and immune compartment interplay in the diverging development of PDAC is still unclear. Therefore, a more mechanistic or a cross-platform multi-omic approach seems necessary to extract more robust and clinically exploitable insights.</p

Systematic analysis of alternative splicing in time course data using Spycone

Motivation: During disease progression or organism development, alternative splicing may lead to isoform switches that demonstrate similar temporal patterns and reflect the alternative splicing co-regulation of such genes. Tools for dynamic process analysis usually neglect alternative splicing. Results: Here, we propose Spycone, a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection.</p