Assessing the accuracy of blood RNA profiles to identify patients with post-concussion syndrome: A pilot study in a military patient population

<div><p>Mild traumatic brain injury (mTBI) is a complex, neurophysiological condition that can have detrimental outcomes. Yet, to date, no objective method of diagnosis exists. Physical damage to the blood-brain-barrier and normal waste clearance via the lymphatic system may enable the detection of biomarkers of mTBI in peripheral circulation. Here we evaluate the accuracy of whole transcriptome analysis of blood to predict the clinical diagnosis of post-concussion syndrome (PCS) in a military cohort. Sixty patients with clinically diagnosed chronic concussion and controls (no history of concussion) were recruited (retrospective study design). Male patients (46) were split into a training set comprised of 20 long-term concussed (> 6 months and symptomatic) and 12 controls (no documented history of concussion). Models were validated in a testing set (control = 9, concussed = 5). RNA_Seq libraries were prepared from whole blood samples for sequencing using a SOLiD5500XL sequencer and aligned to hg19 reference genome. Patterns of differential exon expression were used for diagnostic modeling using support vector machine classification, and then validated in a second patient cohort. The accuracy of RNA profiles to predict the clinical diagnosis of post-concussion syndrome patients from controls was 86% (sensitivity 80%; specificity 89%). In addition, RNA profiles reveal duration of concussion. This pilot study shows the potential utility of whole transcriptome analysis to establish the clinical diagnosis of chronic concussion syndrome.</p></div

Exon expression pattern of concussion predicts clinical diagnosis.

<p>A. Hierarchical clustering shows pattern of separation between concussed and controls based on expression of 29 exons (x-axis) B. Principle component analysis plot shows the exon expression patterns separate due to concussion status. C Results of testing SVM generated model of 25 exons (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183113#pone.0183113.t002" target="_blank">Table 2</a>) on testing data set, using a diagnosis of PCS/ concussion as the positive call. Note the accuracy was 86%, sensitivity 80% and specificity of 89%.</p

Exon expression values that give most accurate prediction of chronic concussion in testing data set.

<p>Note exon values give chromosomal location, based on RefSeq annotation guide.</p

Flow chart of study.

<p>Potential participants (62) were recruited from the Neuroscience and Rehabilitation Center–TBI Clinic at the Dwight D. Eisenhower Army Medical Hospital, Fort Gordon, Georgia. Two were found to be ineligible for the study due to moderate or severe TBI/tumor upon further review of medical chart. From the remaining 60 patients, we excluded 4 samples due to failed library QC, low RNA yield or abnormal mapping. Females were ultimately excluded from the study due to low recruitment (n = 11; one with failed library QC). Subjects (n = 46) were split into a modeling set and a second validation set. The modeling set was used to create an algorithm (Index test) for determining the clinical diagnosis of post-concussion syndrome (positive outcome). This was compared to the confirmation of cognitive symptoms consistent with post-concussion sequelae (Reference test). The algorithm was tested on the modeling data set (n = 32) and the test data set (n = 14). Performance on the modeling data was 94% accuracy, and 86% accuracy in the independent validation set.</p

Demographics of males subjects in the study.

<p>Demographics of males subjects in the study.</p

Variants in GCNA, X-linked germ-cell genome integrity gene, identified in men with primary spermatogenic failure

Free PMC article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266742/pdf/nihms-1705620.pdfGEMINI Consortium: Donald F Conrad, Liina Nagirnaja, Kenneth I Aston, Douglas T Carrell, James M Hotaling, Timothy G Jenkins, Rob McLachlan, Moira K O'Bryan, Peter N Schlegel, Michael L Eisenberg, Jay I Sandlow, Emily S Jungheim, Kenan R Omurtag, Alexandra M Lopes, Susana Seixas, Filipa Carvalho, Susana Fernandes, Alberto Barros, João Gonçalves, Iris Caetano, Graça Pinto, Sónia Correia, Maris Laan, Margus Punab, Ewa Rajpert-De Meyts, Niels Jørgensen, Kristian Almstrup, Csilla G Krausz, Keith A Jarvi.Member of GEMINI Consortium: João Gonçalves (INSA), lista completa na pág 1179.Male infertility impacts millions of couples yet, the etiology of primary infertility remains largely unknown. A critical element of successful spermatogenesis is maintenance of genome integrity. Here, we present a genomic study of spermatogenic failure (SPGF). Our initial analysis (n=176) did not reveal known gene-candidates but identifed a potentially signifcant single-nucleotide variant (SNV) in X-linked germ-cell nuclear antigen (GCNA). Together with a larger follow-up study (n=2049), 7 likely clinically relevant GCNA variants were identifed. GCNA is critical for genome integrity in male meiosis and knockout models exhibit impaired spermatogenesis and infertility. Single-cell RNA-seq and immunohistochemistry confrm human GCNA expression from spermatogonia to elongated spermatids. Five identifed SNVs were located in key functional regions, including N-terminal SUMO-interacting motif and C-terminal Spartan-like protease domain. Notably, variant p.Ala115ProfsTer7 results in an early frameshift, while Spartan-like domain missense variants p.Ser659Trp and p.Arg664Cys change conserved residues, likely afecting 3D structure. For variants within GCNA’s intrinsically disordered region, we performed computational modeling for consensus motifs. Two SNVs were predicted to impact the structure of these consensus motifs. All identifed variants have an extremely low minor allele frequency in the general population and 6 of 7 were not detected in>5000 biological fathers. Considering evidence from animal models, germ-cell-specifc expression, 3D modeling, and computational predictions for SNVs, we propose that identifed GCNA variants disrupt structure and function of the respective protein domains, ultimately arresting germ-cell division. To our knowledge, this is the frst study implicating GCNA, a key genome integrity factor, in human male infertility.This study was supported by The Eunice Kennedy Shriver NICHD Grant HD080755 (ANY), the Magee-Womens Research Institute University of Pittsburgh Start Up Fund (ANY), PA DoH Grant SAP4100085736 (ANY), NIH P50 Specialized Center Grant HD096723 (KO, ANY, DC, PNS, KH, and MBE), German Research Foundation Clinical Research Unit ‘Male Germ Cells’ grant DFG CRU326 (FT), National Science Centre in Poland, grants no.: 2017/26/D/NZ5/00789 (AM) and 2015/17/B/NZ2/01157; NCN 2020/37/B/NZ5/00549 (MK), Magee-Womens Research Institute University of Pittsburgh, Faculty Fellowship Award and NICHD T32 HD087194 (JH), GM125812 (MB), GM127569 (MB, JLY, and ANY), NIH R00H090289 (MABE), National Health and Medical Research Council Project grant APP1120356 (MKOB, JAV, and DC), UUKi Rutherford Fund Fellowship (BJH), Estonian Research Council, grants IUT34-12 and PRG1021 (ML), and The Netherlands Organization for Scientifc Research grant no.: 918-15-667 as well as an Investigator Award in Science from the Wellcome Trust grant no.: 209451 (JAV). Computational analysis was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided.info:eu-repo/semantics/publishedVersio