Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra

The decreasing costs and increasing speed and accuracy of DNA sample collection, preparation, and sequencing has rapidly produced an enormous volume of genetic data. However, fast and accurate analysis of the samples remains a bottleneck. Here we present D$^{4}$RAGenS, a genetic sequence identification algorithm that exhibits the Big Data handling and computational power of the Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear algebra and statistical properties to increase computational performance while retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield speed and precision tradeoffs, with applications in biodefense and medical diagnostics. The D$^{4}$RAGenS analysis algorithm is tested over several datasets, including three utilized for the Defense Threat Reduction Agency (DTRA) metagenomic algorithm contest

STR profile authentication via machine learning techniques

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 169-171).Short tandem repeat (STR) DNA profiles have multiple uses in forensic analysis, kinship identification, and human biometrics. However, as biotechnology progresses, there is a growing concern that STR profiles can be created using standard laboratory techniques such as whole genome amplification and molecular cloning. Such technologies can be used to synthesize any STR profile without the need for a physical sample, only knowledge of the desired genetic sequence. Therefore, to preserve the credibility of DNA as a forensic tool, it is imperative to develop means to authenticate STR profiles. The leading technique in the field, methylation analysis, is accurate but also expensive, time-consuming, and degrades the forensic sample so that further analysis is not possible. The realm of machine learning offers techniques to address the need for more effective STR profile authentication. In this work, a set of features were identified at both the channel and profile levels of STR electropherograms. A number of supervised and unsupervised machine learning algorithms were then used to predict whether a given STR electropherogram was authentic or synthesized by laboratory techniques. With the aid of the LNKnet machine learning toolkit, various classifiers were trained with the default set of parameters and the full set of features to quantify their baseline performance. Particular emphasis was placed on detecting profiles generated by Whole Genome Amplification (WGA). A greedy forward-backward search algorithm was implemented to determine the most useful subset of features from the initial group. Though the set of optimal feature values varied by classifier, a trend was observed indicating that the inter-locus imbalance error, stutter count, and range of peak widths for a profile were particularly useful features. These were selected by over two thirds of the classifiers. The signal-to- noise ratio was also a useful feature, selected by seven out of 16 classifiers. The selected features were in turn used to tune the parameters of machine learning algorithms and to compare their performance. From a set of 16 initial classifiers, the K-nearest neighbors, condensed K-nearest neighbors, multi-layer perceptron, Parzen window, and support vector machine classifiers achieved the best performance. These classification algorithms all attained error rates of approximately ten percent, defined as the percentage of profiles misclassified with the highest performing classifier achieving an error rate of less than eight percent. Overall, the classifiers performed well at detecting artificial profiles but had more difficulty accurately distinguishing natural profiles. There were many false positives for the artificial class, since profiles in this category took on a greater range of feature values. Finally, preliminary steps were taken to form classifier committees. However, combining the top performing classifiers via a majority vote did not significantly improve performance. The results of this work demonstrate the feasibility of a completely software-based approach to profile authentication. They confirm that machine learning techniques are a useful tool to trigger further investigation of profile authenticity via more expensive approaches.by Anna Shcherbina.M.Eng

Moesin, the major ERM protein of lymphocytes and platelets, differs from ezrin in its insensitivity to calpain

AbstractThe ERM proteins, ezrin, radixin and moesin, provide regulated linkage of the cytoskeleton with the plasma membrane, particularly in cell surface projections. Ezrin and moesin were found co-expressed, and radixin was not detected, in human blood lymphocytes, monocytes and neutrophils. Moesin is the quantitatively dominant ERM protein in these cells and the only one in platelets. Because Ca2+ signaling pathways involving calpain cleavages are important in blood cells, we examined ERM protein sensitivity to this protease. A striking difference was discovered: sensitivity of ezrin and resistance of moesin (and radixin) to calpain. In intact stimulated lymphocytes, ezrin was cleaved, while moesin was not, strongly suggesting that differential sensitivity to calpain contributes to specialized functions of these proteins

Fluctuations of linear statistics of half-heavy-tailed random matrices

publisher: Elsevier articletitle: Fluctuations of linear statistics of half-heavy-tailed random matrices journaltitle: Stochastic Processes and their Applications articlelink: http://dx.doi.org/10.1016/j.spa.2016.04.030 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved.publisher: Elsevier articletitle: Fluctuations of linear statistics of half-heavy-tailed random matrices journaltitle: Stochastic Processes and their Applications articlelink: http://dx.doi.org/10.1016/j.spa.2016.04.030 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved.publisher: Elsevier articletitle: Fluctuations of linear statistics of half-heavy-tailed random matrices journaltitle: Stochastic Processes and their Applications articlelink: http://dx.doi.org/10.1016/j.spa.2016.04.030 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved

Interim analysis:Open-label extension study of leniolisib for patients with APDS

Background: Activated phosphoinositide 3-kinase delta (PI3Kδ) syndrome (APDS; or p110δ-activating mutations causing senescent T cells, lymphadenopathy, and immunodeficiency) is an inborn error of immunity caused by PI3Kδ hyperactivity. Resultant immune deficiency and dysregulation lead to recurrent sinopulmonary infections, herpes viremia, autoimmunity, and lymphoproliferation. Objective: Leniolisib, a selective PI3Kδ inhibitor, demonstrated favorable impact on immune cell subsets and lymphoproliferation over placebo in patients with APDS over 12 weeks. Here, we report results from an interim analysis of an ongoing open-label, single-arm extension study. Methods: Patients with APDS aged 12 years or older who completed NCT02435173 or had previous exposure to PI3Kδ inhibitors were eligible. The primary end point was safety, assessed via investigator-reported adverse events (AEs) and clinical/laboratory evaluations. Secondary and exploratory end points included health-related quality of life, inflammatory markers, frequency of infections, and lymphoproliferation. Results: Between September 2016 and August 2021, 37 patients (median age, 20 years; 42.3% female) were enrolled. Of these 37 patients, 26, 9, and 2 patients had previously received leniolisib, placebo, or other PI3Kδ inhibitors, respectively. At the data cutoff date (December 13, 2021), median leniolisib exposure was 102 weeks. Overall, 32 patients (87%) experienced an AE. Most AEs were grades 1 to 3; none were grade 4. One patient with severe baseline comorbidities experienced a grade 5 AE, determined as unrelated to leniolisib treatment. While on leniolisib, patients had reduced annualized infection rates (P =.004), and reductions in immunoglobulin replacement therapy occurred in 10 of 27 patients. Other observations include reduced lymphadenopathy and splenomegaly, improved cytopenias, and normalized lymphocyte subsets. Conclusions: Leniolisib was well tolerated and maintained durable outcomes with up to 5 years of exposure in 37 patients with APDS. ClinicalTrials.gov identifier: NCT02859727.</p

Rubella Virus-Associated Cutaneous Granulomatous Disease : a Unique Complication in Immune-Deficient Patients, Not Limited to DNA Repair Disorders

The association of immunodeficiency-related vaccine-derived rubella virus (iVDRV) with cutaneous and visceral granulomatous disease has been reported in patients with primary immunodeficiency disorders (PIDs). The majority of these PID patients with rubella-positive granulomas had DNA repair disorders. To support this line of inquiry, we provide additional descriptive data on seven previously reported patients with Nijmegen breakage syndrome (NBS) (n=3) and ataxia telangiectasia (AT) (n=4) as well as eight previously unreported patients with iVDRV-induced cutaneous granulomas and DNA repair disorders including NBS (n=1), AT (n=5), DNA ligase 4 deficiency (n=1), and Artemis deficiency (n=1). We also provide descriptive data on several previously unreported PID patients with iVDRV-induced cutaneous granulomas including cartilage hair hypoplasia (n=1), warts, hypogammaglobulinemia, immunodeficiency, myelokathexis (WHIM) syndrome (n=1), MHC class II deficiency (n=1), Coronin-1A deficiency (n=1), X-linked severe combined immunodeficiency (X-SCID) (n=1), and combined immunodeficiency without a molecular diagnosis (n=1). At the time of this report, the median age of the patients with skin granulomas and DNA repair disorders was 9years (range 3-18). Cutaneous granulomas have been documented in all, while visceral granulomas were observed in six cases (40%). All patients had received rubella virus vaccine. The median duration of time elapsed from vaccination to the development of cutaneous granulomas was 48months (range 2-152). Hematopoietic cell transplantation was reported to result in scarring resolution of cutaneous granulomas in two patients with NBS, one patient with AT, one patient with Artemis deficiency, one patient with DNA Ligase 4 deficiency, one patient with MHC class II deficiency, and one patient with combined immunodeficiency without a known molecular etiology. Of the previously reported and unreported cases, the majority share the diagnosis of a DNA repair disorder. Analysis of additional patients with this complication may clarify determinants of rubella pathogenesis, identify specific immune defects resulting in chronic infection, and may lead to defect-specific therapies.Peer reviewe

Preexisting autoantibodies to type I IFNs underlie critical COVID-19 pneumonia in patients with APS-1

Patients with biallelic loss-of-function variants of AIRE suffer from autoimmune polyendocrine syndrome type-1 (APS-1) and produce a broad range of autoantibodies (auto-Abs), including circulating auto-Abs neutralizing most type I interferons (IFNs). These auto-Abs were recently reported to account for at least 10% of cases of life-threatening COVID-19 pneumonia in the general population. We report 22 APS-1 patients from 21 kindreds in seven countries, aged between 8 and 48 yr and infected with SARS-CoV-2 since February 2020. The 21 patients tested had auto-Abs neutralizing IFN-α subtypes and/or IFN-ω; one had anti–IFN-β and another anti–IFN-ε, but none had anti–IFN-κ. Strikingly, 19 patients (86%) were hospitalized for COVID-19 pneumonia, including 15 (68%) admitted to an intensive care unit, 11 (50%) who required mechanical ventilation, and four (18%) who died. Ambulatory disease in three patients (14%) was possibly accounted for by prior or early specific interventions. Preexisting auto-Abs neutralizing type I IFNs in APS-1 patients confer a very high risk of life-threatening COVID-19 pneumonia at any age.publishedVersio