A Prediction Model to Prioritize Individuals for a SARS-CoV-2 Test Built from National Symptom Surveys

Background: The gold standard for COVID-19 diagnosis is detection of viral RNA through PCR. Due to global limitations in testing capacity, effective prioritization of individuals for testing is essential. Methods: We devised a model estimating the probability of an individual to test positive for COVID-19 based on answers to 9 simple questions that have been associated with SARS-CoV-2 infection. Our model was devised from a subsample of a national symptom survey that was answered over 2 million times in Israel in its first 2 months and a targeted survey distributed to all residents of several cities in Israel. Overall, 43,752 adults were included, from which 498 self-reported as being COVID-19 positive. Findings: Our model was validated on a held-out set of individuals from Israel where it achieved an auROC of 0.737 (CI: 0.712–0.759) and auPR of 0.144 (CI: 0.119–0.177) and demonstrated its applicability outside of Israel in an independently collected symptom survey dataset from the US, UK, and Sweden. Our analyses revealed interactions between several symptoms and age, suggesting variation in the clinical manifestation of the disease in different age groups. Conclusions: Our tool can be used online and without exposure to suspected patients, thus suggesting worldwide utility in combating COVID-19 by better directing the limited testing resources through prioritization of individuals for testing, thereby increasing the rate at which positive individuals can be identified. Moreover, individuals at high risk for a positive test result can be isolated prior to testing. Funding: E.S. is supported by the Crown Human Genome Center, Larson Charitable Foundation New Scientist Fund, Else Kroener Fresenius Foundation, White Rose International Foundation, Ben B. and Joyce E. Eisenberg Foundation, Nissenbaum Family, Marcos Pinheiro de Andrade and Vanessa Buchheim, Lady Michelle Michels, and Aliza Moussaieff and grants funded by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation. H.R. is supported by the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center and by a research grant from Madame Olga Klein – Astrachan

Modeling double strand break susceptibility to interrogate structural variation in cancer

Abstract Background Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge from errors in the repair processes following DNA double strand breaks (DSBs). Results We used experimentally quantified DSB frequencies in cell lines with matched chromatin and sequence features to derive the first quantitative genome-wide models of DSB susceptibility. These models are accurate and provide novel insights into the mutational mechanisms generating DSBs. Models trained in one cell type can be successfully applied to others, but a substantial proportion of DSBs appear to reflect cell type-specific processes. Using model predictions as a proxy for susceptibility to DSBs in tumors, many SV-enriched regions appear to be poorly explained by selectively neutral mutational bias alone. A substantial number of these regions show unexpectedly high SV breakpoint frequencies given their predicted susceptibility to mutation and are therefore credible targets of positive selection in tumors. These putatively positively selected SV hotspots are enriched for genes previously shown to be oncogenic. In contrast, several hundred regions across the genome show unexpectedly low levels of SVs, given their relatively high susceptibility to mutation. These novel coldspot regions appear to be subject to purifying selection in tumors and are enriched for active promoters and enhancers. Conclusions We conclude that models of DSB susceptibility offer a rigorous approach to the inference of SVs putatively subject to selection in tumors

Proteomics Mapping of Cord Blood Identifies Haptoglobin “Switch-On” Pattern as Biomarker of Early-Onset Neonatal Sepsis in Preterm Newborns

Intra-amniotic infection and/or inflammation (IAI) are important causes of preterm birth and early-onset neonatal sepsis (EONS). A prompt and accurate diagnosis of EONS is critical for improved neonatal outcomes. We sought to explore the cord blood proteome and identify biomarkers and functional protein networks characterizing EONS in preterm newborns.We studied a prospective cohort of 180 premature newborns delivered May 2004-September 2009. A proteomics discovery phase employing two-dimensional differential gel electrophoresis (2D-DIGE) and mass spectrometry identified 19 differentially-expressed proteins in cord blood of newborns with culture-confirmed EONS (n = 3) versus GA-matched controls (n = 3). Ontological classifications of the proteins included transfer/carrier, immunity/defense, protease/extracellular matrix. The 1(st)-level external validation conducted in the remaining 174 samples confirmed elevated haptoglobin and haptoglobin-related protein immunoreactivity (Hp&HpRP) in newborns with EONS (presumed and culture-confirmed) independent of GA at birth and birthweight (P<0.001). Western blot concurred in determining that EONS babies had conspicuous Hp&HpRP bands in cord blood ("switch-on pattern") as opposed to non-EONS newborns who had near-absent "switch-off pattern" (P<0.001). Fetal Hp phenotype independently impacted Hp&HpRP. A bayesian latent-class analysis (LCA) was further used for unbiased classification of all 180 cases based on probability of "antenatal IAI exposure" as latent variable. This was then subjected to 2(nd)-level validation against indicators of adverse short-term neonatal outcome. The optimal LCA algorithm combined Hp&HpRP switch pattern (most input), interleukin-6 and neonatal hematological indices yielding two non-overlapping newborn clusters with low (≤20%) versus high (≥70%) probability of IAI exposure. This approach reclassified ∼30% of clinical EONS diagnoses lowering the number needed to harm and increasing the odds ratios for several adverse outcomes including intra-ventricular hemorrhage.Antenatal exposure to IAI results in precocious switch-on of Hp&HpRP expression. As EONS biomarker, cord blood Hp&HpRP has potential to improve the selection of newborns for prompt and targeted treatment at birth

α-Synuclein Expression Selectively Affects Tumorigenesis in Mice Modeling Parkinson's Disease

Alpha Synuclein (α-Syn) is a protein implicated in mechanisms of neuronal degeneration in Parkinson's disease (PD). α-Syn is primarily a neuronal protein, however, its expression is found in various tumors including ovarian, colorectal and melanoma tumors. It has been hypothesized that neurodegeneration may share common mechanisms with oncogenesis. We tested whether α-Syn expression affects tumorigenesis of three types of tumors. Specifically, B16 melanoma, E0771 mammary gland adenocarcinoma and D122 Lewis lung carcinoma. For this aim, we utilized transgenic mice expression the human A53T α-Syn form. We found that the in vivo growth of B16 and E0771 but not D122 was enhanced in the A53T α-Syn mice. The effect on tumorigenesis was not detected in age-matched APP/PS1 mice, modeling Alzheimer's disease (AD), suggesting a specific effect for α-Syn- dependent neurodegeneration. Importantly, transgenic α-Syn expression was detected within the three tumor types. We further show uptake of exogenously added, purified α-Syn, by the cultured tumor cells. In accord, with the affected tumorigenesis in the young A53T α-Syn mice, over- expression of α-Syn in cultured B16 and E0771 cells enhanced proliferation, however, had no effect on the proliferation of D122 cells. Based on these results, we suggest that certain forms of α-Syn may selectively accelerate cellular mechanisms leading to cancer

Communication-optimal parallel 2.5D matrix multiplication and LU factorization algorithms

One can use extra memory to parallelize matrix multiplication by storing p 1/3 redundant copies of the input matrices on p processors in order to do asymptotically less communication than Cannon’s algorithm [2], and be faster in practice [1]. We call this algorithm “3D ” because it arranges the p processors in a 3D array, and Cannon’s algorithm “2D ” because it stores a single copy of the matrices on a 2D array of processors. We generalize these 2D and 3D algorithms by introducing a new class of “2.5D algorithms”. For matrix multiplication, we can take advantage of any amount of extra memory to store c copies of the data, for any c ∈{1, 2,..., ⌊p 1/3 ⌋}, to reduce the bandwidth cost of Cannon’s algorithm by a factor of c 1/2 and the latency cost by a factor c 3/2. We also show that these costs reach the lower bounds [13, 3], modulo polylog(p) factors. We similarly generalize LU decomposition to 2.5D and 3D, including communication-avoiding pivoting, a stable alternative to partial-pivoting [7]. We prove a novel lower bound on the latency cost of 2.5D and 3D LU factorization, showing that while c copies of the data can also reduce the bandwidth by a factor of c 1/2, the latency must increase by a factor of c 1/2, so that the 2D LU algorithm (c = 1) in fact minimizes latency. Preliminary results of 2.5D matrix multiplication on a Cray XT4 machine also demonstrate a performance gain of up to 3X with respect to Cannon’s algorithm. Careful choice of c also yields up to a 2.4X speed-up over 3D matrix multiplication, due to a better balance between communication costs