Orthogonal Proteomics Methods to Unravel the HOTAIR Interactome

Accumulating evidence highlights the role of long non-coding RNAs (lncRNA) in cellular homeostasis, and their dysregulation in disease settings. Most lncRNAs function by interacting with proteins or protein complexes. While several orthogonal methods have been developed to identify these proteins, each method has its inherent strengths and limitations. Here, we combine two RNA-centric methods ChIRP-MS and RNA-BioID to obtain a comprehensive list of proteins that interact with the well-known lncRNA HOTAIR. Overexpression of HOTAIR has been associated with a metastasis-promoting phenotype in various cancers. Although HOTAIR is known to bind with PRC2 and LSD1 protein complexes, only very limited unbiased comprehensive approaches to map its interactome have been performed. Both ChIRP-MS and RNA-BioID data sets show an association of HOTAIR with mitoribosomes, suggesting that HOTAIR has functions independent of its (post-)transcriptional mode-of-action.

PATH-46. DIAGNOSTIC IMPACT OF THE CNS TUMOR METHYLATION PROFILING IN A NEUROPATHOLOGY CONSULT PRACTICE

DNA methylation profiling coupled with the application of CNS tumor methylation classifier has contributed to precise and accurate diagnostics for a range of tumor types involving the central nervous system. The impact and characteristics of methylation profiling on tumor diagnosis has not been fully assessed in the setting of neuropathology consultation practice. A consecutive series of 1,258 surgical neuropathology samples obtained primarily in a consultation practice were profiled over 2-year period and analyzed using the DKFZ/Heidelberg CNS tumor methylation classifier. Among the 1,045 cases received from outside institutions for consultation, the classifier was able to refine a histologically diagnosed entity (e.g. medulloblastoma) in 13.3% (n = 139) cases. A substantially new diagnosis was able to be rendered in an additional 17.9% (n = 187) cases, many of which could be confirmed using orthogonal methods. A “suggestive” (0.30-0.84) classifier score was found in 23% (242) cases and we found that complementary methods (UMAP, t-SNE and nearest-neighbors) were able to resolve this uncertainty in 118 cases. We found tumor purity significantly associated with varied classifier score (p = 1.53e-11). Computational tumor purity adjustment by deconvolution on a subset of gliomas provided a proof-of-concept to resolve diagnostics in the setting of low tumor purity. Overall, this work directly assesses the benefit of methylation classification in a set of diagnostically challenging CNS tumors, addresses tumor purity diminished methylation signal and provides complementary approaches to address diagnostics in cases of low-confidence classifier scores.

isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data

Background Accurate copy number variant (CNV) detection is especially challenging for both targeted sequencing (TS) and whole‐exome sequencing (WES) data. To maximize the performance, the parameters of the CNV calling algorithms should be optimized for each specific dataset. This requires obtaining validated CNV information using either multiplex ligation-dependent probe amplification (MLPA) or array comparative genomic hybridization (aCGH). They are gold standard but time-consuming and costly approaches. Results We present isoCNV which optimizes the parameters of DECoN algorithm using only NGS data. The parameter optimization process is performed using an in silico CNV validated dataset obtained from the overlapping calls of three algorithms: CNVkit, panelcn.MOPS and DECoN. We evaluated the performance of our tool and showed that increases the sensitivity in both TS and WES real datasets. Conclusions isoCNV provides an easy-to-use pipeline to optimize DECoN that allows the detection of analysis-ready CNV from a set of DNA alignments obtained under the same conditions. It increases the sensitivity of DECoN without the need for orthogonal methods. isoCNV is available at https://gitlab.com/sequentiateampublic/isocnv.

Analysis of Phthalates and Alternative Plasticizers in Gloves by Gas Chromatography–Mass Spectrometry and Liquid Chromatography–UV Detection: A Comparative Study

Gloves represent an essential feature for hand protection because it is a requirement in the professional framework to comply with both hand hygiene standards and the principles of good laboratory practice. Despite their wide use, there is a knowledge gap regarding their composition, including phthalates. The purpose of the present study was to develop two orthogonal methods, GC–MS and HPLC–DAD, for the screening of plasticizers in gloves. Performances of these two methods were compared in terms of ease of use, number of analyzed plasticizers, and sample preparation. The two methods were validated and applied for the identification and quantification of plasticizers in ten gloves made with different materials (vinyl, nitrile, latex, and neoprene). Results revealed the presence of three main ones: DEHP, DEHT, and DINP. Additionally, the contents of plasticizers were extremely variable, depending on the glove material. As expected, the results point out a predominant use of plasticizers in vinyl gloves with an amount that should be of concern. While DEHP is classified as a toxic substance for reproduction 1B, it was, however, quantified in the ten different glove samples studied. This study provides new data regarding the plasticizers’ content in protective gloves, which could be useful for risk assessment.

Automatic Mixed-Precision Quantization Search of BERT

Pre-trained language models such as BERT have shown remarkable effectiveness in various natural language processing tasks. However, these models usually contain millions of parameters, which prevent them from the practical deployment on resource-constrained devices. Knowledge distillation, Weight pruning, and Quantization are known to be the main directions in model compression. However, compact models obtained through knowledge distillation may suffer from significant accuracy drop even for a relatively small compression ratio. On the other hand, there are only a few attempts based on quantization designed for natural language processing tasks, and they usually require manual setting on hyper-parameters. In this paper, we proposed an automatic mixed-precision quantization framework designed for BERT that can conduct quantization and pruning simultaneously. Specifically, our proposed method leverages Differentiable Neural Architecture Search to assign scale and precision for parameters in each sub-group automatically, and at the same pruning out redundant groups of parameters. Extensive evaluations on BERT downstream tasks reveal that our proposed method beats baselines by providing the same performance with much smaller model size. We also show the possibility of obtaining the extremely light-weight model by combining our solution with orthogonal methods such as DistilBERT.

A ß-Secretase Modulator Decreases Tau Pathology and Preserves Short-Term Memory in a Mouse Model of Neurofibrillary Degeneration

Identifying which among several in cellulo pharmacological activities is necessary for the proper in vivo activity is essential for further drug development against Alzheimer’s disease pathophysiological processes. An in-depth structure–activity relationship–based study has been carried out, and two molecules, named MAGS02-14 and PEL24-199, that share a ß-secretase modulatory effect associated or not to a lysosomotropic activity in cellulo have been identified. In terms of chemical formulas, MAGS02-14 and PEL24-199 only differ from each other by a single nitrogen atom. The study aimed to elucidate the in vivo pharmacological effects of lysosomotropic and/or the ß-secretase modulatory activity in a tau pathology mouse model. To address this question, the THY-Tau22 transgenic model of tauopathy was treated with both compounds for 6 weeks in a curative paradigm. Short-term memory, tau burden, and inflammatory processes were analyzed using orthogonal methods, and PEL24-199, but not MAGS02-14, was shown to restore the short-term memory and reduce the neurofibrillary degenerating process. These effects were associated with a reduced phosphorylation of tau, an increased phosphatase expression, and decreased astrogliosis. Our results, therefore, suggest that the lysosomotropic activity may be nonessential for the effect on tau pathology.