Signature Of Fragmentation Research Articles

Evaluation of cost-effective multiple cancer early detection with extremely low coverage whole genome sequencing from plasma.

10538 Background: Cancer continues to be a significant global health concern, with approximately 19.3 million new cases diagnosed and 10.0 million cancer-related deaths reported in 2020. Multi-cancer early detection (MCED) test using the peripheral blood offers a great opportunity to improve the current cancer screening tests with better performance and benefit-to-harm balance. Methods: Here we introduce a novel MCED test called HIFI-PROF, which utilizes extremely low pass whole-genome sequencing (median coverage of 0.6X) to create a multi-dimensional fragmentation signatures model through machine learning approach. HIFI-PROF was developed to detect and localize six types of cancer (colorectal, pancreas, liver, lung, esophagus and gastric cancer). Results: The study included a total of 1,487 participants, comprising 235 colorectal cancers, 227 pancreatic cancers, 150 liver cancers, 125 lung cancers, 107 esophageal cancers, 106 gastric cancers and 537 healthy volunteers. Most of the cancer cases were I/II stage patients (stage I: 38%, stage II: 35%, stage III: 23%, stage IV: 4%). The participants, stratified by age and clinical status, were divided into two sets: a training set (n=1,079) and a validation set (n=408).HIFI-PROF exhibited an excellent performance in the early detection of multiple cancer types, achieving a sensitivity of 87.58% at a specificity of 99.09% and identifying the tissue of origin (TOO) with an accuracy of 81.99%. The sensitivity rates for detecting cancer at different stages were also promising, with a sensitivity of 84.62% for stage I, 85.59% for stage II, 89.61% for stage III and 92.86% for stage IV. Conclusions: These results highlight the high sensitivity and specificity of HIFI-PROF in identifying cancer cases while maintaining a low rate of false-positive results. The use of HIFI-PROF offers a cost-effective MCED test with excellent performance. Such advancements will contribute to improve strategies for multi-cancer early detection, leading to more effective clinical interventions and better outcomes.

Abstract 7019: Identifying cell free DNA methylation signatures in cerebrospinal fluids for the early detection of brain metastasis in non-small cell lung cancer

Abstract Brain metastasis (BM) is major complication for adult cancer patients. Up to 2% of all cancer patients develop BM. Non-small cell lung cancer (NSCLC) contributes up to 55% of all BM. Approximately 15-20% of NSCLC patients show metastases at diagnosis, and approximately half will develop BM during their clinical course. Even after treatments like surgery, radiation and immunotherapy, the prognosis is dismal with median overall survival less than 13 months. No standard screening or early diagnostic testing exists for BM now. There is an urgent need to identify genomic features that predispose towards brain metastasis - these biomarkers could be used for early detection and disease monitoring. The cfDNA methylation and fragmentation patterns provide potential epigenetic biomarkers for detecting cancer. These patterns are specific to cell/tissue origin and cancer types. DNA methylation refers to a chemical modification of a specific carbon with an added methyl group in the cytosine. Hypomethylation in the gene body and hypermethylation in the promoter region lead to cancer development. We hypothesize that there are specific cfDNA methylation and fragmentation signatures in CSF that reflect the epigenetic states of BMs, and cancer predisposition for brain metastasis. We applied a novel single molecule sequencing approach to study CSF cfDNA methylation of BM from NSCLC. Our cohort consisted of ten patients with NSCLC-BM (samples N=14) and eleven healthy controls (N=11). Briefly, extracted cfDNA was obtained from CSF samples. Multiplexed sequencing libraries were prepared following an optimized protocol, and sequenced on PromethION flow cell for 72hr by Oxford nanopore technologies. Data were processed with the onboard base and methylation calling, followed by our bioinformatic pipelines for cfDNA size and methylation analysis. Distinct methylation patterns were found on significant CpG sites (fdr-corrected p<0.01) between cancer patients and controls, in both gene-body and promoter region. Biological pathway analysis showed enrichment in essential functions involving PI3K, ERBB2, TNF-α signaling (p<0.03), all of which play a role in NSCLC. Enriched mono-nucleosome levels (p = 1.07e-03) and differentiated mono-/tri- nucleosome ratios (p = 1.34e-03) were found in cancer vs. controls. Overall, our results suggest that a combination of cfDNA methylation and fragmentation can detect NSCLC-BM. This study will benefit BM patients with early detection and better prognosis, and extend our understanding in brain metastases in NSCLC. Citation Format: Tianqi (Kiki) Chen, Billy T. Lau, Xiangqi Bai, Melanie Hayden Gephart, Hanlee P. Ji. Identifying cell free DNA methylation signatures in cerebrospinal fluids for the early detection of brain metastasis in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7019.

Observing the LMC with APEX: Signatures of large-scale feedback in the molecular clouds of 30 Doradus

Context. Stellar feedback plays a crucial role in star formation and the life cycle of molecular clouds. The intense star formation region 30 Doradus, which is located in the Large Magellanic Cloud (LMC), is a unique target for detailed investigation of stellar feedback owing to the proximity of the hosting galaxy and modern observational capabilities that together allow us to resolve individual molecular clouds – nurseries of star formation. Aims. We study the impact of large-scale feedback on the molecular gas using the new observational data in the 12CO(3 − 2) line obtained with the APEX telescope. Methods. Our data cover an unprecedented area of 13.8 sq. deg. of the LMC disc with a spatial resolution of 5 pc and provide an unbiased view of the molecular clouds in the galaxy. Using these data, we located molecular clouds in the disc of the galaxy, estimated their properties, such as the areal number density, relative velocity and separation, width of the line profile, CO line luminosity, size, and virial mass, and compared these properties of the clouds of 30 Doradus with those in the rest of the LMC disc. Results. We find that, compared with the rest of the observed molecular clouds in the LMC disc, those in 30 Doradus show the highest areal number density; they are spatially more clustered, they move faster with respect to each other, and they feature larger linewidths. In parallel, we do not find statistically significant differences in such properties as the CO line luminosity, size, and virial mass between the clouds of 30 Doradus and the rest of the observed field. Conclusions. We interpret our results as signatures of gas dispersal and fragmentation due to high-energy large-scale feedback.

Multicenter clinical validation of GutSeer: A cost-effective, blood-based assay for early detection and localization of major gastrointestinal cancers.

739 Background: Gastrointestinal (GI) cancers account for over one-third of cancer-related deaths. We have previously developed GutSeer, a non-invasive assay utilizing cell-free DNA (cfDNA) methylation and fragmentation signatures, to cost-effectively detect and localize five major GI cancers, including colorectal (CC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC). Methods: GutSeer was initially developed and validated in a large retrospective cohort of 1844 plasma samples, demonstrating excellent performance with specificity, sensitivity, and tissue of origin (TOO) accuracy at 96.7%, 86.2%, and 82%, respectively. In this study, we conducted a blind, multi-center clinical test for further validation. Participants were recruited from two centers, Beijing (BJ) and Shanghai (SH). Cohort BJ included 42 participants (20 healthy, 22 cancer), and Cohort SH comprised 403 participants (105 healthy, 123 benign, 175 cancer). Over half of the cancer samples in Cohort SH were in early-stage disease (TNM stages I and II). Results: In Cohort BJ, GutSeer showed 100% specificity, 68.2% sensitivity, and 86.7% TOO accuracy. Individual cancer type sensitivities within Cohort BJ were 100% for CC, 57.1% for EC, 75% for GC, 40% for PC, and 100% for LC. In Cohort SH, GutSeer exhibited 94.6% specificity for healthy samples and 86.2% for benign conditions. For cancer samples in Cohort SH, the overall sensitivity achieved 78.9%, with specific sensitivities of 92.3% for CC, 81.0% for EC, 78.4% for GC, 52.9% for PC, and 86.4% for LC. Moreover, GutSeer also efficiently detected early-stage cancers, with stage-wise sensitivities ranging from 64.7% (stage I) to 96.3% (stage IV). TOO accuracy in Cohort SH was 79.0%, with individual cancer types achieving accuracies from 71.7% to 94.7% (72.2% for CC, 71.7% for EC/GC, 94.7% for LC, and 77.8% for PC). Conclusions: GutSeer demonstrates promising potential as a cost-effective, non-invasive test for early detection and localization of major GI cancers. Its consistently high performance in both retrospective and multi-center blind clinical tests implies its potential to revolutionize the screening and diagnosis paradigm for GI cancers.

Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures.

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.

Analysis of Phosphatidylinositol Modifications by Reactive Nitrogen Species Using LC-MS: Coming to Grips with Their Nitroxidative Susceptibility.

Phosphatidylinositols (PIs) are complex lipids that play a key role in cell signaling. Like other phospholipids, they are esterified with unsaturated fatty acyl residues (FAs), making them susceptible to modification by reactive oxygen and nitrogen species (RNS). Recent studies using mass spectrometry (MS)-based lipidomics approaches have revealed that lipid nitration results in a plethora of structurally and chemically modified lipids (epilipids), including nitrated and nitroxidized derivatives of phosphatidylcholines, phosphatidylethanolamines, phosphatidylserines, and cardiolipins. However, there is a notable lack of knowledge regarding the characterization of RNS-modified PI derivatives. In this study, we used C18 high-resolution liquid chromatography-tandem MS approaches to describe the fragmentation signature of nitrated and nitroxidized PIs, bearing different fatty acyl chains. Using this approach and accurate mass measurements, we were able to identify nitro- PI derivatives, dinitro- and nitrohydroxy- derivatives for a few PI species. The data showed the typical neutral loss of nitrous acid (HNO2) as well as the fragmentation patterns corresponding to modified fatty acyl chains (such as NOx-RCOO-, [M - NOx-RCOOH - H]- and [M - NOx-RCOOH - C6H10O5 - H]-), making it possible to identify these epilipids. The susceptibility of PIs to nitration was also investigated, revealing that it depends exclusively on the chains of unsaturated FAs esterified in PI, showing a higher conversion rate for those with C18:1. Overall, the knowledge gathered in this study will contribute to the precise characterization of these epilipids in complex biological samples, offering new opportunities to unveil the pathophysiological roles of nitrated and nitroxidized PI derivatives at the cellular and tissue levels.

Pan-cancer detection and tissue-of-origin identification by whole-genome sequencing of plasma cell-free DNA.

3055 Background: Despite notable advances in cancer therapeutics, much of the mortality of human cancers results from late diagnosis. The early detection of cancer remains an unmet need that can benefit the effective treatments and better prognosis. Cell-free DNA (cfDNA) in the circulation provides an emerging diagnostic method for non-invasive cancer screening. The fragmentation signatures of cfDNA originating from different cell types or malignancies display distinct patterns, suggesting the potential as cancer biomarkers. This study aims to investigate the fragmentation profiles of cfDNA in several cancer types, and to develop a new approach for pan-cancer detection and tissue-of-origin identification. Methods: Plasma samples from 739 patients with lung (N=577), colorectal (N=98), breast (N=51) and liver (N=13) cancers, as well as 716 healthy individuals were collected in this study. Cell-free DNA was isolated and used to generate the whole-genome sequencing data. We developed an approach called PatternWGS (Pan-cancer detection and tissue-of-origin identification by whole-genome sequencing) to analyze the DNA fragmentation patterns comprehensively. Fragmentation features integrating cfDNA fragment size distribution, frequency of end motif sequence, and copy number variation were utilized to construct the optimal prediction model. We presented a multi-cancer detection model for distinguishing individuals with and without cancer using Generalized Linear Models (glmnet), and further built a cancer-origin model to predict cancer types using Random Forest algorithm. Training and testing occurred on randomly selected sampling splits of 60% and 40% of the data, respectively. Features selection and model construction were performed by only training cohort, and the test set was solely used for performance evaluation. Results: A larger proportion of shorter cfDNA fragments in cancers was observed. We proposed a fragment score indicating the degree of short fragmentation, and this fragment score was significantly higher in cancers (p<0.001). Several end motifs were identified to be specifically enriched in cancer patients. The multi-cancer detection model reached an area under the curve (AUC) of 0.98, with the sensitivity of 93% at 95% specificity. The sensitivity of detecting lung, colorectal, breast, and liver cancers reached 94.2%, 87%, 100% and 100% respectively, at 95% specificity. The cancer-origin model demonstrated an overall accuracy of 81.7% for the identification of tissue of origin. Conclusions: Our prediction model demonstrated a superior performance for multi-cancer detection and cancer origin prediction, suggesting the potential of plasma cfDNA fragmentation signatures as non-invasive biomarkers for early cancer detection in clinical. The distinct fragmentation patterns in cancer provide new insight to understand the mechanism of tumorigenesis.

Click chemistry-based enrichment strategy for tracing cellular fatty acid metabolism by LC-MS/MS

Fatty acids (FAs), which were initially recognized as energy sources and essential building blocks of biomembranes, serve as the precursors of important signaling molecules. Tracing FA metabolism is essential to understanding the biochemical activity and role of FAs in physiological and pathological events. Inspired by the advances in click chemistry for protein enrichment, we herein established a click chemistry-based enrichment (CCBE) strategy for tracing the cellular metabolism of eicosapentaenoic acid (EPA, 20:5 n-3) in neural cells. Terminal alkyne-labeled EPA (EPAA) used as a surrogate was incubated with N2a, mouse neuroblastoma cells, and alkyne-labeled metabolites (ALMs) were selectively captured by an azide-modified resin via a Cu(I)-catalyzed azide-alkyne cycloaddition reaction for enrichment. After removing unlabeled metabolites, ALMs containing a triazole moiety were cleaved from solid-phase resins and subjected to liquid chromatography mass spectrometry (LC-MS) analysis. The proposed CCBE strategy is highly selective for capturing and enriching alkyne-labeled metabolites from the complicated matrices. In addition, this method can overcome current detection limits by enhancing MS sensitivity of targets, improving the chromatographic separation of sn-position glycerophospholipid regioisomers, facilitating structural characterization of ALMs by a specific MS/MS fragmentation signature, and providing versatile fluorescence detection of ALMs for cellular distribution. This CCBE strategy might be expanded to trace the metabolism of other FAs, small molecules, or drugs.

Structural characterization and mass spectrometry fragmentation signatures of macrocyclic alkanes isolated from a Sydney Basin torbanite, Australia

Structural characterization and mass spectrometry fragmentation signatures of macrocyclic alkanes isolated from a Sydney Basin torbanite, Australia

Early detection and localization of major gastrointestinal cancers by combining DNA methylation and fragmentomic signatures generated from a single cfDNA targeted sequencing assay.

330 Background: Gastrointestinal (GI) cancers totally account for more than one third of the cancerous deaths, yet there is no cost-effective blood-based assay for the early detection of GI cancers. We sought to develop GutSeer, a noninvasive test based on cell-free DNA (cfDNA) methylation and fragmentation signatures derived from one single targeted DNA methylation sequencing panel, for early detection and localization of five major GI cancers, including colorectal (CC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC). Methods: A DNA methylation targeted sequencing panel with 1656 target regions was designed. It was then verified in a large cohort of retrospective cancer and control plasma samples for feature selection and modeling. The participants were randomly divided into a training cohort and a validation cohort in a 1:1 ratio. DNA methylation and fragmentomic features were calculated based on GutSeer sequencing data. An ensemble stacked machine learning approach was built to classify cancer and healthy samples in training cohort and tested in validation cohort. We also constructed a TOO model to predict the tissue of origin of detected cancer samples. Results: To develop GutSeer assay, we have enrolled and tested a total of 1844 retrospective plasma samples (787 healthy, 342 LC, 239 GC, 209 EC, 180 CC, and 87 PC), over half of the cancer samples were diagnosed with early-stage disease (TNM stage I 35.6%; stage II 23.3%; stage III 21.7%; stage IV 12.5%). Cancer- vs-healthy model was built on training cohort and tested in validation cohort, achieving an AUC of 0.94 (sensitivity=77.7%, specificity=96.4%) with methylation features, and 0.95 (sensitivity=77.1%, specificity=95.9%) with fragmentomic features. Combining these features could achieve AUC of 0.963 (sensitivity = 86.2%, specificity = 96.7%). For individual cancer types, the sensitivity was 93.3% (CC), 81.1% (EC), 70.3% (GC), 96.5% (LC) and 86.4% (PC), respectively. For predicted cancer samples, we achieved an 82% top-one (66.7% CC, 87.0% GC/EC, 89.0% LC, 63.2% PC) and 95.2% top-two (86,9% CC, 98.2% GC/EC, 97.6% LC, 89.5% PC) TOO accuracy (ACC, accuracy of predicting the most likely, and the top 2 most likely tissue or organ types where the identified cancer was located, respectively) in validation cohort with TOO model combined all features. Conclusions: Based on a single targeted DNA methylation sequencing assay, GutSeer, which combined cfDNA methylation and fragmentomic signatures, could detect and localize the major five GI cancers with high accuracy but low cost. Although this is a pilot study with limited sample size, GutSeer demonstrated the potential to be further optimized into non-invasive diagnostics for blood-based early screening and diagnosis for GI cancers.

Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing

The Oxford Nanopore (ONT) platform provides portable and rapid genome sequencing, and its ability to natively profile DNA methylation without complex sample processing is attractive for point-of-care real-time sequencing. We recently demonstrated ONT shallow whole-genome sequencing to detect copy number alterations (CNAs) from the circulating tumor DNA (ctDNA) of cancer patients. Here, we show that cell type and cancer-specific methylation changes can also be detected, as well as cancer-associated fragmentation signatures. This feasibility study suggests that ONT shallow WGS could be a powerful tool for liquid biopsy.Graphical

Early detection of hepatocellular carcinoma with methylation and fragmentation signatures of circulating tumour DNA: a prospective, multicentre, case-control, observational study

Hepatocellular carcinoma is one of the deadliest cancers worldwide. Early detection has been shown to enable more effective treatments, thus decreasing morbidity and mortality. Non-invasive cancer detection via circulating tumour DNA (ctDNA) has emerged as a promising approach to monitor the molecular changes in liver tumour cells. We aimed to use ctDNA methylation and fragmentation signals to develop a blood-based assay for hepatocellular carcinoma early detection, named HcSeer. A targeted methylation sequencing panel was designed to integrate 1601 liver cancer-informative methylation markers, on the basis of in-house data and data from the public databases The Cancer Genome Atlas and Gene Expression Omnibus. Various methylation features were constructed from methylation sequencing data, including methylation haplotype load and methylated haplotype fraction. Low-pass whole-genome sequencing data from plasma samples were also analysed at a mean sequencing depth of 10×. Fragmentomic features, such as end motif, breakpoint motif, fragmentation size ratio, and copy number variation, were extracted from whole-genome sequencing data. A two-step deep neural network model was built to classify cancer and healthy samples with selected features of both types. The robustness of entire approach was verified with a 3× cross-validation by randomly splitting samples into training set and test set at a 2:1 ratio. In the discovery phase, a case-control study was designed to develop the HcSeer assay for the early detection of hepatocellular carcinoma. A total of 401 participants were recruited (200 healthy individuals and 201 patients with hepatocellular carcinoma). Most patients with cancer were at early stages standardised by Chinese liver cancer staging (109 [54%] at stage I and 25 [12%] at stage II). The classification model of HcSeer assay was built and cross-validated to achieve an average area under the curve of 0·99 (sensitivity 94% [189 of 201; 95% CI 90-97%] and specificity 96% [192 of 200; 92-98%]). The detection accuracy was observed to increase with cancer stages, with 91% (99 of 109; 95% CI 83-95%) sensitivity for stage I, 96% (24 of 25; 77-100%) for stage II, 98% (43 of 44; 86-100%) for stage III, and 100% (23 of 23, 84-100%) for stage IV. The validation cohort is ongoing, with the aim of reaching 510 plasma samples from multiple centres, including a full spectrum of liver diseases and age-matched healthy controls. The validation phase is expected to be completed in June, 2022. We have developed the HcSeer assay to combine the signatures of DNA methylation and genome-wide fragmentome. In a case-control study, we showed its feasibility to detect early-stage hepatocellular carcinoma with high accuracy. We propose it as a potential aid for non-invasive diagnostics of hepatocellular carcinoma. The performance of the assay is being validated in an independent sample set collected through a multicentre study, which was approved by the Ethical Committee of Zhongshan Hospital affiliated to Fudan University (number B2020-299R). This study was supported by the National Key Research and Development Program of China (grant number 2019YFC1315800).

Open Search of Peptide Glycation Products from Tandem Mass Spectra

Identification of chemically modified peptides in mass spectrometry (MS)-based glycation studies is a crucial yet challenging task. There is a need to establish a mode for matching tandem mass spectrometry (MS/MS) data, allowing for both known and unknown peptide glycation modifications. We present an open search approach that uses classic and modified peptide fragment ions. The latter are shifted by the mass delta of the modification. Both provide key structural information that can be used to assess the peptide core structure of the glycation product. We also leverage redundant neutral losses from the modification side chain, introducing a third ion class for matching referred to as characteristic fragment ions. We demonstrate that peptide glycation product MS/MS spectra contain multidimensional information and that most often, more than half of the spectral information is ignored if no attempt is made to use a multi-step matching algorithm. Compared to regular and/or modified peptide ion matching, our triple-ion strategy significantly increased the median interpretable fraction of the glycation product MS/MS spectra. For reference, we apply our approach for Amadori product characterization and identify all established diagnostic ions automatically. We further show how this method effectively applies the open search concept and allows for optimized elucidation of unknown structures by presenting two hitherto undescribed peptide glycation modifications with a delta mass of 102.0311 and 268.1768 Da. We characterize their fragmentation signature by integration with isotopically labeled glycation products, which provides high validity for non-targeted structure identification.

Distinct Stress-Dependent Signatures of Cellular and Extracellular tRNA-Derived Small RNAs.

The cellular response to stress is an important determinant of disease pathogenesis. Uncovering the molecular fingerprints of distinct stress responses may identify novel biomarkers and key signaling pathways for different diseases. Emerging evidence shows that transfer RNA‐derived small RNAs (tDRs) play pivotal roles in stress responses. However, RNA modifications present on tDRs are barriers to accurately quantifying tDRs using traditional small RNA sequencing. Here, AlkB‐facilitated methylation sequencing is used to generate a comprehensive landscape of cellular and extracellular tDR abundances in various cell types during different stress responses. Extracellular tDRs are found to have distinct fragmentation signatures from intracellular tDRs and these tDR signatures are better indicators of different stress responses than miRNAs. These distinct extracellular tDR fragmentation patterns and signatures are also observed in plasma from patients on cardiopulmonary bypass. It is additionally demonstrated that angiogenin and RNASE1 are themselves regulated by stressors and contribute to the stress‐modulated abundance of sub‐populations of cellular and extracellular tDRs. Finally, a sub‐population of extracellular tDRs is identified for which AGO2 appears to be required for their expression. Together, these findings provide a detailed profile of stress‐responsive tDRs and provide insight about tDR biogenesis and stability in response to cellular stressors.

Integrated 5-hydroxymethylcytosine and fragmentation signatures as enhanced biomarkers in lung cancer

BackgroundLung cancer is one of most common cancers worldwide, with a 5-year survival rate of less than 20%, which is mainly due to late-stage diagnosis. Noninvasive methods using 5-hydroxymethylation of cytosine (5hmC) modifications and fragmentation profiles from 5hmC cell-free DNA (cfDNA) sequencing provide an opportunity for lung cancer detection and management.ResultsA total of 157 lung cancer patients were recruited to generate the largest lung cancer cfDNA 5hmC dataset, which mainly consisted of 62 lung adenocarcinoma (LUAD), 48 lung squamous cell carcinoma (LUSC) and 25 small cell lung cancer (SCLC) patients, with most patients (131, 83.44%) at advanced tumor stages. A 37-feature 5hmC model was constructed and validated to distinguish lung cancer patients from healthy controls, with areas under the curve (AUCs) of 0.8938 and 0.8476 (sensitivity = 87.50% and 72.73%, specificity = 83.87% and 80.60%) in two distinct validation sets. Furthermore, fragment profiles of cfDNA 5hmC datasets were first explored to develop a 48-feature fragmentation model with good performance (AUC = 0.9257 and 0.822, sensitivity = 87.50% and 78.79%, specificity = 80.65% and 76.12%) in the two validation sets. Another diagnostic model integrating 5hmC signals and fragment profiles improved AUC to 0.9432 and 0.8639 (sensitivity = 87.50% and 83.33%, specificity = 90.30% and 77.61%) in the two validation sets, better than models based on either of them alone and performing well in different stages and lung cancer subtypes. Several 5hmC markers were found to be associated with overall survival (OS) and disease-free survival (DFS) based on gene expression data from The Cancer Genome Atlas (TCGA).ConclusionsBoth the 5hmC signal and fragmentation profiles in 5hmC cfDNA data are sensitive and effective in lung cancer detection and could be incorporated into the diagnostic model to achieve good performance, promoting research focused on clinical diagnostic models based on cfDNA 5hmC data.Graphical abstract

Novel Meteor Observations Using FM Radio Broadcast Over Gadanki, India

A novel GNU radio-based software-defined radio receiver using USRP (universal serial radio peripheral) front end and YAGI antenna is developed to record meteor echoes in passive mode using FM broadcast. Pilot observations are made for four days in August-September 2020 to record FM signals at 103.2MHz, 104MHz, and 107.5 MHz with a varying sample rate of 8 KHz and 200 KHz. The in-phase and quadrature signals are processed to obtain the amplitude and phase of the received signals for identification of the meteor echoes. A new algorithm is developed to automatically detect the echoes using a normalized amplitude threshold and a minimum temporal separation between two echoes. Overdense meteor echoes with a plateau-like amplitude response are found using a ratio test and remaining echoes are rejected. Thus, the system is able to detect almost all types of meteor echoes reported earlier. Notably, signatures of fragmentation and background wind are also observed in form of different phase patterns. Histogram of the meteor counts shows a peak for shorter durations indicating larger populations of the smaller size of meteoroids. The log-log plot of the meteor duration versus count is found to exhibit a characteristic transition of the slope. An empirical estimate of the height of the echoes is obtained. The echoes show micro-meteoroid populations which may depend upon the transmitting-receiving setup and the geometry. Hence, an inexpensive, low-power, portable, passive and automated meteor detection system is demonstrated which can be highly useful for understanding the meteor phenomena and estimation of atmospheric parameters.

Observation of charged droplets from electrospray ionization (ESI) plumes in API mass spectrometers

Electrospray ionization (ESI) generates bare analyte ions from charged droplets, which result from spraying a liquid in a strong electric field. Experimental observations available in the literature suggest that at least a significant fraction of the initially generated droplets remain large, have long lifetimes, and can thus aspirate into the inlet system of an atmospheric pressure ionization mass spectrometer (API-MS). We report on the observation of fragment signatures from charged droplets penetrating deeply the vacuum stages of three commercial mass spectrometer systems with largely different ion source and spray configurations. Charged droplets can pass through the ion source and pressure reduction stages and even into the mass analyzer region. Since droplet signatures were found in all investigated instruments, the incorporation of charged droplets is considered a general phenomenon occurring with common spray conditions in ESI sources.

ALMA Survey of Orion Planck Galactic Cold Clumps (ALMASOP): Detection of Extremely High-density Compact Structure of Prestellar Cores and Multiple Substructures Within

Abstract Prestellar cores are self-gravitating dense and cold structures within molecular clouds where future stars are born. They are expected, at the stage of transitioning to the protostellar phase, to harbor centrally concentrated dense (sub)structures that will seed the formation of a new star or the binary/multiple stellar systems. Characterizing this critical stage of evolution is key to our understanding of star formation. In this work, we report the detection of high-density (sub)structures on the thousand-astronomical-unit (au) scale in a sample of dense prestellar cores. Through our recent ALMA observations toward the Orion Planck Galactic Cold Clumps, we have found five extremely dense prestellar cores, which have centrally concentrated regions of ∼2000 au in size, and several 107 cm−3 in average density. Masses of these centrally dense regions are in the range of 0.30 to 6.89 M ⊙. For the first time, our higher resolution observations (0.8″ ∼ 320 au) further reveal that one of the cores shows clear signatures of fragmentation; such individual substructures/fragments have sizes of 800–1700 au, masses of 0.08 to 0.84 M ⊙, densities of 2 − 8 × 107 cm−3, and separations of ∼1200 au. The substructures are massive enough (≳0.1 M ⊙) to form young stellar objects and are likely examples of the earliest stage of stellar embryos that can lead to widely (∼1200 au) separated multiple systems.

The hierarchical fragmentation of filaments and the role of sub-filaments

ABSTRACT Recent observations have revealed the presence of small fibres or sub-filaments within larger filaments. We present a numerical fragmentation study of fibrous filaments investigating the link between cores and sub-filaments using hydrodynamical simulations performed with the moving-mesh code arepo. Our study suggests that cores form in two environments: (i) as isolated cores, or small chains of cores, on a single sub-filament, or (ii) as an ensemble of cores located at the junction of sub-filaments. We term these isolated and hub cores, respectively. We show that these core populations are statistically different from each other. Hub cores have a greater mean mass than isolated cores, and the mass distribution of hub cores is significantly wider than isolated cores. This fragmentation is reminiscent of parsec-scale hub-filament systems, showing that the combination of turbulence and gravity leads to similar fragmentation signatures on multiple scales, even within filaments. Moreover, the fact that fragmentation proceeds through sub-filaments suggests that there exists no characteristic fragmentation length-scale between cores. This is in opposition to earlier theoretical works studying fibre-less filaments which suggest a strong tendency towards the formation of quasi-periodically spaced cores, but in better agreement with observations. We also show tentative signs that global collapse of filaments preferentially form cores at both filament ends, which are more massive and dense than other cores.

Lasting signature of forest fragmentation.

Animal communities that endured historical environmental upheavals are less sensitive to modern ones