Read Counts Research Articles

Geometric Network Analysis Defines Poor-Prognosis Subtypes in Multiple Myeloma

The prognosis for patients with multiple myeloma (MM) is dependent on a variety of measures including treatment received, clinical factors, and genomic characteristics, with several specific genomic features predicting for shorter progression free survival (PFS). Studies have been conducted to identify biomarkers using gene expression profiles and copy number variation (CNV) data. However, no study to date has integrated genomic data from a systems view, incorporating interactions between biomarkers in a network. We apply an innovative geometric network analysis that integrates complex interactions to characterize patterns of biological behavior not captured by individual genomic events. The methodology is mathematically well-defined and has no fitting parameters. We hypothesized that such a systems mathematical approach applied to gene interaction networks may delineate biologically relevant MM subtypes and potential new therapeutic targets. We overlaid RNA-Seq and CNV data from the MMRF CoMMpass dataset (IA19) on a gene interactome derived from the Human Protein Reference Database (HPRD) using a novel graph metric of network robustness - Ollivier-Ricci curvature (ORC). ORC considers both local and global connectivity in assessing the robustness of each pathway characterized by feedback loops in a network. Hierarchical agglomerative clustering of the resulting ORC matrix computed for each omics-dataset was performed to identify subtypes based on the patterns of curvature, with the optimal number of clusters determined by silhouette score. Survival analysis for progression free survival (PFS) was performed employing Kaplan-Meier and log-rank tests. A differential gene expression analysis between high and low risk groups, as identified by the gene expression data, was then carried out using raw read counts with DESeq2. Further downstream bioinformatic analysis was conducted using MetaCore software. Both RNA-Seq and CNV data were available in 659 patients. Gene product interactions were determined by protein-protein interactions derived from HPRD, resulting in a network with 8,468 nodes and 33,695 edges. The ORC analysis using RNA-Seq data resulted in 6 clusters, and Kaplan-Meier analysis showed statistical significance in PFS with log-rank p=0.0016. Clusters predicting the best and worst PFS were associated with specific genomic features including CCND1 and MAF/MAFB translocations, respectively (each p<0.05, Fisher's exact test). Considering the central role of CCND1 in MM biology, we repeated our analysis with t(11;14) patients excluded, with the resulting clustering remaining significant for predicting PFS. The ORC analysis of CNV data on HPRD resulted in 8 clusters, and Kaplan-Meier analysis showed statistical significance in PFS with log-rank p=0.0082. Genomic features associated with the clusters included hyperdiploidy and CCND1 (longest PFS), and MAF-translocations and APOBEC-activity (shortest PFS) (each p<0.05, Fisher's). The worst PFS cluster had a higher prevalence of chromothripsis (p<0.0001, Fisher's) than other clusters, known to be an independent predictor for MM survival. Considering the impact of hyperdiploidy on CNV in MM, analyzing hyperploid and non-hyperdiploid patients separately was still significant for PFS prediction. After DESeq2 analysis to perform differential gene expression analysis between the high and low risk groups, the Benjamini-Hochberg method was applied to adjust the resulting p-values, producing false discovery rate (FDR). A list of 118 genes derived with FDR < 0.05 and |fold change| > 3.5 was input into MetaCore to identify biological correlates associated with the risk of PFS. Key implicated biological processes included cell cycle progression, inflammation and immune response. In this study, we applied the geometric network analysis tool ORC to multi-omics data in MM represented as biological networks to identify individuals at high risk of short PFS and relevant biological correlates. One hundred and eighteen key genes associated with MM prognosis were identified. Decreased robustness of signaling near immune-related genes was associated with shorter survival. For both RNAseq and CNV-based data, patient clusters were associated with known predictors of PFS, and remained predictive when removing specific genomic features, indicating that our methods are providing new biological insights. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

A Multi-Cohort Gene Expression Classifier (ALLCatchR) Identifies B-Precursor ALL Subtypes and Their Developmental Trajectories across Age Groups

The molecular landscape of B cell precursor acute lymphoblastic leukemia (BCP-ALL) describes >20 disease subtypes defined by initiating genomic alterations and corresponding gene expression signatures. Some subtypes represent phenocopies with shared gene expression signatures but distinct or absent genomic drivers. The majority of BCP-ALL subtypes have been acknowledged as diagnostic entities by the currently revised classifications of hematologic malignancies (WHO-HAEM5 / International Consensus Classification). Thus, unified diagnostic approaches are needed to systematically identify subtypes in individual BCP-ALL cases in clinical routine. Gene expression signatures represent the shared functional equivalent of heterogeneous genomic driver alterations. Therefore, we established a gene expression-based classifier to identify BCP-ALL subtypes and related clinical and biological phenotypes. We analyzed a total of n=3477 BCP-ALL gene expression profiles obtained from transcriptome sequencing of adult (n=1253) and pediatric patients (n=2224) including publicly available (n=2074; Gu Z et al. Nat. Genet. 2019; Schmidt B. et al. Blood Adv. 2022) and own data sets (n=1403) from adult and pediatric European study groups (GMALL, AIEOP-BFM, CELL). Raw read counts from 6 cohorts representing heterogeneous library preps (i.e., stranded/unstranded; mRNA/total RNA), sequencing platforms/depths, and gene count quantifications were included. All samples had been previously allocated to molecular BCP-ALL subtypes based on integration of independent genomic profiling, gene fusion calling and gene expression. For classification, data with defined subgroup allocation were randomly split into training (n=1831; 70%) and testing sets (n=785; 30%) and two hold-out studies (n=305). To define gene expression signatures of 21 BCP-ALL subtypes, we used training set samples and extracted specific gene sets employing different feature selection algorithms as well as ranking of genes in individual subtypes. Based on these definitions we established a BCP-ALL subtype classifier (ALLCatchR), which integrates machine learning models (support vector machines) and nearest-neighbors associations of single sample gene set enrichment analyses (Fig. A). ALLCatchR performed molecular subtype allocation for the testing set at high sensitivity (0.934±0.165) and specificity (0.997±0.005) on average across subtypes. A similar performance was achieved in the two hold-out cohorts despite completely independent data structures which have not been shown to the classifier (sensitivity: 0.946±0.097 / specificity: 0.996±0.007). Overall accuracies of 0.952 and 0.92 for testing and hold-out-data sets indicate that ALLCatchR reliably identifies molecular BCP-ALL subtypes in mixed adult / pediatric cohorts based on gene expression profiles alone (Fig. A, below). Among n=44/1090 (4%) samples of the combined hold-out/testing set with ambiguous subtype allocation (deemed 'unclassified'), ALLCatchR provides candidate subtype allocations (sensitivity: 0.671±0.393 / specificity: 0.978±0.036) for confirmation based on genomic driver alterations. In addition, we modified our classifier to predict baseline variables such as patient's sex (accuracy: 0.991), immunophenotype (accuracy: 0.935) or blast count (R=0.633). To define the corresponding B cell differentiation stage of origin, we compared BCP-ALL subtypes to differentiation stage-specific gene sets extracted from 7 FACS-sorted lymphopoietic subsets from healthy bone marrow donors (n=4). We observed defined BCP-ALL subtype-specific enrichment patterns which can be grouped along 4 major differentiation stages (Pro-B to Pre-B-II-Large, Fig. 1B) with a high degree of similarity in independent adult and pediatric cohorts. Thus, B cell developmental trajectories underlying BCP-ALL subtypes are dissected at high resolution by gene expression profiling and differentiation stages may favor the selection of specific leukemogenic drivers. ALLCatchR is a gene expression classifier for 21 established BCP-ALL molecular subtypes, which performs at independently validated cohorts with an accuracy of >90% and allows imputation of clinical baseline variables as well as underlying B cell developmental trajectories. It provides a reliable basis for systematic diagnostic approaches in pediatric and adult BCP-ALL patients. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Clinical Significance of IGH Clonality Analysis with RNA Sequencing in B-Cell Precursor Acute Lymphoblastic Leukemia

Background: B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common pediatric malignancy. Using minimal residual disease (MRD) and genetic subtypes, risk stratification algorithms have considerably improved clinical outcomes. However, despite being in a better prognostic group, a non-negligible number of relapses occur, and prognostic markers for these patients are required for more effective treatment strategies. RNA sequencing (RNA-seq) is a useful tool for detecting patient-specific immunoglobulin heavy chain (IGH) junctional sequences (Li et al., Leukemia. 2020), which can be informative for evaluating the nature of leukemic clones. Here, we analyzed IGH clonality of pediatric BCP-ALL using RNA-seq data and revealed its validity for evaluating disease characteristics and impact on prognosis. Methods: We analyzed RNA-seq data of 136 pediatric BCP-ALL samples at diagnosis and 46 samples at relapse from the TARGET ALL Expansion Phase 2 cohort. Using MiXCR (Bolotin et al., Nat Methods 2015) and Vidjil (Duez et al., PLoS One 2016), we detected IGH complete VDJ and incomplete DJ rearrangements. Then, we estimated the expected number of IGH rearrangements corresponding to each read count (E-value) by power-law model. Finally, we identified leukemic "disease clones” with E-values less than 0.01 (Fig. 1a). Results: First, we detected a total of 55,518 IGH clones, of which 48,555 were complete rearrangements and 6,963 were incomplete. In total, 351 disease clones (median 2, range 0-8 per sample) were identified by power-law model: 193 clones were complete rearrangements, and 158 clones were incomplete. Interestingly, the numbers and compositions (complete or incomplete rearrangements) of disease clones varied significantly between samples. Thus, we defined three clonality patterns and classified each sample based on identified disease clones: "Undetectable disease clones (UDC)” if the sample had no disease clone (n=18); "Incomplete Disease Clones (IDC)” if there were one or more disease clones with incomplete rearrangements (n=72); and "Complete Disease Clones (CDC)” if all disease clones of that sample have complete rearrangements (n=46). Gene Set Enrichment Analysis (GSEA) indicated a strong enrichment for hematopoietic stem cell (HSC) and multipotent progenitor (MPP) signatures in UDC and pro-B cell signatures in IDC. Similarly, CIBERSORTx (Newman et al., Nat Biotechnol 2019) showed higher proportions of HSCs, MPPs, and pre-pro B cells in UDC and pro-B cells in IDC. Furthermore, there was a significant enrichment in pathways promoting cell cycle progression in IDC. Of note, in high hyperdiploid (HHD) cases, which are typically associated with a favorable prognosis, event-free survival (EFS) was significantly lower in UDC and IDC (p=0.01) (Fig. 1b). Moreover, even in MRD-negative HHD patients, those with UDC and IDC had significantly worse EFS (p=0.01) and overall survival (p=0.03) (Fig. 1b). Finally, we compared the primary and relapse clones in 46 patients. As a result, 37 patients had one or more disease clones at diagnosis, and 29 (78%) had these primary disease clones at relapse. On the other hand, 9 (24%) patients showed relapse-specific disease clones, which should lead to relapse in these cases. The other 9 primary patients with UDC had no disease clone also at relapse. Discussions: We identified three clonality patterns, UDC, IDC, and CDC based on RNA-seq analysis of the IGH rearrangement status. According to the findings of GSEA and CIBERSORTx, leukemic cells of UDC, IDC, and CDC patients have nature of progenitors to pre-proB cells, pro-B cells, and more mature cells, respectively. These findings are consistent with the knowledge that IGH rearrangement occurs during pro-B to pre-B maturation in normal B-cells. Patients with immature clonality status, UDC and IDC, were associated with poor prognosis in high hyperdiploid subtype and could be a potential prognostic marker in MRD-negative patients. In addition, we revealed that primary disease clones contributed to relapse in 78% of cases, while relapse-specific clones were implicated in relapse in 24% of cases. Additionally, UDC clonality pattern was carried over at relapse. These findings shed light on the mechanisms of leukemic clonal evolution and relapse, and suggest that the status of IGH rearrangement may serve as a novel prognostic marker in BCP-ALL. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

AGR2 expression as a predictive biomarker for therapy response in esophageal squamous cell carcinoma.

Despite multidisciplinary therapy, the prognosis is poor for esophageal squamous cell carcinoma (ESCC). In the locally advanced stage, neoadjuvant chemoradiotherapy (nCRT) followed by surgery could provide survival benefits to some patients. Here, we aimed to identify for tumor therapy response a biomarker based on RNA sequencing. We collected endoscopic biopsies of 32 ESCC patients, who were divided according to nCRT response, into two groups: the complete response group (n = 13) and the non-complete response group (n = 19). RNA-sequencing data showed that 464 genes were differentially expressed. Increased in non-complete response group, 4 genes increased expressions were AGR2 (anterior gradient 2), GADD45B (growth arrest and DNA damage inducible beta), PPP1R15A (protein phosphatase 1 regulatory subunit 15A) and LRG1 (leucine rich alpha-2-glycoprotein 1). The areas under the curve (AUC) of the AGR2 gene was 0.671 according to read counts of RNA-seq and therapy response of nCRT. In vitro study showed that apoptosis cell was significantly increased in the AGR2-knockdown TE-2 cell line treated with cisplatin and 5-Fluorouracil (5-FU), when compared with si-control. Results suggest that in ESCC, the AGR2 gene is a promising and predictive gene marker for the response to anti-tumor therapy.

DNA read count calibration for single-molecule, long-read sequencing

There are many applications in which quantitative information about DNA mixtures with different molecular lengths is important. Gene therapy vectors are much longer than can be sequenced individually via short-read NGS. However, vector preparations may contain smaller DNAs that behave differently during sequencing. We have used two library preparations each for Pacific Biosystems (PacBio) and Oxford Nanopore Technologies NGS to determine their suitability for quantitative assessment of varying sized DNAs. Equimolar length standards were generated from E. coli genomic DNA. Both PacBio library preparations provided a consistent length dependence though with a complex pattern. This method is sufficiently sensitive that differences in genomic copy number between DNA from E. coli grown in exponential and stationary phase conditions could be detected. The transposase-based Oxford Nanopore library preparation provided a predictable length dependence, but the random sequence starts caused the loss of original length information. The ligation-based approach retained length information but read frequency was more variable. Modeling of E. coli versus lambda read frequency via cubic spline smoothing showed that the shorter genome could be used as a suitable internal spike-in for DNAs in the 200 bp to 10 kb range, allowing meaningful QC to be carried out with AAV preparations.

PSAT091 The Impact of Pregnancy and Aging on the Female Mouse Adrenal Transcriptome

Abstract Introduction Mouse adrenal glands contain a zona glomerulosa (mineralocorticoid production), zona fasciculata (glucocorticoid production), and a remnant of the fetal adrenal cortex (termed the x-zone). Instead of involuting at birth, as seen in humans, the x-zone expands postnatally and is predicted to function in the metabolism of progesterone and 11-deoxycorticosterone via its high expression of the enzyme, 20α-hydroxysteroid dehydrogenase (Akr1c18). Moreover, mouse adrenals have a clear x-zone sex-divergence at the time of puberty when the x-zone regresses in males. In females, the x-zone regresses during the first pregnancy and with aging. Sexual dimorphism unrelated to the x-zone has been documented in mouse adrenals, including differentially expressed sex-chromosome and sex-steroid driven genes, making the definition of x-zone specific transcripts more challenging. Objective Use RNA sequencing (RNAseq) to better define the x-zone transcriptome by comparing virgin mice to post-pregnant and aged females. Methods Adrenals from four 25-week-old (wk) virgin, four 25 wk post-pregnant, and four 50 wk female mice were used for RNAseq. Reads were aligned to the mm38 assembly with STAR. Expression was quantified with featureCounts. Normalization was performed with edgeR using the TMM method. Unwanted sources of variation were removed with sva. Statistical comparison was performed using limma. Statistically significant (FDR-adjusted, P&amp;lt;0.05) expression between groups allowed characterization of differentially expressed genes (DEGs). Results RNAseq allowed the analysis of adrenal gland RNA transcripts and identified 17,829 genes that met read count thresholds. 246 DEGs were found between virgin and post-pregnant females with 93 showing higher expression in virgin adrenals and 153 with higher expression in post-pregnant female adrenals. The most altered genes included Akr1c18 (fold change (FC) = 174.06; higher in virgin adrenals) and the proton transporter, Otop1 (FC = 20.82; higher in post-pregnant adrenals). When comparing 25 wk to 50 wk adrenals, 269 DEGs were found with 136 showing higher expression in 25 wk adrenals and 133 higher in the 50 wk adrenals. The most altered genes were Akr1c18 (FC = 34.17; higher in 25 wk adrenals) and the mouse mast cell protease, Mcpt2 (FC = 14.42; higher in 50 wk adrenals). The 25 wk virgin mice had 22 transcripts that were higher and 41 that were lower than found in both post-pregnant and 50 wk animals. Within this list are the established x-zone markers: Akr1c18 and the phosphoinositide 3-kinase, Pik3c2g. Discussion Clear differences in transcript levels were observed between virgin mice and those who had underwent pregnancy or aged to 50 wk. Both aged and post-pregnant mice showed lower expression of known x-zone transcripts and RNAseq analysis provided a tentative list of additional x-zone markers. Using female mice with differing x-zone status reduced the number of sexually dimorphic genes and allowed the characterization of genes related to the x-zone. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m.

Read Count Patterns and Detection of Cancerous Copy Number Alterations in Plasma Cell-Free DNA Whole Exome Sequencing Data for Advanced Non-Small Cell Lung Cancer.

Plasma cell-free DNA (cfDNA) sequencing data have been widely studied for early diagnosis and treatment response or recurrence monitoring of cancers because of the non-invasive benefits. In cancer studies, whole exome sequencing (WES) is mostly used for discovering single nucleotide variants (SNVs), but it also has the potential to detect copy number alterations (CNAs) that are mostly discovered by whole genome sequencing or microarray. In clinical settings where the quantity of the acquired blood from the patients is limited and where various sequencing experiments are not possible, providing various types of mutation information such as CNAs and SNVs using only WES will be helpful in the treatment decision. Here, we questioned whether the plasma cfDNA WES data for patients with advanced non-small cell lung cancer (NSCLC) could be exploited for CNA detection. When the read count (RC) signals of the WES data were investigated, a similar fluctuation pattern was observed among the signals of different samples, and it can be a major challenge hindering CNA detection. When these RC patterns among cfDNA were suppressed by the method we proposed, the cancerous CNAs were more distinguishable in some samples with higher cfDNA quantity. Although the potential to detect CNAs using the plasma cfDNA WES data for NSCLC patients was studied here, further studies with other cancer types, with more samples, and with more sophisticated techniques for bias correction are required to confirm our observation. In conclusion, the detection performance for cancerous CNAs can be improved by controlling RC bias, but it depends on the quantity of cfDNA in plasma.

2FAST2Q: a general-purpose sequence search and counting program for FASTQ files.

BackgroundThe increasingly widespread use of next generation sequencing protocols has brought the need for the development of user-friendly raw data processing tools. Here, we explore 2FAST2Q, a versatile and intuitive standalone program capable of extracting and counting feature occurrences in FASTQ files. Despite 2FAST2Q being previously described as part of a CRISPRi-seq analysis pipeline, in here we further elaborate on the program’s functionality, and its broader applicability and functions.Methods2FAST2Q is built in Python, with published standalone executables in Windows MS, MacOS, and Linux. It has a familiar user interface, and uses an advanced custom sequence searching algorithm.ResultsUsing published CRISPRi datasets in which Escherichia coli and Mycobacterium tuberculosis gene essentiality, as well as host-cell sensitivity towards SARS-CoV2 infectivity were tested, we demonstrate that 2FAST2Q efficiently recapitulates published output in read counts per provided feature. We further show that 2FAST2Q can be used in any experimental setup that requires feature extraction from raw reads, being able to quickly handle Hamming distance based mismatch alignments, nucleotide wise Phred score filtering, custom read trimming, and sequence searching within a single program. Moreover, we exemplify how different FASTQ read filtering parameters impact downstream analysis, and suggest a default usage protocol. 2FAST2Q is easier to use and faster than currently available tools, efficiently processing not only CRISPRi-seq / random-barcode sequencing datasets on any up-to-date laptop, but also handling the advanced extraction of de novo features from FASTQ files. We expect that 2FAST2Q will not only be useful for people working in microbiology but also for other fields in which amplicon sequencing data is generated. 2FAST2Q is available as an executable file for all current operating systems without installation and as a Python3 module on the PyPI repository (available at https://veeninglab.com/2fast2q).

Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells

BackgroundSophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem.MethodsWe applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts.ResultsWe show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions.ConclusionsOur results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes.

Transcription start site signal profiling improves transposable element RNA expression analysis at locus-level.

The transcriptional activity of Transposable Elements (TEs) has been involved in numerous pathological processes, including neurodegenerative diseases such as amyotrophic lateral sclerosis and frontotemporal lobar degeneration. The TE expression analysis from short-read sequencing technologies is, however, challenging due to the multitude of similar sequences derived from singular TEs subfamilies and the exaptation of TEs within longer coding or non-coding RNAs. Specialised tools have been developed to quantify the expression of TEs that either relies on probabilistic re-distribution of multimapper count fractions or allow for discarding multimappers altogether. Until now, the benchmarking across those tools was largely limited to aggregated expression estimates over whole TEs subfamilies. Here, we compared the performance of recently published tools (SQuIRE, TElocal, SalmonTE) with simplistic quantification strategies (featureCounts in unique, fraction and random modes) at the individual loci level. Using simulated datasets, we examined the false discovery rate and the primary driver of those false positive hits in the optimal quantification strategy. Our findings suggest a high false discovery number that exceeds the total number of correctly recovered active loci for all the quantification strategies, including the best performing tool TElocal. As a remedy, filtering based on the minimum number of read counts or baseMean expression improves the F1 score and decreases the number of false positives. Finally, we demonstrate that additional profiling of Transcription Start Site mapping statistics (using a k-means clustering approach) significantly improves the performance of TElocal while reporting a reliable set of detected and differentially expressed TEs in human simulated RNA-seq data.

Characterizing the amplification of STR markers in multiplex polymerase chain displacement reaction using massively parallel sequencing

Polymerase chain displacement reaction (PCDR) showed advantages in forensic low-template DNA analysis with improved amplification efficiency, higher allele detection capacity, and lower stutter artifact than PCR. However, characteristics of STR markers after PCDR amplification remain unclarified for the limited resolving power of capillary electrophoresis (CE). This issue can be addressed by massively parallel sequencing (MPS) technology with higher throughput and discriminability. Here, we developed a multiplex PCDR system including 24 STRs and amelogenin. In addition, a PCR reference was established for comparison. After amplification, products were subjected to PCR-free library construction and sequenced on the Illumina NovaSeq system. We implemented a sequence-matching pipeline to separate different amplicon types of PCDR products from the combination of primers. In the sensitivity test, the PCDR multiplex obtained full STR profiles with as low as 125 pg 2800M control DNA. Based on that, single-source DNA samples were tested. First, highly concordant genotypes were observed among the PCDR multiplex, the PCR reference, and CE-based STR kits. Next, read counts of different PCDR amplicon types were investigated, showing a relative abundance of 78:12:12:1 for the shortest amplicon S, the two medium amplicons M1 and M2, and the longest amplicon L. We also analyzed the stutter artifacts for distinct amplicon types, and the results revealed the reduction of N − 1 and N − 2 contraction stutters, and the increase of N + 1 and N + 2 elongation stutters in PCDR samples. Moreover, we confirmed the feasibility of PCDR for amplifying degraded DNA samples and unbalanced DNA mixtures. Compared to the previous proof of principle study, our work took a further step to characterize the complete profile of STR markers in the PCDR context. Our results suggested that the PCDR-MPS workflow is an effective approach for forensic STR analysis. Corresponding findings in this study may help the development of PCDR-based assays and probabilistic methods in future studies.

Exponential Slope from Absolute Lymphocyte Counts during Radio-Chemotherapy Can Predict an Aggressive Course of Cervical Cancer.

Simple SummaryLymphopenia associated with clinical outcomes has three potential implications for cancer progression, treatment effects, and constitutional immune capacity. Neutrophil-to-lymphocyte ratio (NLR) and lymphopenia before and after treatment are biomarkers of immune suppression due to cancer progression and treatment effects. A decrease and an increase in the exponential slope (α) estimated from total absolute lymphocyte counts during concurrent radio–chemotherapy for cervical cancer are associated with aggressive and non-aggressive disease courses and are completely independent of stage and NLR. The association between survival and α-related mRNAs from plasma exosomes may imply an insufficient restoration of unstimulated lymphocytes in the context of immune response. The proposed α may be a practical and simple biomarker of constitutional immune capacity.This study aimed to investigate whether the exponential slope α from absolute lymphocyte counts during concurrent radio–chemotherapy (CRT) is associated with aggressive and non-aggressive courses of cervical cancer. We analyzed 362 patients with stage IB–IVB cervical cancer treated with CRT in two groups: 323 patients without mRNA data (cohort 1) and 39 with mRNA data (cohort 2) from plasma exosomes. We calculated the α of each patient; 69 patients who died of cancer in cohort 1 were divided into 44 who died within 30 months (aggressive group), and 25 who died after more than 30 months (non-aggressive group). The median follow-up periods of cohorts 1 and 2 were 63 and 28 months, respectively. The log2 fold change (log2FC) between read counts of mRNAs before treatment and after the second week of CRT was calculated. Multivariate analyses from cohort 1 showed that neutrophil-to-lymphocyte ratio (NLR) ≥ 2.43 and α < 0.08 were statistically significant predictors of disease-specific survival (DSS) in the aggressive group (DSS-A), whereas α ≥ 0.08 was the only significant predictor of DSS in the non-aggressive group (DSS-NA). The 2.5-year DSS-A and 8-year DSS-NA rates of patients with α ≥ 0.08 and α < 0.08 were 86.7% and 73%, and 78.5% and 94.8% in the high-NLR group, respectively. In cohort 2, patients with both NLR < 2.7 and α ≥ 0.07 had a higher 2.5-year DSS rate than did those with either NLR ≥ 2.72 or α < 0.07. E2F8 and STX6 significantly correlated with ɑ and survival. The 2.5-year DSS rates in patients with E2F8 + STX6 (log2FC) < 0.2429 and ≥0.2429 were 100% and 77.2%, respectively. The exponential slope α can potentially distinguish between aggressive and non-aggressive courses in cervical cancer patients.

Regulation of Genes Related to Cognition after tDCS in an Intermittent Hypoxic Brain Injury Rat Model.

Background: Hypoxic brain injury is a condition caused by restricted oxygen supply to the brain. Several studies have reported cognitive decline, particularly in spatial memory, after exposure to intermittent hypoxia (IH). However, the effect and mechanism of action of IH exposure on cognition have not been evaluated by analyzing gene expression after transcranial direct current stimulation (tDCS). Hence, the purpose of this study was to investigate the effects of tDCS on gene regulation and cognition in a rat model of IH-induced brain injury. Methods: Twenty-four 10-week-old male Sprague–Dawley rats were divided into two groups: IH exposed rats with no stimulation and IH-exposed rats that received tDCS. All rats were exposed to a hypoxic chamber containing 10% oxygen for twelve hours a day for five days. The stimulation group received tDCS at an intensity of 200 µA over the frontal bregma areas for 30 min each day for a week. As a behavior test, the escape latency on the Morris water maze (MWM) test was measured to assess spatial memory before and after stimulation. After seven days of stimulation, gene microarray analysis was conducted with a KEGG mapper tool. Results: Although there were no significant differences between the groups before and after stimulation, there was a significant effect of time and a significant time × group interaction on escape latency. In the microarray analysis, significant fold changes in 12 genes related to neurogenesis were found in the stimulation group after tDCS (p < 0.05, fold change > 2 times, the average of the normalized read count (RC) > 6 times). The highly upregulated genes in the stimulation group after tDCS were SOS, Raf, PI3K, Rac1, IRAK, and Bax. The highly downregulated genes in the stimulation group after tDCS were CHK, Crk, Rap1, p38, Ras, and NF-kB. Conclusion: In this study, we confirmed that SOS, Raf, PI3K, Rac1, IRAK, and Bax were upregulated and that CHK, Crk, Rap1, p38, Ras, and NF-kB were downregulated in a rat model of IH-induced brain injury after application of tDCS.

An ambiguous sequence-based allele of SE33

SE33 was a well-known autosomal short tandem repeat (STR) marker that was high polymorphic and therefore was high discrimination power. The sequence structure of STR markers has been increasingly explored with next-generation sequencing (NGS) technology. The sequencing resulted in the development of a new locus designation and allele nomenclature that was also backward compatible with the conventional capillary electrophoresis. SE33 was one of the STR markers that had been coamplified by Forenseq™ Signature Prep Kit (Verogen) but were not analyzed and illustrated in the Universal Analysis Software (UAS) (Verogen). This study reported an ambiguous sequence-based allele 16.3 of the SE33 locus. This allele was observed while analyzed by STRait Razor 3.0. The configuration file was modified from the previous studies to include 15 bp of 5′ flanking region and 24 bp of 3′ flanking region. The ambiguous allele was called 16.3 (106 bp) with a read count of 2070. However, the sequence of the repeat region cannot be designated as allele 16.3. Several possible scenarios for allele designation were presented and discussed.

Variations in Bacterial Communities and Antibiotic Resistance Genes Across Diverse Recycled and Surface Water Irrigation Sources in the Mid-Atlantic and Southwest United States: A CONSERVE Two-Year Field Study.

Reduced availability of agricultural water has spurred increased interest in using recycled irrigation water for U.S. food crop production. However, there are significant knowledge gaps concerning the microbiological quality of these water sources. To address these gaps, we used 16S rRNA gene and metagenomic sequencing to characterize taxonomic and functional variations (e.g., antimicrobial resistance) in bacterial communities across diverse recycled and surface water irrigation sources. We collected 1 L water samples (n = 410) between 2016 and 2018 from the Mid-Atlantic (12 sites) and Southwest (10 sites) U.S. Samples were filtered, and DNA was extracted. The V3-V4 regions of the 16S rRNA gene were then PCR amplified and sequenced. Metagenomic sequencing was also performed to characterize antibiotic, metal, and biocide resistance genes. Bacterial alpha and beta diversities were significantly different (p < 0.001) across water types and seasons. Pathogenic bacteria, such as Salmonella enterica, Staphylococcus aureus, and Aeromonas hydrophilia were observed across sample types. The most common antibiotic resistance genes identified coded against macrolides/lincosamides/streptogramins, aminoglycosides, rifampin and elfamycins, and their read counts fluctuated across seasons. We also observed multi-metal and multi-biocide resistance across all water types. To our knowledge, this is the most comprehensive longitudinal study to date of U.S. recycled water and surface water used for irrigation. Our findings improve understanding of the potential differences in the risk of exposure to bacterial pathogens and antibiotic resistance genes originating from diverse irrigation water sources across seasons and U.S. regions.

Altered non-coding RNA profiles of seminal plasma extracellular vesicles of men with poor semen quality undergoing in vitro fertilization treatment.

Currently, the precise mechanisms that underline male infertility are still unclear. Accumulating data implicate non-coding RNA cargo of seminal plasma extracellular vesicles due to their association with poor semen quality and higher expression levels relative to vesicle-free seminal plasma. We assessed sperm-free seminal plasma extracellular vesicle non-coding RNA profiles from 91 semen samples collected from male participants of couples seeking infertility treatment. Men were classified into two groups (poor, n=32; normal, n=59) based on World Health Organization semen cutoffs. Small RNA sequencing reads were mapped to standard biotype-specific transcriptomes in the order micro RNA>transfer RNA>piwi-interacting RNA>ribosomal RNA >ribosomal RNA>circular RNA>long non-coding RNA using STAR. Differential expression of normalized non-coding RNA read counts between the two groups was conducted by EdgeR (Fold change ≥1.5 and (false discovery rate [FDR]<0.05). Small RNA sequencing identified a wide variety of seminal plasma extracellular vesicle non-coding RNA biotypes including micro RNA, ribosomal RNAs, piwi-interacting RNAs, transfer RNA, long non-coding RNAs as well as circular RNAs, and fragments associated with pseudogenes, and nonsense-mediated decay. The expression levels of 57 seminal plasma extracellular vesicle non-coding RNAs (micro RNA: 6, piwi-interacting RNA: 4, ribosomal RNA: 6, circular RNA: 34, and long non-coding RNA: 7) were altered in men with poor semen quality relative to normal semen parameters, many (60%) of which were circular RNA species. Ontology analysis of differentially expressed micro RNAs and circular RNAs showed enrichment in functional terms related to cellular communication and early development. This is the first study to generate comprehensive seminal plasma extracellular vesicle non-coding RNA profiles in a clinical setting and to determine the differences between men with normal and abnormal semen parameters. Thus, our study suggests that seminal plasma extracellular vesicle non-coding RNAs may represent novel biomarkers of male reproductive phenotypes.

ANALYTICAL PERFORMANCE OF A DNA/RNA-BASED NEXT-GENERATION SEQUENCING (NGS) PANEL FOR MYELOID NEOPLASMS

Myeloid neoplasms (MNs) are a heterogeneous group of neoplasms including acute myeloid leukemia, myelodysplastic syndrome, myeloproliferative neoplasms and myelodysplastic syndrome/myeloproliferative neoplasms. MNs show a complex genetic basis, and the number of alterations to be analyzed for risk diagnostic, stratification and management of patients increases continuously. Consequently, there is a demand for multiple molecular and cytogenetic techniques with the aim of detecting a wide variety of genetic abnormalities. Since conventional techniques are insufficient to study all such abnormalities, the NGS panel has become a useful tool for broad characterization of the spectrum of mutations in MNs. However, before NGS panels can be introduced in the laboratory routine, their performance should be evaluated. In this context, we describe the analytical performance of the Oncomine™Myeloid Research Assay (OMA) (Thermo Fisher), a DNA/RNA-based amplicon sequencing panel, that evaluated simultaneously 40 genes (17 full genes and 23 hotspots genes) and 29 fusions drivers genes (over 600 partners) known to be recurrently mutated in MNs. Commercial controls Seraseq Myeloid Mutation DNA and the Seraseq Myeloid Fusion RNA were used for sensitivity, specificity, accuracy, repeatability, reproducibility and limit of detection (LoD) testing. Prospective sequencing was performed for samples from 34 patients presenting with suspected or confirmed MN. Libraries was prepared by manual method and sequenced by the Ion S5 Sequencer. Data obtained were analysed on Ion Reporter 5.18 software. The OMA assay showed good performance in terms of depth of coverage, on-target reads, and uniformity. The mean reads lenght ranged from 223 to 231 bp for DNA sample and from 88 to 124 bp for RNA, within the expected standard for the panel. The panel achieved 98,8% and 100% concordance with DNA and RNA controls, reaching analytical sensitivity of 99% and 100% for DNA and RNA, respectively. Regarding the type of DNA mutation: single nucleotide variants (SNP), indels or indels hot spots, the sensitivity found was 100%, 96% and 95%, respectively. The accuracy and analytical specificity was 100% for DNA panel. The LoD was 5% variant allele fraction (VAF) for DNA (SNP and indel) and 1-log reduction from initial number of reads counts for RNA fusion genes. Repeatability and reproducibility were 98% for DNA and 100% for RNA. However, the coefficient of variation (CV) within-run and between-run in VAF was >20% for large indels in CALR, FLT3 (ITD) and SRSF2 . The vertical coverage of each sample was inspected with a coverage threshold of 100X. Some amplicons in the assay failed repeatedly (ASXL1_1.875, BCOR_8.202477, BRAF_18.30633, CEBPA_1.1.86676, PRPF8_12.173121, SH2B3_1.85663). However, the OMA is unable to detect specific variants in ASXL1 (c.1900_1922del23 and c.1934_1935insG) and CEBPA (c.68_69insC). This is a known limitation of the Ion Torrent chemistry for accurate sequencing of large homopolymers and high GC content regions. Of the 34 patient samples, 28 (87.5%) had at least one reportable variant. A total of 58 DNA variants and six gene fusions were detected. Some variants were confirmed by orthogonal techniques. In conclusion, OMA is a highly accurate and reproducible NGS panel for the detection of common genetic abnormalities in MNs.

Environmental DNA metabarcoding read numbers and their variability predict species abundance, but weakly in non‐dominant species

AbstractMetabarcoding of environmental DNA (eDNA) provides more comprehensive, efficient, and non‐invasive sampling of biological communities than conventional methods. However, limitations of metabarcoding include taxon‐specific biases in amplification and sequencing that distort assessments of community composition. Further, hyper‐abundant species may disproportionately affect community composition assessments and impair the detection of rare species (i.e., “species masking”). In this study, we examine methodological approaches to improve eDNA metabarcoding assessments of community structure using fish community diversity in a pond in south Florida using MiFish primers modified to improve cichlid detection. Mitochondrial 12S eDNA amplicon sequencing via Illumina NovaSeq was analyzed using the DADA2 model‐based exact sequence inference. The fish species and abundances in the system were recorded during piscicide treatment and subsequent native species restocking. Our results demonstrate that (1) ultra‐high‐throughput sequencing on the newer NovaSeq patterned flow cell provided reliable detection of very rare taxa—with detections of a single individual. (2) Read numbers were significantly correlated to the total surface area of the fish population, and numerical abundance to a lesser degree; however, dominant taxa largely drove those correlations, and simulations showed that biases in the most abundant taxa will have disproportionate effects on the strength of the correlation. (3) The read number coefficient of variation for each species across spatially separated replicate samples may provide less biased abundance estimates compared with estimates based on average read counts. Finally, (4) exact sequence inference detected multiple haplotypes and population genetic diversity within a species. Our results demonstrate the real‐world metabarcoding capacity to reveal community structure and reliably detect rare species and unique haplotypes and shows that read numbers can, to a limited degree, be used to infer the size of fish populations. Careful examination of detection biases among dominant taxa and spatial variation among samples are required for rigorous eDNA‐based estimates of community structure. Our results demonstrate the capacity of NovaSeq metabarcoding to reveal freshwater fish community structure and reliably detect rare species and unique haplotypes. Metabarcoding read numbers were significantly correlated to the total surface area of the fish species' populations, allowing for conditional inferences of population sizes. However, dominant taxa largely drove those correlations, and simulations indicated that biases toward the most abundant taxa will have disproportionate effects on the strength of the correlation.

Development and validation of targeted environmental DNA (eDNA) metabarcoding for early detection of 69 invasive fishes and aquatic invertebrates

AbstractInvasive species are of concern due to their impacts on ecosystems and economies, but they pose significant control challenges. Environmental DNA (eDNA) is a powerful tool in the detection of aquatic organisms at low densities due to high detection sensitivity and relative ease of sample collection. Aquatic eDNA analyses have increased worldwide and are generally either applied to few target species (quantitative PCR), or for broad taxonomic applications (metabarcoding). Here, we describe the development and testing of a hybrid approach that utilized high‐sensitivity PCR primer sets and high‐throughput sequencing (HTS), referred to as targeted metabarcoding, to detect 69 fishes and invertebrates. We identified target species based on reports of globally important invasive species and developed two independent PCR primers for each species (CO1 and a second mtDNA region). We assessed sensitivity and eDNA interference for all 138 primers (2 per species and 69 species) using standard end‐point PCR and tested them on 10 eDNA samples spiked with various amounts of one or more of the target species DNA. The sensitivity of the 138 primer sets ranged between 1.5 × 10−5 and 2.64 ng template DNA (mean = 0.069 ng). Primers were also tested for interference effects using plankton eDNA to simulate field conditions. The inclusion of interfering plankton DNA reduced the sensitivity for most primer sets by one or more orders of magnitude (range 0–3). Overall, our targeted metabarcoding resulted in the detection of ~98% of species in the DNA spiked samples, and perhaps more importantly, the HTS read count was positively related to the quantity of spiked DNA (p &lt; 0.002). We envision this technique being particularly useful for the early detection of species at low population densities; however, there are diverse applications of targeted metabarcoding for monitoring aquatic community composition and to quantify ecosystem change and health.

NoisET: Noise Learning and Expansion Detection of T-Cell Receptors.

High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. We review methods for accounting for both biological and experimental noise and present an easy-to-use python package NoisET that implements and generalizes a previously developed Bayesian method. It can be used to learn experimental noise models for repertoire sequencing from replicates, and to detect responding clones following a stimulus. We test the package on different repertoire sequencing technologies and data sets. We review how such approaches have been used to identify responding clonotypes in vaccination and disease data. Availability: NoisET is freely available to use with source code at github.com/statbiophys/NoisET.