Downstream Analysis Research Articles

Multimodal joint deconvolution and integrative signature selection in proteomics

Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL.

Optimized Method for High‐Quality RNA Extraction from Formalin‐Fixed Paraffin‐Embedded Tissues

AbstractRNA extraction from formalin‐fixed paraffin‐embedded tissues (FFPE) presents a notable challenge. Extracting RNA from FFPE tissues has been challenging due to RNA degradation and chemical bonding between RNA and proteins during the fixation process. This results in reduced concentration and quality of the extracted RNA from FFPE tissues, impacting subsequent downstream analyses. In this study, we present an optimized approach involves replacing the tissue temperature‐digestion step with tissue crushing and homogenization. RNA extraction was performed on 100 FFPE tissue blocks obtained from the archive of Pathology Department of Shiraz University of Medical Sciences. After deparafinization of the tissues, RNA was extracted using this modified method, and the results compared to those of a standard extraction procedure and commercial kits used for FFPE tissue. Then gel electrophoresis and Real time PCR were done to check the quality and integrity of the extracted RNA. The results show that the concentration of RNA obtained from this method was significantly (P value <0.0001) higher than the other methods. In conclusion, we have successfully introduced a modified method that enables the extraction of high‐quality and intact RNA from FFPE tissue. This method stands as a reliable option for molecular biology studies necessitating precise RNA extraction.

A roadmap of phylogenomic methods for studying polyploid plant genera

AbstractPhylogenetic inference of polyploid species is the first step towards understanding their patterns of diversification. In this paper, we review the challenges and limitations of inferring species relationships of polyploid plants using traditional phylogenetic sequencing approaches, as well as the mischaracterization of the species tree from single or multiple gene trees. We provide a roadmap to infer interspecific relationships among polyploid lineages by comparing and evaluating the application of current phylogenetic, phylogenomic, transcriptomic, and whole‐genome approaches using different sequencing platforms. For polyploid species tree reconstruction, we assess the following criteria: (1) the amount of prior information or tools required to capture the genetic region(s) of interest; (2) the probability of recovering homeologs for polyploid species; and (3) the time efficiency of downstream data analysis. Moreover, we discuss bioinformatic pipelines that can reconstruct networks of polyploid species relationships. In summary, although current phylogenomic approaches have improved our understanding of reticulate species relationships in polyploid‐rich genera, the difficulties of recovering reliable orthologous genes and sorting all homeologous copies for allopolyploids remain a challenge. In the future, assembled long‐read sequencing data will assist the recovery and identification of multiple gene copies, which can be particularly useful for reconstructing the multiple independent origins of polyploids.

A method for sampling the living wood microbiome

Abstract Efforts to characterize microbial life across diverse environments have progressed tremendously, yet the microbiome of Earth's largest biomass reservoir—the wood of living trees—has been largely unexplored. Current understanding of the tree microbiome is largely confined to roots and leaves, with little attention given to the endophytic microbiome of wood, even though emergent studies have indicated this zone as a niche for unique taxa, of consequence for ecosystem health and global biogeochemical cycles. The lack of investigation derives partly from the physical recalcitrance of wood, which presents challenges during sampling, homogenization, and the extraction of nucleic acids. In response to these issues, we present an optimized method for processing wood for use in microbial analyses, from sampling through to downstream analyses. Using methane‐cycling taxa as model endophytes, we assess losses in recovery during our method, and determine a limit‐of‐detection of approximately 500 cells per 100 mg of (dry) wood. For all six species evaluated—which represented several diverse taxa of hardwoods and softwoods—PCR inhibition proved minimal, and we expect this method to be applicable for a majority of tree species. The methods presented herein can facilitate future investigation into the wood microbiome and global microbial ecology of methane cycling.

Impact of the inaccessible genome on genotype imputation and genome-wide association studies.

Genotype imputation is widely used in genome-wide association studies (GWAS). However, both the genotyping chips and imputation reference panels are dependent on next-generation sequencing (NGS). Due to the nature of NGS, some regions of the genome are inaccessible to sequencing. To date, there has been no complete evaluation of these regions and their impact on the identification of associations in GWAS remains unclear. In this study, we systematically assess the extent to which variants in inaccessible regions are underrepresented on genotyping chips and imputation reference panels, in GWAS results and in variant databases. We also determine the proportion of genes located in inaccessible regions and compare the results across variant masks defined by the 1000 Genomes Project and the TOPMed program. Overall, fewer variants were observed in inaccessible regions in all categories analyzed. Depending on the mask used and normalized for region size, only 4%-17% of the genotyped variants are located in inaccessible regions and 52 to 581 genes were almost completely inaccessible. From the Cooperative Health Research in South Tyrol (CHRIS) study, we present a case study of an association located in an inaccessible region that is driven by genotyped variants and cannot be reproduced by imputation in GRCh37. We conclude that genotyping, NGS, genotype imputation and downstream analyses such as GWAS and fine mapping are systematically biased in inaccessible regions, due to missed variants and spurious associations. To help researchers assess gene and variant accessibility, we provide an online application (https://gab.gm.eurac.edu).

Site-specific mutagenesis screening in KRASG12D mutant library to uncover resistance mechanisms to KRASG12D inhibitors

KRAS plays a crucial role in regulating cell survival and proliferation and is one of the most commonly mutated oncogenes in human cancers. The novel KRASG12D inhibitor, MRTX1133, demonstrates promising antitumor efficacy in vitro and in vivo. However, the development of acquired resistance in treated patients presents a considerable challenge to sustained therapeutic effectiveness. In response to this challenge, we conducted site-specific mutagenesis screening to identify potential secondary mutations that could induce resistance to MRTX1133. We screened a range of KRASG12D variants harboring potential secondary mutations, and 44 representative variants were selected for in-depth validation of the pooled screening outcomes. We identified eight variants (G12D with V9E, V9W, V9Q, G13P, T58Y, R68G, Y96W, and Q99L) that exhibited substantial resistance, with V9W showing notable resistance, and downstream signaling analyses and structural modeling were conducted. We observed that secondary mutations in KRASG12D can lead to acquired resistance to MRTX1133 and BI-2865, a novel pan-KRAS inhibitor, in human cancer cell lines. This evidence is critical for devising new strategies to counteract resistance mechanisms and, ultimately, enhance treatment outcomes in patients with KRASG12D-mutant cancers.

Genetic associations of cardiovascular risk genes in European patients with coronary artery spasm.

Coronary artery spasm (CAS) is a frequent finding in patients presenting with angina pectoris. Although the pathogenesis of CAS is incompletely understood, previous studies suggested a genetic contribution. Our study aimed to elucidate genetic variants in a cohort of European patients with angina and unobstructed coronary arteries who underwent acetylcholine (ACh) provocation testing. A candidate association analysis of 208 genes previously associated with cardiovascular conditions was performed using genotyped and imputed variants in patients grouped in epicardial (focal, diffuse) CAS (n = 119) and microvascular CAS (n = 87). Patients with a negative ACh test result (n = 45) served as controls. We found no association below the genome-wide significance threshold of p < 5 × 10-8, thus not confirming variants in ALDH2, NOS3, and ROCK2 previously reported in CAS patients of Asian ancestry. However, the analysis identified suggestive associations (p < 10-05) for the groups of focal epicardial CAS (CDH13) and diffuse epicardial CAS (HDAC9, EDN1). Downstream analysis of the potential EDN1 risk locus showed that CAS patients have significantly increased plasma endothelin-1 levels (ET-1) compared to controls. An EDN1 haplotype comprising rs9349379 and rs2070698 was significantly associated to ET-1 levels (p = 0.01). In summary, we suggest EDN1 as potential genetic risk loci for patients with diffuse epicardial CAS, and European ancestry. Plasma ET-1 levels may serve as a potential cardiac marker.

MinION Sequencing of Fungi in Sub-Saharan African Air and a Novel LAMP Assay for Rapid Detection of the Tropical Phytopathogenic Genus Lasiodiplodia.

To date, there have been no DNA-based metabarcoding studies into airborne fungi in tropical Sub-Saharan Africa. In this initial study, 10 air samples were collected onto Vaseline-coated acrylic rods mounted on drones flown at heights of 15-50 meters above ground for 10-15 min at three sites in Ghana. Purified DNA was extracted from air samples, the internal transcribed spacer (ITS) region was amplified using fungal-specific primers, and MinION third-generation amplicon sequencing was undertaken with downstream bioinformatics analyses utilizing GAIA cloud-based software (at genus taxonomic level). Principal coordinate analyses based on Bray-Curtis beta diversity dissimilarity values found no clear evidence for the structuring of fungal air communities, nor were there significant differences in alpha diversity, based on geographic location (east vs. central Ghana), underlying vegetation type (cocoa vs. non-cocoa), or height above ground level (15-23 m vs. 25-50 m), and despite the short flight times (10-15 min), ~90 operational taxonomic units (OTUs) were identified in each sample. In Ghanaian air, fungal assemblages were skewed at the phylum taxonomic level towards the ascomycetes (53.7%) as opposed to basidiomycetes (24.6%); at the class level, the Dothideomycetes were predominant (29.8%) followed by the Agaricomycetes (21.8%). The most common fungal genus in Ghanaian air was cosmopolitan and globally ubiquitous Cladosporium (9.9% of reads). Interestingly, many fungal genera containing economically important phytopathogens of tropical crops were also identified in Ghanaian air, including Corynespora, Fusarium, and Lasiodiplodia. Consequently, a novel loop-mediated isothermal amplification (LAMP) assay, based on translation elongation factor-1α sequences, was developed and tested for rapid, sensitive, and specific detection of the fungal phytopathogenic genus Lasiodiplodia. Potential applications for improved tropical disease management are considered.

Label-free separation of peripheral blood mononuclear cells from whole blood by gradient acoustic focusing

Efficient techniques for separating target cells from undiluted blood are necessary for various diagnostic and research applications. This paper presents acoustic focusing in dense media containing iodixanol to purify peripheral blood mononuclear cells (PBMCs) from whole blood in a label-free and flow-through format. If the blood is laminated or mixed with iodixanol solutions while passing through the resonant microchannel, all the components (fluids and cells) rearrange according to their acoustic impedances. Red blood cells (RBCs) have higher effective acoustic impedance than PBMCs. Therefore, they relocate to the pressure node despite the dense medium, while PBMCs stay near the channel walls due to their negative contrast factor relative to their surrounding medium. By modifying the medium and thus tuning the contrast factor of the cells, we enriched PBMCs relative to RBCs by a factor of 3600 to 11,000 and with a separation efficiency of 85%. That level of RBC depletion is higher than most other microfluidic methods and similar to that of density gradient centrifugation. The current acoustophoretic chip runs up to 20 µl/min undiluted whole blood and can be integrated with downstream analysis.

Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed).

Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred. We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations. Our algorithms provide a biologically assumptionfree approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

Comprehensive Assessment of the Intrinsic Pancreatic Microbiome.

We sought to comprehensively profile tissue and cyst fluid in patients with benign, precancerous, and cancerous conditions of the pancreas to characterize the intrinsic pancreatic microbiome. Small studies in pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasm (IPMN) have suggested that intra-pancreatic microbial dysbiosis may drive malignant transformation. Pancreatic samples were collected at the time of resection from 109 patients. Samples included tumor tissue (control, n=20; IPMN, n=20; PDAC, n=19) and pancreatic cyst fluid (IPMN, n=30; SCA, n=10; MCN, n=10). Assessment of bacterial DNA by quantitative PCR and 16S ribosomal RNA gene sequencing was performed. Downstream analyses determined the relative abundances of individual taxa between groups and compared intergroup diversity. Whole-genome sequencing data from 140 patients with PDAC in the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) were analyzed to validate findings. Sequencing of pancreatic tissue yielded few microbial reads regardless of diagnosis, and analysis of pancreatic tissue showed no difference in the abundance and composition of bacterial taxa between normal pancreas, IPMN, or PDAC groups. Low-grade dysplasia (LGD) and high-grade dysplasia (HGD) IPMN were characterized by low bacterial abundances with no difference in tissue composition and a slight increase in Pseudomonas and Sediminibacterium in HGD cyst fluid. Decontamination analysis using the CPTAC database confirmed a low-biomass, low-diversity intrinsic pancreatic microbiome that did not differ by pathology. Our analysis of the pancreatic microbiome demonstrated very low intrinsic biomass that is relatively conserved across diverse neoplastic conditions and thus unlikely to drive malignant transformation.

Risk Assessment of Mamak Dam Based on The ICOLD Modification Method and Risk Index Method

The Mamak Dam has the risk of damage and the need for evacuation at an extreme downstream level. Risk considerations are based on the potential natural disasters of the dam, the impact of dam failure, design loads, and safety coefficients. The Risk Index method results in a score with parameters such as spillway capacity adequacy, safety factor adequacy against slopes, disaster factor (hazard), and all parameters analyzed to determine the Dam Safety Index (Idam). The drawback of the Risk Index method is neglecting downstream failure analysis. The ICOLD modification method for parameters is the same as the Risk Index method, but the ICOLD method considers downstream analysis. The modification involves adding parameters, such as the condition of the dam slope cover layer, based on the Mamak Dam Large Inspection results. In the Risk Index method, the Idam calculation value is 262.5. The total risk index value for Mamak Dam is 51.73, with a dam safety value of 92.337. In the ICOLD method, the Idam calculation value is 245, with a total risk index value for Mamak Dam of 48.28 and a Dam Safety value of 92.848. Meanwhile, using the Modified ICOLD method, the Idam calculation value is 245, with a total risk index value for Mamak Dam of 49.93 and a Dam Safety value of 92.603. Based on these results, the Dam Safety value with the Modified ICOLD method has decreased but still falls within the satisfactory classification (&gt;75). Therefore, it can be concluded that Mamak Dam is currently at a safe level against normal loads and extraordinary loads (design floods and design earthquakes).

Abstract P29: GSTT2 AND THE RESPONSE TO BCG IMMUNOTHERAPY

Abstract Whilst M. bovis Bacillus Calmette-Guérin (BCG) therapy remains the gold-standard for treatment of high-risk non-muscle invasive bladder cancer (BC), 30-40% of patients fail therapy, resulting in disease recurrence and progression. A better understanding of the underlying mechanisms of BCG therapy can aid in revealing key response pathways, which may lead to the discovery of new targets, and in turn new approaches to treat BC. The glutathione-S-transferase theta 2 (GSTT2) gene is a member of the GST family. Loss of GSTT2 expression has been associated with modulation of intracellular ROS and BCG survival in BC cell lines. Retrospective analysis of BC patients revealed that patients with the GSTT2B deletion responded better to fewer instillations of BCG. To understand these responses, wild-type (WT) and GSTT2-knockout (KO) mice were implanted with MB49-PSA BC cells, in subcutaneous and orthotopic settings. Once tumors were established, the mice were treated with four weekly BCG instillations, after which the subcutaneous tumors and bladders were harvested for downstream analysis. Single-cell RNA sequencing of whole-bladders was used to identify specific cell clusters within the bladder, and to analyze differential gene expression. Hmga2, Ahnak and Peak1 were found to be differentially expressed in bladders from WT and GSTT2-KO mice. These genes are known to be involved in pro- and anti-tumorigenic pathways such as epithelial-mesenchymal transition and inflammation. Additionally, quantitative real-time PCR analysis in the subcutaneous and orthotopic tumors revealed differences in the expression of immune-related genes, especially markers of immune exhaustion such as PD-L1 and CTLA-4. Flow cytometry analysis of subcutaneous tumors also revealed differences in T-cell infiltration. Thus, loss of GSTT2 may influence the response to BCG therapy, through modulating downstream signaling pathways and immune responses. Elucidating such pathways can lead to a better understanding of the mechanism of BCG and its role as a therapy for BC. Citation Format: Mugdha Patwardhan, Kane Toh, Edmund Chiong, Ratha Mahendran. GSTT2 AND THE RESPONSE TO BCG IMMUNOTHERAPY [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P29.

Genetic insight into a polygenic trait using a novel genome-wide association approach in a wild amphibian population.

Body size variation is central in the evolution of life-history traits in amphibians, but the underlying genetic architecture of this complex trait is still largely unknown. Herein, we studied the genetic basis of body size and fecundity of the alternative morphotypes in a wild population of the Greek smooth newt (Lissotriton graecus). By combining a genome-wide association approach with linkage disequilibrium network analysis, we were able to identify clusters of highly correlated loci thus maximizing sequence data for downstream analysis. The putatively associated variants explained 12.8% to 44.5% of the total phenotypic variation in body size and were mapped to genes with functional roles in the regulation of gene expression and cell cycle processes. Our study is the first to provide insights into the genetic basis of complex traits in newts and provides a useful tool to identify loci potentially involved in fitness-related traits in small data sets from natural populations in non-model species.

An Optimized Genotyping Workflow for Identifying Highly SCRaMbLEd Synthetic Yeasts.

Synthetic Sc2.0 yeast strains contain hundreds to thousands of loxPsym recombination sites that allow restructuring of the Saccharomyces cerevisiae genome by SCRaMbLE. Thus, a highly diverse yeast population can arise from a single genotype. The selection of genetically diverse candidates with rearranged synthetic chromosomes for downstream analysis requires an efficient and straightforward workflow. Here we present loxTags, a set of qPCR primers for genotyping across loxPsym sites to detect not only deletions but also inversions and translocations after SCRaMbLE. To cope with the large number of amplicons, we generated qTagGer, a qPCR genotyping primer prediction tool. Using loxTag-based genotyping and long-read sequencing, we show that light-inducible Cre recombinase L-SCRaMbLE can efficiently generate diverse recombination events when applied to Sc2.0 strains containing a linear or a circular version of synthetic chromosome III.

Strategies for enhancing automatic fixation detection in head-mounted eye tracking.

Moving through a dynamic world, humans need to intermittently stabilize gaze targets on their retina to process visual information. Overt attention being thus split into discrete intervals, the automatic detection of such fixation events is paramount to downstream analysis in many eye-tracking studies. Standard algorithms tackle this challenge in the limiting case of little to no head motion. In this static scenario, which is approximately realized for most remote eye-tracking systems, it amounts to detecting periods of relative eye stillness. In contrast, head-mounted eye trackers allow for experiments with subjects moving naturally in everyday environments. Detecting fixations in these dynamic scenarios is more challenging, since gaze-stabilizing eye movements need to be reliably distinguished from non-fixational gaze shifts. Here, we propose several strategies for enhancing existing algorithms developed for fixation detection in the static case to allow for robust fixation detection in dynamic real-world scenarios recorded with head-mounted eye trackers. Specifically, we consider (i) an optic-flow-based compensation stage explicitly accounting for stabilizing eye movements during head motion, (ii) an adaptive adjustment of algorithm sensitivity according to head-motion intensity, and (iii) a coherent tuning of all algorithm parameters. Introducing a new hand-labeled dataset, recorded with the Pupil Invisible glasses by Pupil Labs, we investigate their individual contributions. The dataset comprises both static and dynamic scenarios and is made publicly available. We show that a combination of all proposed strategies improves standard thresholding algorithms and outperforms previous approaches to fixation detection in head-mounted eye tracking.

Rusa deer microbiota: the importance of preliminary data analysis for meaningful diversity comparisons.

The microbiome is an important consideration for the conservation of endangered species. Studies provided evidence of the effect of behavior and habitat change on the microbiota of wild animals and reported various inferences. It indicates the complexity of factors influencing microbiota diversity, including incomplete sampling procedures. Data abnormality may arise due to the procedures warranting preliminary analysis, such as rarefaction, before downstream analysis. This present study demonstrated the effect of data rarefaction and aggregation on the comparison of wild rusa deer's gut microbial diversity. Eighty-five feces samples were collected from 11 deer populations inhabiting three national parks in Java and Bali islands. Using the Illumina Nova-Seq platform, fragments of 16s rRNA gene were sequenced, and raw data of 51,389 reads corresponding to 2 domains, 22 phyla, 45 classes, 83 orders, 182 families, and 460 genera of bacteria were obtained. Data rarefaction was applied at two different library sizes (minimum and fixed) and aggregation (11 populations into 3 research sites) to investigate its effect on the microbial diversity comparison. There are significant differences in alpha diversity between populations, but not research sites, at all library sizes of rarefaction. A similar finding is also found in beta diversity. Moreover, data rarefaction and aggregation result in different values of the diversity metrics. This present study shows that statistical analysis remains a substantial concern in microbiome studies applied to conservation biology. It suggests reporting a more detailed data normalization in microbiome studies as an inherent control of suboptimal sampling, particularly when involving feces.

WAS IT A MATch I SAW? Approximate palindromes lead to overstated false match rates in benchmarks using reversed sequences.

Software for labeling biological sequences typically produces a theory-based statistic for each match (the E-value) that indicates the likelihood of seeing that match's score by chance. E-values accurately predict false match rate for comparisons of random (shuffled) sequences, and thus provide a reasoned mechanism for setting score thresholds that enable high sensitivity with low expected false match rate. This threshold-setting strategy is challenged by real biological sequences, which contain regions of local repetition and low sequence complexity that cause excess matches between non-homologous sequences. Knowing this, tool developers often develop benchmarks that use realistic-seeming decoy sequences to explore empirical tradeoffs between sensitivity and false match rate. A recent trend has been to employ reversed biological sequences as realistic decoys, because these preserve the distribution of letters and the existence of local repeats, while disrupting the original sequence's functional properties. However, we and others have observed that sequences appear to produce high scoring alignments to their reversals with surprising frequency, leading to overstatement of false match risk that may negatively affect downstream analysis. We demonstrate that an alignment between a sequence S and its (possibly mutated) reversal tends to produce higher scores than alignment between truly unrelated sequences, even when S is a shuffled string with no notable repetitive or low-complexity regions. This phenomenon is due to the unintuitive fact that (even randomly shuffled) sequences contain palindromes that are on average longer than the longest common substrings (LCS) shared between permuted variants of the same sequence. Though the expected palindrome length is only slightly larger than the expected LCS, the distribution of alignment scores involving reversed sequences is strongly right-shifted, leading to greatly increased frequency of high-scoring alignments to reversed sequences. Overestimates of false match risk can motivate unnecessarily high score thresholds, leading to potentially reduced true match sensitivity. Also, when tool sensitivity is only reported up to the score of the first matched decoy sequence, a large decoy set consisting of reversed sequences can obscure sensitivity differences between tools. As a result of these observations, we advise that reversed biological sequences be used as decoys only when care is taken to remove positive matches in the original (un-reversed) sequences, or when overstatement of false labeling is not a concern. Though the primary focus of the analysis is on sequence annotation, we also demonstrate that the prevalence of internal palindromes may lead to an overstatement of the rate of false labels in protein identification with mass spectrometry.

Variations in antimicrobial resistance genes present in the rectal faeces of seals in Scottish and Liverpool Bay coastal waters

Antibiotic resistance genes originating from human activity are considered important environmental pollutants. Wildlife species can act as sentinels for coastal environmental contamination and in this study we used qPCR array technology to investigate the variety and abundance of antimicrobial resistance genes (ARGs), mobile genetic elements (MGEs) and integrons circulating within seal populations both near to and far from large human populations located around the Scottish and northwest English coast. Rectal swabs were taken from 50 live grey seals and nine live harbour seals. Nucleic acids were stabilised upon collection, enabling extraction of sufficient quality and quantity DNA for downstream analysis. 78 ARG targets, including genes of clinical significance, four MGE targets and three integron targets were used to monitor genes within 22 sample pools. 30 ARGs were detected, as well as the integrons intl1 and intl2 and tnpA transposase. Four β-lactam, nine tetracycline, two phenicol, one trimethoprim, three aminoglycoside and ten multidrug resistance genes were detected as well as mcr-1 which confers resistance to colistin, an important drug of last resort. No sulphonamide, vancomycin, macrolide, lincosamide or streptogramin B (MLSB) resistance genes were detected. Resistance genes were detected in all sites but the highest number of ARGs (n = 29) was detected in samples derived from grey seals on the Isle of May, Scotland during the breeding season, and these genes also had the highest average abundance in relation to the 16S rRNA gene. This pilot study demonstrates the effectiveness of a culture-independent workflow for global analysis of ARGs within the microbiota of live, free-ranging, wild animals from habitats close to and remote from human habitation, and highlights seals as a valuable indicator species for monitoring the presence, abundance and land-sea transference of resistance genes within and between ecosystems.

Single-cell profiling reveals transcriptome dynamics during bovine oocyte growth

BackgroundMammalian follicle development is characterized by extensive changes in morphology, endocrine responsiveness, and function, providing the optimum environment for oocyte growth, development, and resumption of meiosis. In cattle, the first signs of transcription activation in the oocyte are observed in the secondary follicle, later than during mouse and human oogenesis. While many studies have generated extensive datasets characterizing gene expression in bovine oocytes, they are mostly limited to the analysis of fully grown and matured oocytes. The aim of the present study was to apply single-cell RNA sequencing to interrogate the transcriptome of the growing bovine oocyte from the secondary follicle stage through to the mid-antral follicle stage.ResultsSingle-cell RNA-seq libraries were generated from oocytes of known diameters (< 60 to > 120 μm), and datasets were binned into non-overlapping size groups for downstream analysis. Combining the results of weighted gene co-expression network and Trendy analyses, and differently expressed genes (DEGs) between size groups, we identified a decrease in oxidative phosphorylation and an increase in maternal -genes and transcription regulators across the bovine oocyte growth phase. In addition, around 5,000 genes did not change in expression, revealing a cohort of stable genes. An interesting switch in gene expression profile was noted in oocytes greater than 100 μm in diameter, when the expression of genes related to cytoplasmic activities was replaced by genes related to nuclear activities (e.g., chromosome segregation). The highest number of DEGs were detected in the comparison of oocytes 100–109 versus 110–119 μm in diameter, revealing a profound change in the molecular profile of oocytes at the end of their growth phase.ConclusionsThe current study provides a unique dataset of the key genes and pathways characteristic of each stage of oocyte development, contributing an important resource for a greater understanding of bovine oogenesis.