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BACKGROUND: RNA sequencing (RNA-Seq) offers profound insights into the complex transcriptomes of diverse biological systems. However, standard differential expression analysis pipelines based on DESeq2 and edgeR encounter challenges when applied to the immediate early transcriptomes of Chlamydia spp., obligate intracellular bacteria. These challenges arise from their reliance on assumptions that do not hold in scenarios characterized by extensive transcriptomic activation and limited repression. RESULTS: Standard analyses using unique chlamydial RNA-Seq reads alone identify nearly 300 upregulated and about 300 downregulated genes, significantly deviating from actual RNA-Seq read trends. By incorporating both chlamydial and host reads or adjusting for total sequencing depth, the revised normalization methods each detected over 700 upregulated genes and 30 or fewer downregulated genes, closely aligned with observed RNA-Seq data. Further validation through qRT-PCR analysis confirmed the effectiveness of these adjusted approaches in capturing the true extent of transcriptomic activation during the immediate early phase of chlamydial infection. CONCLUSIONS: This study highlights the limitations of standard RNA-Seq analysis tools in scenarios with extensive transcriptomic activation, such as in Chlamydia spp. during early infection. Our revised normalization methods, incorporating host reads or total sequencing depth, provide a more accurate representation of gene expression dynamics. These approaches may inform similar adjustments in other systems with unbalanced gene expression dynamics, enhancing the accuracy of transcriptomic analysis.

Inferring gene regulatory networks from single-cell RNA-sequencing trajectories has been an active area of research yet methods are still needed to identify regulators governing cell transitions. We developed DREAMIT (Dynamic Regulation of Expression Across Modules in Inferred Trajectories) to annotate transcription-factor activity along single-cell trajectory branches, using ensembles of relations to target genes. Using a benchmark representing several different tissues, as well as external validation with ATAC-Seq and Perturb-Seq data on hematopoietic cells, the method was found to have higher tissue-specific sensitivity and specificity over competing approaches.

Next-generation sequencing (NGS) has proven clinical utility on disease management and serves as an important tool for genomic surveillance. Currently, hurdles surrounding its implementation, namely the complex and demanding analytical workflows, have impeded its widespread use in many laboratories. To address this challenge, the UCLA Molecular Microbiology and Pathogen Genomics Laboratory evaluated the performance of the Tecan MagicPrep NGS system, a commercial automated solution for library preparation for clinical whole-genome sequencing assays, against the Illumina Nextera DNA Flex Library Prep. Using 35 unique organisms (28 bacteria and 7 fungi) for various clinical applications, including microbial identification and genomic characterization, we compared the quantity and quality of the prepared libraries and the resulting sequences, and concordance of the overall results. We also assessed the impact of its implementation on laboratory workflow. The MagicPrep NGS produced higher library concentrations with smaller sizes, and correspondingly, higher molarity. Quality metrics of the sequences, however, demonstrated no significant impact on the overall results, producing 100% concordance with the reference method. Importantly, workflow analysis showed 5 hours less hands-on time per run with more flexibility. This evaluation study indicates that performance of the MagicPrep NGS is comparable to the Nextera DNA Flex with the added benefit of improving workflow efficiency and reducing labor for performing routine clinical microbial whole-genome sequencing tests.

Merging diverse single-cell RNA sequencing (scRNA-seq) data from numerous experiments, laboratories and technologies can uncover important biological insights. Nonetheless, integrating scRNA-seq data encounters special challenges when the datasets are composed of diverse cell type compositions. Scanorama offers a robust solution for improving the quality and interpretation of heterogeneous scRNA-seq data by effectively merging information from diverse sources. Scanorama is designed to address the technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of multiple scRNA-seq datasets. Here we provide a detailed protocol for using Scanorama within a Scanpy-based single-cell analysis workflow coupled with Google Colaboratory, a cloud-based free Jupyter notebook environment service. The protocol involves Scanorama integration, a process that typically spans 0.5-3 h. Scanorama integration requires a basic understanding of cellular biology, transcriptomic technologies and bioinformatics. Our protocol and new Scanorama-Colaboratory resource should make scRNA-seq integration more widely accessible to researchers.

INTRODUCTION: Early neoplastic progression of Barretts esophagus (BE) is often treated with endoscopic therapy. Although effective, some patients are refractory to therapy or recur after apparent eradication of the BE. The goal of this study was to determine whether genomic alterations within the treated BE may be associated with persistent or recurrent disease. METHODS: We performed DNA sequencing on pre-treatment esophageal samples from 45 patients who were successfully treated by endoscopic therapy and did not recur as well as pre-treatment and post-treatment samples from 40 patients who had persistent neoplasia and 21 patients who had recurrent neoplasia. The genomic alterations were compared between groups. RESULTS: The genomic landscape was similar between all groups. Patients with persistent disease were more likely to have pre-treatment alterations involving the receptor tyrosine kinase pathway ( P = 0.01), amplifications of oncogenes ( P = 0.01), and deletions of tumor suppressor genes ( P = 0.02). These associations were no longer significant after adjusting for patient age and BE length. More than half of patients with persistent (52.5%) or recurrent (57.2%) disease showed pre-treatment and post-treatment samples that shared at least 50% of their driver mutations. DISCUSSION: Pre-treatment samples were genomically similar between those who responded to endoscopic therapy and those who had persistent or recurrent disease, suggesting there is not a strong genomic component to treatment response. Although it was expected to find shared driver mutations in pre-treatment and post-treatment samples in patients with persistent disease, the finding that an equal number of patients with recurrent disease also showed this relation suggests that many recurrences represent undetected minimal residual disease.

Schizophyllum commune is a mushroom-forming fungus notable for its distinctive fruiting bodies with split gills. It is used as a model organism to study mushroom development, lignocellulose degradation and mating type loci. It is a hypervariable species with considerable genetic and phenotypic diversity between the strains. In this study, we systematically phenotyped 16 dikaryotic strains for aspects of mushroom development and 18 monokaryotic strains for lignocellulose degradation. There was considerable heterogeneity among the strains regarding these phenotypes. The majority of the strains developed mushrooms with varying morphologies, although some strains only grew vegetatively under the tested conditions. Growth on various carbon sources showed strain-specific profiles. The genomes of seven monokaryotic strains were sequenced and analyzed together with six previously published genome sequences. Moreover, the related species Schizophyllum fasciatum was sequenced. Although there was considerable genetic variation between the genome assemblies, the genes related to mushroom formation and lignocellulose degradation were well conserved. These sequenced genomes, in combination with the high phenotypic diversity, will provide a solid basis for functional genomics analyses of the strains of S. commune.

OBJECTIVES: Anal cancer risk is elevated in MSM with HIV (MSMWH). Anal high-risk human papillomavirus (hr-HPV) infection is necessary but insufficient to develop high-grade squamous intraepithelial lesion (HSIL), the anal cancer precursor, suggesting additional factors. We sought to determine whether the microbiome of the anal canal is distinct by comparing it with the microbiome of stool. We also sought to determine whether changes in the anal microbiome are associated with HSIL among MSMWH. DESIGN: Cross-sectional comparison of the microbiome of the anal canal with the microbiome of stool in MSMWH and cross-sectional comparison of the anal microbiome of MSMWH with anal HSIL with the anal microbiome of MSMWH without anal HSIL. METHODS: Sterile swabs were used to sample the anus of MSMWH for microbiome and HPV testing, followed by high-resolution anoscopy. Stool samples were mailed from home. 16S sequencing was used for bacterial identification. Measures of alpha diversity, beta diversity, and differential abundance analysis were used to compare samples. RESULTS: One hundred sixty-six anal samples and 103 matching stool samples were sequenced. Beta diversity showed clustering of stool and anal samples. Of hr-HPV-positive MSMWH, 31 had HSIL and 13 had no SIL. Comparison of the microbiome between these revealed 28 different species. The highest-fold enrichment among MSMWH/hr-HPV/HSIL included pro-inflammatory and carcinogenic Prevotella, Parasuterella, Hungatella, Sneathia, and Fusobacterium species. The anti-inflammatory Anaerostipes caccae showed the greatest reduction among MSMWH/hr-HPV/HSIL. CONCLUSION: The anal microbiome is distinct from stool. A pro-inflammatory and carcinogenic environment may be associated with anal HSIL.

Recently, we reported the discovery of a novel endoglucanase of the glycoside hydrolase family 12 (GH12), designated IfCelS12A, from the haloalkaliphilic anaerobic bacterium Iocasia fonsfrigidae strain SP3-1, which was isolated from a hypersaline pond in the Samut Sakhon province of Thailand (ca. 2017). IfCelS12A exhibits high substrate specificity on carboxymethyl cellulose and amorphous cellulose but low substrate specificity on b-1,3;1,4-glucan. Unlike some endoglucanases of the GH12 family, IfCelS12A does not exhibit hydrolytic activity on crystalline cellulose (i.e., Avicel™). High-Pressure Liquid Chromatography (HPLC) and Thin Layer Chromatography (TLC) analyses of products resulting from IfCelS12-mediated hydrolysis indicate mode of action for this enzyme. Notably, IfCelS12A preferentially hydrolyzes cellotetraoses, cellopentaoses, and cellohexaoses with negligible activity on cellobiose or cellotriose. Kinetic analysis with cellopentaose and barely b-D-glucan as cellulosic substrates were conducted. On cellopentaose, IfCelS12A demonstrates a 16-fold increase in activity (KM = 0.27 mM; kcat = 0.36 s-1; kcat/KM = 1.34 mM-1 s-1) compared to the enzymatic hydrolysis of barley b-D-glucan (KM: 0.04 mM, kcat: 0.51 s-1, kcat/KM = 0.08 mM-1 s-1). Moreover, IfCelS12A enzymatic efficacy is stable in hypersaline sodium chlorids (NaCl) solutions (up to 10% NaCl). Specifically, IfCel12A retains notable activity after 24 h at 2M NaCl (10% saline solution). IfCelS12A used as a cocktail component with other cellulolytic enzymes and in conjunction with mobile sequestration platform technology offers additional options for deconstruction of ionic liquid-pretreated cellulosic feedstock. KEY POINTS: • IfCelS12A from an anaerobic alkaliphile Iocasia fronsfrigidae shows salt tolerance • IfCelS12A in cocktails with other enzymes efficiently degrades cellulosic biomass • IfCelS12A used with mobile enzyme sequestration platforms enhances hydrolysis.

The jaguar (Panthera onca) is the largest living cat species native to the Americas and one of few large American carnivorans to have survived into the Holocene. However, the extent to which jaguar diversity declined during the end-Pleistocene extinction event remains unclear. For example, Pleistocene jaguar fossils from North America are notably larger than the average extant jaguar, leading to hypotheses that jaguars from this continent represent a now-extinct subspecies (Panthera onca augusta) or species (Panthera augusta). Here, we used a hybridization capture approach to recover an ancient mitochondrial genome from a large, late Pleistocene jaguar from Kingston Saltpeter Cave, Georgia, United States, which we sequenced to 26-fold coverage. We then estimated the evolutionary relationship between the ancient jaguar mitogenome and those from other extinct and living large felids, including multiple jaguars sampled across the species' current range. The ancient mitogenome falls within the diversity of living jaguars. All sampled jaguar mitogenomes share a common mitochondrial ancestor ~400 thousand years ago, indicating that the lineage represented by the ancient specimen dispersed into North America from the south at least once during the late Pleistocene. While genomic data from additional and older specimens will continue to improve understanding of Pleistocene jaguar diversity in the Americas, our results suggest that this specimen falls within the variation of extant jaguars despite the relatively larger size and geographic location and does not represent a distinct taxon.

Tandem repeats are frequent across the human genome, and variation in repeat length has been linked to a variety of traits. Recent improvements in long read sequencing technologies have the potential to greatly improve tandem repeat analysis, especially for long or complex repeats. Here, we introduce LongTR, which accurately genotypes tandem repeats from high-fidelity long reads available from both PacBio and Oxford Nanopore Technologies. LongTR is freely available at https://github.com/gymrek-lab/longtr and https://zenodo.org/doi/10.5281/zenodo.11403979 .

MOTIVATION: The alignment of sequencing reads is a critical step in the characterization of ancient genomes. However, reference bias and spurious mappings pose a significant challenge, particularly as cutting-edge wet lab methods generate datasets that push the boundaries of alignment tools. Reference bias occurs when reference alleles are favoured over alternative alleles during mapping, whereas spurious mappings stem from either contamination or when endogenous reads fail to align to their correct position. Previous work has shown that these phenomena are correlated with read length but a more thorough investigation of reference bias and spurious mappings for ancient DNA has been lacking. Here, we use a range of empirical and simulated palaeogenomic datasets to investigate the impacts of mapping tools, quality thresholds, and reference genome on mismatch rates across read lengths. RESULTS: For these analyses, we introduce AMBER, a new bioinformatics tool for assessing the quality of ancient DNA mapping directly from BAM-files and informing on reference bias, read length cut-offs and reference selection. AMBER rapidly and simultaneously computes the sequence read mapping bias in the form of the mismatch rates per read length, cytosine deamination profiles at both CpG and non-CpG sites, fragment length distributions, and genomic breadth and depth of coverage. Using AMBER, we find that mapping algorithms and quality threshold choices dictate reference bias and rates of spurious alignment at different read lengths in a predictable manner, suggesting that optimized mapping parameters for each read length will be a key step in alleviating reference bias and spurious mappings. AVAILABILITY AND IMPLEMENTATION: AMBER is available for noncommercial use on GitHub (https://github.com/tvandervalk/AMBER.git). Scripts used to generate and analyse simulated datasets are available on Github (https://github.com/sdolenz/refbias_scripts).

Whole-genome bisulfite sequencing (BS-Seq) measures cytosine methylation changes at single-base resolution and can be used to profile cell-free DNA (cfDNA). In plasma, ultrashort single-stranded cfDNA (uscfDNA, ∼50 nt) has been identified together with 167 bp double-stranded mononucleosomal cell-free DNA (mncfDNA). However, the methylation profile of uscfDNA has not been described. Conventional BS-Seq workflows may not be helpful because bisulfite conversion degrades larger DNA into smaller fragments, leading to erroneous categorization as uscfDNA. We describe the '5mCAdpBS-Seq' workflow in which pre-methylated 5mC (5-methylcytosine) single-stranded adapters are ligated to heat-denatured cfDNA before bisulfite conversion. This method retains only DNA fragments that are unaltered by bisulfite treatment, resulting in less biased uscfDNA methylation analysis. Using 5mCAdpBS-Seq, uscfDNA had lower levels of DNA methylation (∼15%) compared to mncfDNA and was enriched in promoters and CpG islands. Hypomethylated uscfDNA fragments were enriched in upstream transcription start sites (TSSs), and the intensity of enrichment was correlated with expressed genes of hemopoietic cells. Using tissue-of-origin deconvolution, we inferred that uscfDNA is derived primarily from eosinophils, neutrophils, and monocytes. As proof-of-principle, we show that characteristics of the methylation profile of uscfDNA can distinguish non-small cell lung carcinoma from non-cancer samples. The 5mCAdpBS-Seq workflow is recommended for any cfDNA methylation-based investigations.

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Tuberculosis (TB) remains a leading cause of death from an infectious disease worldwide, partly due to a lack of effective strategies to screen and triage individuals with potential TB. Whole blood RNA signatures have been tested as biomarkers for TB, but have failed to meet the World Health Organizations (WHO) optimal target product profiles (TPP). Here, we use RNA sequencing and machine-learning to investigate the utility of plasma cell-free RNA (cfRNA) as a host-response biomarker for TB in cohorts from Uganda, Vietnam and Philippines. We report a 6-gene cfRNA signature, which differentiates TB-positive and TB-negative individuals with AUC = 0.95, 0.92, and 0.95 in test, training and validation, respectively. This signature meets WHO TPPs (sensitivity: 97.1% [95% CI: 80.9-100%], specificity: 85.2% [95% CI: 72.4-100%]) regardless of geographic location, sample collection method and HIV status. Overall, our results identify plasma cfRNA as a promising host response biomarker to diagnose TB.

MOTIVATION: Phylogenetic placement of a query sequence on a backbone tree is increasingly used across biomedical sciences to identify the content of a sample from its DNA content. The accuracy of such analyses depends on the density of the backbone tree, making it crucial that placement methods scale to very large trees. Moreover, a new paradigm has been recently proposed to place sequences on the species tree using single-gene data. The goal is to better characterize the samples and to enable combined analyses of marker-gene (e.g., 16S rRNA gene amplicon) and genome-wide data. The recent method DEPP enables performing such analyses using metric learning. However, metric learning is hampered by a need to compute and save a quadratically growing matrix of pairwise distances during training. Thus, the training phase of DEPP does not scale to more than roughly 10 000 backbone species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331 270 species. RESULTS: This paper explores divide-and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP. While divide-and-conquer has been extensively used in phylogenetics, applying divide-and-conquer to data-hungry machine-learning methods needs nuance. C-DEPP uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing 20 million 16S fragments on the GG2 reference tree in 41 h of computation. AVAILABILITY AND IMPLEMENTATION: The dataset and C-DEPP software are freely available at https://github.com/yueyujiang/dataset_cdepp/.

Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

Intermuscular adipose tissue (IMAT) is a relatively understudied adipose depot located between muscle fibers. IMAT content increases with age and BMI and is associated with metabolic and muscle degenerative diseases; however, an understanding of the biological properties of IMAT and its interplay with the surrounding muscle fibers is severely lacking. In recent years, single-cell and nuclei RNA sequencing have provided us with cell type-specific atlases of several human tissues. However, the cellular composition of human IMAT remains largely unexplored due to the inherent challenges of its accessibility from biopsy collection in humans. In addition to the limited amount of tissue collected, the processing of human IMAT is complicated due to its proximity to skeletal muscle tissue and fascia. The lipid-laden nature of the adipocytes makes it incompatible with single-cell isolation. Hence, single nuclei RNA sequencing is optimal for obtaining high-dimensional transcriptomics at single-cell resolution and provides the potential to uncover the biology of this depot, including the exact cellular composition of IMAT. Here, we present a detailed protocol for nuclei isolation and library preparation of frozen human IMAT for single nuclei RNA sequencing. This protocol allows for the profiling of thousands of nuclei using a droplet-based approach, thus providing the capacity to detect rare and low-abundant cell types.

In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, there lacks specific evaluation of correction efficacy for varying contamination levels. Here, we show that DecontX and CellBender under-correct highly contaminating genes, while SoupX and scAR over-correct lowly/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly contaminating genes while avoiding over-correction of other genes.

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, SARS-CoV-2 variants capable of breakthrough infections have attracted global attention. These variants have significant mutations in the receptor-binding domain (RBD) of the spike protein and the membrane (M) protein, which may imply an enhanced ability to evade immune responses. In this study, an examination of co-mutations within the spike RBD and their potential correlation with mutations in the M protein was conducted. The EVmutation method was utilized to analyze the distribution of the mutations to elucidate the relationship between the mutations in the spike RBD and the alterations in the M protein. Additionally, the Sequence-to-Sequence Transformer Model (S2STM) was employed to establish mapping between the amino acid sequences of the spike RBD and M proteins, offering a novel and efficient approach for streamlined sequence analysis and the exploration of their interrelationship. Certain mutations in the spike RBD, G339D-S373P-S375F and Q493R-Q498R-Y505, are associated with a heightened propensity for inducing mutations at specific sites within the M protein, especially sites 3 and 19/63. These results shed light on the concept of mutational synergy between the spike RBD and M proteins, illuminating a potential mechanism that could be driving the evolution of SARS-CoV-2.

UNLABELLED: A comprehensive microbial surveillance was conducted at NASAs Mars 2020 spacecraft assembly facility (SAF), where whole-genome sequencing (WGS) of 110 bacterial strains was performed. One isolate, designated 179-BFC-A-HST, exhibited less than 80% average nucleotide identity (ANI) to known species, suggesting a novel organism. This strain demonstrated high-level resistance [minimum inhibitory concentration (MIC) >256 mg/L] to third-generation cephalosporins, including ceftazidime, cefpodoxime, combination ceftazidime/avibactam, and the fourth-generation cephalosporin cefepime. The results of a comparative genomic analysis revealed that 179-BFC-A-HST is most closely related to Virgibacillus halophilus 5B73CT, sharing an ANI of 78.7% and a digital DNA-DNA hybridization (dDDH) value of 23.5%, while their 16S rRNA gene sequences shared 97.7% nucleotide identity. Based on these results and the recent recognition that the genus Virgibacillus is polyphyletic, strain 179-BFC-A-HST is proposed as a novel species of a novel genus, Tigheibacillus jepli gen. nov., sp. nov (type strain 179-BFC-A-HST = DSM 115946T = NRRL B-65666T), and its closest neighbor, V. halophilus, is proposed to be reassigned to this genus as Tigheibacillus halophilus comb. nov. (type strain 5B73CT = DSM 21623T = JCM 21758T = KCTC 13935T). It was also necessary to reclassify its second closest neighbor Virgibacillus soli, as a member of a novel genus Paracerasibacillus, reflecting its phylogenetic position relative to the genus Cerasibacillus, for which we propose Paracerasibacillus soli comb. nov. (type strain CC-YMP-6T = DSM 22952T = CCM 7714T). Within Amphibacillaceae (n = 64), P. soli exhibited 11 antibiotic resistance genes (ARG), while T. jepli encoded for 3, lacking any known β-lactamases, suggesting resistance from variant penicillin-binding proteins, disrupting cephalosporin efficacy. P. soli was highly resistant to azithromycin (MIC >64 mg/L) yet susceptible to cephalosporins and penicillins. IMPORTANCE: The significance of this research extends to understanding microbial survival and adaptation in oligotrophic environments, such as those found in SAF. Whole-genome sequencing of several strains isolated from Mars 2020 mission assembly cleanroom facilities, including the discovery of the novel species Tigheibacillus jepli, highlights the resilience and antimicrobial resistance (AMR) in clinically relevant antibiotic classes of microbes in nutrient-scarce settings. The study also redefines the taxonomic classifications within the Amphibacillaceae family, aligning genetic identities with phylogenetic data. Investigating ARG and virulence factors (VF) across these strains illuminates the microbial capability for resistance under resource-limited conditions while emphasizing the role of human-associated VF in microbial survival, informing sterilization practices and microbial management in similar oligotrophic settings beyond spacecraft assembly cleanrooms such as pharmaceutical and medical industry cleanrooms.

Transposable elements (TE) play critical roles in shaping genome evolution. Highly repetitive TE sequences are also a major source of assembly gaps making it difficult to fully understand the impact of these elements on host genomes. The increased capacity of long-read sequencing technologies to span highly repetitive regions promises to provide new insights into patterns of TE activity across diverse taxa. Here we report the generation of highly contiguous reference genomes using PacBio long-read and Omni-C technologies for three species of Passerellidae sparrow. We compared these assemblies to three chromosome-level sparrow assemblies and nine other sparrow assemblies generated using a variety of short- and long-read technologies. All long-read based assemblies were longer (range: 1.12 to 1.41 Gb) than short-read assemblies (0.91 to 1.08 Gb) and assembly length was strongly correlated with the amount of repeat content. Repeat content for Bell's sparrow (31.2% of genome) was the highest level ever reported within the order Passeriformes, which comprises over half of avian diversity. The highest levels of repeat content (79.2% to 93.7%) were found on the W chromosome relative to other regions of the genome. Finally, we show that proliferation of different TE classes varied even among species with similar levels of repeat content. These patterns support a dynamic model of TE expansion and contraction even in a clade where TEs were once thought to be fairly depauperate and static. Our work highlights how the resolution of difficult-to-assemble regions of the genome with new sequencing technologies promises to transform our understanding of avian genome evolution.

IntroductionLate-onset Alzheimer's disease (LOAD) has a strong genetic component. Participants in Long-Life Family Study (LLFS) exhibit delayed onset of dementia, offering a unique opportunity to investigate LOAD genetics.MethodsWe conducted a whole genome sequence analysis of 3475 LLFS members. Genetic associations were examined in six independent studies (N = 14,260) with a wide range of LOAD risk. Association analysis in a sub-sample of the LLFS cohort (N = 1739) evaluated the association of LOAD variants with beta amyloid (Aβ) levels.ResultsWe identified several single nucleotide polymorphisms (SNPs) in tight linkage disequilibrium within the MTUS2 gene associated with LOAD (rs73154407, p = 7.6 × 10-9). Association of MTUS2 variants with LOAD was observed in the five independent studies and was significantly stronger within high levels of Aβ42/40 ratio compared to lower amyloid.DiscussionMTUS2 encodes a microtubule associated protein implicated in the development and function of the nervous system, making it a plausible candidate to investigate LOAD biology.HighlightsLong-Life Family Study (LLFS) families may harbor late onset Alzheimer's dementia (LOAD) variants. LLFS whole genome sequence analysis identified MTUS2 gene variants associated with LOAD. The observed LLFS variants generalized to cohorts with wide range of LOAD risk. The association of MTUS2 with LOAD was stronger within high levels of beta amyloid. Our results provide evidence for MTUS2 gene as a novel LOAD candidate locus.

In the human genome, heterozygous sites refer to genomic positions with a different allele or nucleotide variant on the maternal and paternal chromosomes. Resolving these allelic differences by chromosomal copy, also known as phasing, is achievable on a short-read sequencer when using a library preparation method that captures long-range genomic information. TELL-Seq is a library preparation that captures long-range genomic information with the aid of molecular identifiers (barcodes). The same barcode is used to tag the reads derived from the same long DNA fragment within a range of up to 200 kilobases (kb), generating linked-reads. This strategy can be used to phase an entire genome. Here, we introduce a TELL-Seq protocol developed for targeted applications, enabling the phasing of enriched loci of varying sizes, purity levels, and heterozygosity. To validate this protocol, we phased 2-200 kb loci enriched with different methods: CRISPR/Cas9-mediated excision coupled with pulse-field electrophoresis for the longest fragments, CRISPR/Cas9-mediated protection from exonuclease digestion for mid-size fragments, and long PCR for the shortest fragments. All selected loci have known clinical relevance: BRCA1, BRCA2, MLH1, MSH2, MSH6, APC, PMS2, SCN5A-SCN10A, and PKI3CA. Collectively, the analyses show that TELL-Seq can accurately phase 2-200 kb targets using a short-read sequencer.

ObjectivesIndividuals increasingly experience delays or interruptions in schooling; we evaluate the association between these non-traditional education trajectories and mental health.MethodsUsing year-by-year education data for 7,501 National Longitudinal Survey of Youth 1979 participants, ages 14-48 (262,535 person-years of education data), we applied sequence analysis and a clustering algorithm to identify educational trajectory groups, incorporating both type and timing to credential. Linear regression models, adjusted for early-life confounders, evaluated relationships between educational trajectories and mental health component scores (MCS) from the 12-item short form instrument at age 50. We evaluated effect modification by race, gender, and race by gender.ResultsWe identified 24 distinct educational trajectories based on highest credential and educational timing. Compared to high school (HS) diplomas, < HS (beta=-3.41, 95%CI:-4.74,-2.07) and general educational development credentials (GEDs) predicted poorer MCS (beta=-2.07,95%CI:-3.16,-0.98). The following educational trajectories predicted better MCS: some college immediately after High School (beta=1.52, 95%CI:0.68,2.37), Associate degrees after long interruptions (beta=1.73, 95%CI:0.27,3.19), and graduate school soon after Bachelor's completion (beta=1.13, 95%CI:0.21,2.06). Compared to White men, Black women especially benefited from educational credentials higher than HS in predicting MCS.ConclusionsBoth type and timing of educational credential predicted mental health. Black women's mental higher especially benefited from higher educational credentials.

Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but advances in long-read sequencing are leading to widely available, high-quality phased assemblies. Constructing a pangenome graph directly from assemblies, as opposed to variant calls, leverages the graph's ability to represent variation at different scales. Here we present the Minigraph-Cactus pangenome pipeline, which creates pangenomes directly from whole-genome alignments, and demonstrate its ability to scale to 90 human haplotypes from the Human Pangenome Reference Consortium. The method builds graphs containing all forms of genetic variation while allowing use of current mapping and genotyping tools. We measure the effect of the quality and completeness of reference genomes used for analysis within the pangenomes and show that using the CHM13 reference from the Telomere-to-Telomere Consortium improves the accuracy of our methods. We also demonstrate construction of a Drosophila melanogaster pangenome.

MOTIVATION: Taxonomic classification of short reads and taxonomic profiling of metagenomic samples are well-studied yet challenging problems. The presence of species belonging to groups without close representation in a reference dataset is particularly challenging. While k-mer-based methods have performed well in terms of running time and accuracy, they tend to have reduced accuracy for such novel species. Thus, there is a growing need for methods that combine the scalability of k-mers with increased sensitivity. RESULTS: Here, we show that using locality-sensitive hashing (LSH) can increase the sensitivity of the k-mer-based search. Our method, which combines LSH with several heuristics techniques including soft lowest common ancestor labeling and voting, is more accurate than alternatives in both taxonomic classification of individual reads and abundance profiling. AVAILABILITY AND IMPLEMENTATION: CONSULT-II is implemented in C++, and the software, together with reference libraries, is publicly available on GitHub https://github.com/bo1929/CONSULT-II.

BACKGROUND: Pneumonia is a common cause of morbidity and mortality, yet a causative pathogen is identified in a minority of cases. Plasma microbial cell-free DNA sequencing may improve diagnostic yield in immunocompromised patients with pneumonia. METHODS: In this prospective, multicenter, observational study of immunocompromised adults undergoing bronchoscopy to establish a pneumonia etiology, plasma microbial cell-free DNA sequencing was compared to standardized usual care testing. Pneumonia etiology was adjudicated by a blinded independent committee. The primary outcome, additive diagnostic value, was assessed in the Per Protocol population (patients with complete testing results and no major protocol deviations) and defined as the percent of patients with an etiology of pneumonia exclusively identified by plasma microbial cell-free DNA sequencing. Clinical additive diagnostic value was assessed in the Per Protocol subgroup with negative usual care testing. RESULTS: Of 257 patients, 173 met Per Protocol criteria. A pneumonia etiology was identified by usual care in 52/173 (30.1%), plasma microbial cell-free DNA sequencing in 49/173 (28.3%) and the combination of both in 73/173 (42.2%) patients. Plasma microbial cell-free DNA sequencing exclusively identified an etiology of pneumonia in 21/173 patients (additive diagnostic value 12.1%, 95% confidence interval [CI], 7.7% to 18.0%, P < .001). In the Per Protocol subgroup with negative usual care testing, plasma microbial cell-free DNA sequencing identified a pneumonia etiology in 21/121 patients (clinical additive diagnostic value 17.4%, 95% CI, 11.1% to 25.3%). CONCLUSIONS: Non-invasive plasma microbial cell-free DNA sequencing significantly increased diagnostic yield in immunocompromised patients with pneumonia undergoing bronchoscopy and extensive microbiologic and molecular testing. CLINICAL TRIALS REGISTRATION: NCT04047719.

Ancient environmental DNA (aeDNA) from lake sediments has yielded remarkable insights for the reconstruction of past ecosystems, including suggestions of late survival of extinct species. However, translocation and lateral inflow of DNA in sediments can potentially distort the stratigraphic signal of the DNA. Using three different approaches on two short lake sediment cores of the Yamal peninsula, West Siberia, with ages spanning only the past hundreds of years, we detect DNA and identified mitochondrial genomes of multiple mammoth and woolly rhinoceros individuals-both species that have been extinct for thousands of years on the mainland. The occurrence of clearly identifiable aeDNA of extinct Pleistocene megafauna (e.g. >400 K reads in one core) throughout these two short subsurface cores, along with specificities of sedimentology and dating, confirm that processes acting on regional scales, such as extensive permafrost thawing, can influence the aeDNA record and should be accounted for in aeDNA paleoecology.

Reference-free genome phasing is vital for understanding allele inheritance and the impact of single-molecule DNA variation on phenotypes. To achieve thorough phasing across homozygous or repetitive regions of the genome, long-read sequencing technologies are often used to perform phased de novo assembly. As a step toward reducing the cost and complexity of this type of analysis, we describe new methods for accurately phasing Oxford Nanopore Technologies (ONT) sequence data with the Shasta genome assembler and a modular tool for extending phasing to the chromosome scale called GFAse. We test using new variants of ONT PromethION sequencing, including those using proximity ligation, and show that newer, higher accuracy ONT reads substantially improve assembly quality.

Marine macroalgae produce abundant and diverse polysaccharides, which contribute substantially to the organic matter exported to the deep ocean. Microbial degradation of these polysaccharides plays an important role in the turnover of macroalgal biomass. Various members of the Planctomycetes-Verrucomicrobia-Chlamydia (PVC) superphylum are degraders of polysaccharides in widespread anoxic environments. In this study, we isolated a novel anaerobic bacterial strain NLcol2T from microbial mats on the surface of marine sediments offshore Santa Barbara, CA, USA. Based on 16S ribosomal RNA (rRNA) gene and phylogenomic analyses, strain NLcol2T represents a novel species within the Pontiella genus in the Kiritimatiellota phylum (within the PVC superphylum). Strain NLcol2T is able to utilize various monosaccharides, disaccharides, and macroalgal polysaccharides such as agar and ɩ-carrageenan. A near-complete genome also revealed an extensive metabolic capacity for anaerobic degradation of sulfated polysaccharides, as evidenced by 202 carbohydrate-active enzymes (CAZymes) and 165 sulfatases. Additionally, its ability of nitrogen fixation was confirmed by nitrogenase activity detected during growth on nitrogen-free medium, and the presence of nitrogenases (nifDKH) encoded in the genome. Based on the physiological and genomic analyses, this strain represents a new species of bacteria that may play an important role in the degradation of macroalgal polysaccharides and with relevance to the biogeochemical cycling of carbon, sulfur, and nitrogen in marine environments. Strain NLcol2T (= DSM 113125T = MCCC 1K08672T) is proposed to be the type strain of a novel species in the Pontiella genus, and the name Pontiella agarivorans sp. nov. is proposed.IMPORTANCEGrowth and intentional burial of marine macroalgae is being considered as a carbon dioxide reduction strategy but elicits concerns as to the fate and impacts of this macroalgal carbon in the ocean. Diverse heterotrophic microbial communities in the ocean specialize in these complex polymers such as carrageenan and fucoidan, for example, members of the Kiritimatiellota phylum. However, only four type strains within the phylum have been cultivated and characterized to date, and there is limited knowledge about the metabolic capabilities and functional roles of related organisms in the environment. The new isolate strain NLcol2T expands the known substrate range of this phylum and further reveals the ability to fix nitrogen during anaerobic growth on macroalgal polysaccharides, thereby informing the issue of macroalgal carbon disposal.

Opinion 129 addresses the status of Firmicutes corrig. Gibbons and Murray 1978 (Approved Lists 1980). The name has the category 'division' and was included in the Approved Lists of Bacterial Names, although that category had previously been removed from the International Code of Nomenclature of Bacteria (1975 revision onwards). When the category 'phylum' was introduced into the International Code of Nomenclature of Prokaryotes (ICNP) in 2021, equivalence between 'phylum' and 'division' was not stipulated. Since the definition of the taxonomic categories and their relative order is one of the principal tasks of every code of nomenclature, the inclusion of Firmicutes corrig. Gibbons and Murray 1978 in the Approved Lists was an error. The name is either not validly published or illegitimate because its category is not covered by the ICNP. If Firmicutes corrig. Gibbons and Murray 1978 (Approved Lists 1980) was a validly published phylum name, it would be illegitimate because it would contravene Rule 8, which does not permit any deviation from the requirement to derive a phylum name from the name of the type genus. Since Firmicutes corrig. Gibbons and Murray 1978 is also part of a 'misfitting megaclassification' recognized in Opinion 128, the name is rejected, without any pre-emption regarding a hypothetically validly published name Firmicutes at the rank of phylum. Gracilicutes Gibbons and Murray 1978 (Approved Lists 1980) and Anoxyphotobacteriae Gibbons and Murray 1978 (Approved Lists 1980) are also rejected. The validly published phylum names have a variety of advantages over their not validly published counterparts and cannot be replaced with ad hoc names suggested in the literature. To ease the transition, it is recommended to mention the not validly published phylum names which strongly deviate in spelling from their validly published counterparts along with the latter in publications during the next years.

In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cells conditions along the trajectorys path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

BackgroundThe accurate identification of the functional elements in the bovine genome is a fundamental requirement for high-quality analysis of data informing both genome biology and genomic selection. Functional annotation of the bovine genome was performed to identify a more complete catalog of transcript isoforms across bovine tissues.ResultsA total of 160,820 unique transcripts (50% protein coding) representing 34,882 unique genes (60% protein coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio isoform sequencing data, Oxford Nanopore Technologies sequencing data, de novo assembled transcripts from RNA sequencing data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as whole transcriptome termini site sequencing, RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing. A large proportion of identified transcripts (69%) were unannotated, of which 86% were produced by annotated genes and 14% by unannotated genes. A median of two 5' untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as noncoding genes in fetal tissues but as protein-coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available quantitative trait loci data to study tissue-tissue interconnection involved in different traits and construct the first bovine trait similarity network.ConclusionsThese validated results show significant improvement over current bovine genome annotations.

BackgroundMany approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually).ResultsWe approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.ConclusionsNew insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

Rheumatoid arthritis (RA) management leans toward achieving remission or low disease activity. In this study, we conducted single-cell RNA sequencing (scRNA-Seq) of peripheral blood mononuclear cells (PBMCs) from 36 individuals (18 patients with RA and 18 matched controls, accounting for age, sex, race, and ethnicity), to identify disease-relevant cell subsets and cell type-specific signatures associated with disease activity. Our analysis revealed 18 distinct PBMC subsets, including an IFN-induced transmembrane 3-overexpressing (IFITM3-overexpressing) IFN-activated monocyte subset. We observed an increase in CD4+ T effector memory cells in patients with moderate-high disease activity (DAS28-CRP ≥ 3.2) and a decrease in nonclassical monocytes in patients with low disease activity or remission (DAS28-CRP < 3.2). Pseudobulk analysis by cell type identified 168 differentially expressed genes between RA and matched controls, with a downregulation of proinflammatory genes in the γδ T cell subset, alteration of genes associated with RA predisposition in the IFN-activated subset, and nonclassical monocytes. Additionally, we identified a gene signature associated with moderate-high disease activity, characterized by upregulation of proinflammatory genes such as TNF, JUN, EGR1, IFIT2, MAFB, and G0S2 and downregulation of genes including HLA-DQB1, HLA-DRB5, and TNFSF13B. Notably, cell-cell communication analysis revealed an upregulation of signaling pathways, including VISTA, in both moderate-high and remission-low disease activity contexts. Our findings provide valuable insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

BackgroundRNA-seq has brought forth significant discoveries regarding aberrations in RNA processing, implicating these RNA variants in a variety of diseases. Aberrant splicing and single nucleotide variants (SNVs) in RNA have been demonstrated to alter transcript stability, localization, and function. In particular, the upregulation of ADAR, an enzyme that mediates adenosine-to-inosine editing, has been previously linked to an increase in the invasiveness of lung adenocarcinoma cells and associated with splicing regulation. Despite the functional importance of studying splicing and SNVs, the use of short-read RNA-seq has limited the community's ability to interrogate both forms of RNA variation simultaneously.ResultsWe employ long-read sequencing technology to obtain full-length transcript sequences, elucidating cis-effects of variants on splicing changes at a single molecule level. We develop a computational workflow that augments FLAIR, a tool that calls isoform models expressed in long-read data, to integrate RNA variant calls with the associated isoforms that bear them. We generate nanopore data with high sequence accuracy from H1975 lung adenocarcinoma cells with and without knockdown of ADAR. We apply our workflow to identify key inosine isoform associations to help clarify the prominence of ADAR in tumorigenesis.ConclusionsUltimately, we find that a long-read approach provides valuable insight toward characterizing the relationship between RNA variants and splicing patterns.

Long-read sequencing is driving rapid progress in genome assembly across all major groups of life, including species of the family Drosophilidae, a longtime model system for genetics, genomics, and evolution. We previously developed a cost-effective hybrid Oxford Nanopore (ONT) long-read and Illumina short-read sequencing approach and used it to assemble 101 drosophilid genomes from laboratory cultures, greatly increasing the number of genome assemblies for this taxonomic group. The next major challenge is to address the laboratory culture bias in taxon sampling by sequencing genomes of species that cannot easily be reared in the lab. Here, we build upon our previous methods to perform amplification-free ONT sequencing of single wild flies obtained either directly from the field or from ethanol-preserved specimens in museum collections, greatly improving the representation of lesser studied drosophilid taxa in whole-genome data. Using Illumina Novaseq X Plus and ONT P2 sequencers with R10.4.1 chemistry, we set a new benchmark for inexpensive hybrid genome assembly at US $150 per genome while assembling genomes from as little as 35 ng of genomic DNA from a single fly. We present 183 new genome assemblies for 179 species as a resource for drosophilid systematics, phylogenetics, and comparative genomics. Of these genomes, 62 are from pooled lab strains and 121 from single adult flies. Despite the sample limitations of working with small insects, most single-fly diploid assemblies are comparable in contiguity (>1 Mb contig N50), completeness (>98% complete dipteran BUSCOs), and accuracy (>QV40 genome-wide with ONT R10.4.1) to assemblies from inbred lines. We present a well-resolved multi-locus phylogeny for 360 drosophilid and 4 outgroup species encompassing all publicly available (as of August 2023) genomes for this group. Finally, we present a Progressive Cactus whole-genome, reference-free alignment built from a subset of 298 suitably high-quality drosophilid genomes. The new assemblies and alignment, along with updated laboratory protocols and computational pipelines, are released as an open resource and as a tool for studying evolution at the scale of an entire insect family.

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher's performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Sample multiplexing enables pooled analysis during single-cell RNA sequencing workflows, thereby increasing throughput and reducing batch effects. A challenge for all multiplexing techniques is to link sample-specific barcodes with cell-specific barcodes, then demultiplex sample identity post-sequencing. However, existing demultiplexing tools fail under many real-world conditions where barcode cross-contamination is an issue. We therefore developed deMULTIplex2, an algorithm inspired by a mechanistic model of barcode cross-contamination. deMULTIplex2 employs generalized linear models and expectation-maximization to probabilistically determine the sample identity of each cell. Benchmarking reveals superior performance across various experimental conditions, particularly on large or noisy datasets with unbalanced sample compositions.

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.

OBJECTIVES: Viral lower respiratory tract infection (vLRTI) contributes to substantial morbidity and mortality in children. Diagnosis is typically confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) of nasopharyngeal specimens in hospitalized patients; however, it is unknown whether nasopharyngeal detection accurately reflects presence of virus in the lower respiratory tract (LRT). This study evaluates agreement between viral detection from nasopharyngeal specimens by RT-PCR compared with metagenomic next-generation RNA sequencing (RNA-Seq) from tracheal aspirates (TAs). DESIGN: This is an analysis of of a seven-center prospective cohort study. SETTING: Seven PICUs within academic childrens hospitals in the United States. PATIENTS: Critically ill children (from 1 mo to 18 yr) who required mechanical ventilation via endotracheal tube for greater than or equal to 72 hours. INTERVENTIONS: We evaluated agreement in viral detection between paired upper and LRT samples. Results of clinical nasopharyngeal RT-PCR were compared with TA RNA-Seq. Positive and negative predictive agreement and Cohens Kappa were used to assess agreement. MEASUREMENTS AND MAIN RESULTS: Of 295 subjects with paired testing available, 200 (68%) and 210 (71%) had positive viral testing by RT-PCR from nasopharyngeal and RNA-Seq from TA samples, respectively; 184 (62%) were positive by both nasopharyngeal RT-PCR and TA RNA-Seq for a virus, and 69 (23%) were negative by both methods. Nasopharyngeal RT-PCR detected the most abundant virus identified by RNA-Seq in 92.4% of subjects. Among the most frequent viruses detected, respiratory syncytial virus demonstrated the highest degree of concordance (κ = 0.89; 95% CI, 0.83-0.94), whereas rhinovirus/enterovirus demonstrated lower concordance (κ = 0.55; 95% CI, 0.44-0.66). Nasopharyngeal PCR was more likely to detect multiple viruses than TA RNA-Seq (54 [18.3%] vs 24 [8.1%], p ≤ 0.001). CONCLUSIONS: Viral nucleic acid detection in the upper versus LRT reveals good overall agreement, but concordance depends on the virus. Further studies are indicated to determine the utility of LRT sampling or the use of RNA-Seq to determine LRTI etiology.

T(8;21)(q22;q22), which generates the AML1-ETO fusion oncoprotein, is a common chromosomal abnormality in acute myeloid leukemia (AML) patients. Despite having favorable prognosis, 40% of patients will relapse, highlighting the need for innovative models and application of the newest technologies to study t(8;21) leukemogenesis. Currently, available AML1-ETO mouse models have limited utility for studying the pre-leukemic stage because AML1-ETO produces mild hematopoietic phenotypes and no leukemic transformation. Conversely, overexpression of a truncated variant, AML1-ETO9a (AE9a), promotes fully penetrant leukemia and is too potent for studying pre-leukemic changes. To overcome these limitations, we devised a germline-transmitted Rosa26 locus AE9a knock-in mouse model that moderately overexpressed AE9a and developed leukemia with long latency and low penetrance. We observed pre-leukemic alterations in AE9a mice, including skewing of progenitors towards granulocyte/monocyte lineages and replating of stem and progenitor cells. Next, we performed single-cell RNA sequencing to identify specific cell populations that contribute to these pre-leukemic phenotypes. We discovered a subset of common myeloid progenitors that have heightened granulocyte/monocyte bias in AE9a mice. We also observed dysregulation of key hematopoietic transcription factor target gene networks, blocking cellular differentiation. Finally, we identified Sox4 activation as a potential contributor to stem cell self-renewal during the pre-leukemic stage.

The purpose of this paper is to re‐examine the sequence symmetry analysis (SSA) in a mathematical framework to improve the understanding and use of the design. The mathematical properties of the crude, null‐effects, and adjusted sequence ratios (SR) are analyzed in the presence of prescription time‐trends and unmeasured time‐invariant confounding. The theoretical results are illustrated in a simulation study. The crude SR can be interpreted as an estimator of the hazard ratio (HR) of treatment when the allowed time between initiation of the treatment and the outcome drug is small. The HR can easily be estimated flexibly as a function of covariates, such as age and sex, using logistic regression. The crude SR implicitly adjusts for unmeasured time‐invariant confounding, whereas the null‐effects SR, and thereby the adjusted SR, make little sense unless treatment and outcome are strictly independent. The use of the adjusted SR should be abandoned. Another design should be used if it is infeasible to require treatment and outcome sufficiently close. The crude SR can be modeled flexibly with logistic regression to estimate covariate‐dependent HRs or at least to test whether the HR depends on covariates. [ABSTRACT FROM AUTHOR]

\nHIGHLIGHTSActivity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data. Presents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation techniquePresents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation technique [ABSTRACT FROM AUTHOR]

Objective Background Methods Results Conclusion Aiming to generate evidence on how contextual conditions shape individuals' opportunities and constraints and, ultimately, life courses, we focus on a period of childcare expansion in reunified Germany. We investigate differences in employment trajectories around mothers' first childbirths to identify potential East–West convergence.During Germany's division (1949–1990), universal public childcare and female full‐time employment were the norm in East Germany, while the male breadwinner model was dominant in the West. These differences, although declining, persisted even decades after reunification. In 2008, a reform aimed at expanding childcare availability to facilitate mothers' employment throughout the country.We measure East–West differences in employment trajectories around childbirth pre‐ (1990–2007) and post‐reform (2008–2021) in terms of timing, order, and duration of events over time. We use data on 359 East and 986 West German first‐time‐mothers from the German Socio‐Economic Panel and sequence analysis tools.Before the reform, employment trajectories between East and West German mothers differed both in timing and duration of employment states. After the reform, these differences decreased, showing a general convergence in the prevalence of post‐birth part‐time employment. Nonetheless, longer maternity leave is still more prevalent among West German mothers, while East German mothers are more likely to maintain full‐time jobs.Our findings show how policy settings and reforms shape life courses in a context‐dependent fashion. They illustrate the importance of a methodological approach that focuses on process outcomes and supports a theoretical perspective that highlights how historical time and place shape life courses. [ABSTRACT FROM AUTHOR]

Porcine hemagglutinating encephalomyelitis virus (PHEV) is the only known porcine neurotropic coronavirus, which is prevalent worldwide at present. It is of great significance to understand the genetic and evolutionary characteristics of PHEV in order to perform effective measures for prevention and control of this disease. In this study, a total of 6,986 tissue samples and nasopharyngeal swabs were collected from different regions of Guangxi province in southern China during 2021-2024, and were tested for PHEV using a quadruplex RT-qPCR. The positivity rate of PHEV was 2.81% (196/6,986), of which tissue samples and nasopharyngeal swabs had 2.05% (87/4,246) and 3.98% (109/2,740) positivity rates, respectively. Fifty PHEV positive samples were selected for PCR amplification and gene sequencing. Sequence analysis revealed that the nucleotide homology and amino acid similarities of S, M, and N genes were 94.3%-99.3% and 92.3%-99.2%, 95.0%-99.7% and 94.7%-100.0%, 94.0%-99.5% and 93.5%-99.3%, respectively, indicating M and N genes were more conservative than S gene. Phylogenetic trees based on these three genes revealed that PHEV strains from different countries could be divided into two groups G1 and G2, and the PHEV strains from Guangxi province obtained in this study distributed in subgroups G1c and G2b. Bayesian analysis revealed that the population size of PHEV has been in a relatively stable state since its discovery until it expanded sharply around 2015, and still on the slow rise thereafter. S gene sequences analysis indicated that PHEV strains existed variation of mutation, and recombination. The results indicated that the prevalent PHEV strains in Guangxi province had complex evolutionary trajectories and high genetic diversity. To the best of our knowledge, this is the first report on the genetic and evolutionary characteristics of PHEV in southern China. [ABSTRACT FROM AUTHOR]

Background: Mouth breathing (MB), a risk factor of oral dysbiosis and halitosis, is linked with craniofacial anomalies and pediatric obstructive sleep apnea. Here, we aimed to analyze tongue microbiota in children from the perspective of their breathing pattern before/during orthodontic treatment. Methods: This prospective case–control study included 30 children with orthodontic anomalies, 15 with MB and 15 with nasal breathing (NB), matched by age, sex, and body mass index. All underwent orthodontic examination and sleep apnea monitoring. Tongue swabs were collected before starting (timepoint M0) and approx. six months into the orthodontic therapy (timepoint M6). Oral candidas and bacteriome were analyzed using mass spectrometry technique and 16S rRNA sequencing, respectively. Results: MB was associated with higher apnea–hypopnea index. At M0, oral candidas were equally present in both groups. At M6, Candida sp. were found in six children with MB but in none with NB. No significant differences in bacterial diversity were observed between groups and timepoints. However, presence/relative abundance of genus Solobacterium was higher in children with MB than NB at M0. Conclusions: Significant links between MB and the presence of genus Solobacterium (M0) as well as Candida sp. (M6) were found in children with orthodontic anomalies, highlighting the risk of halitosis in them. [ABSTRACT FROM AUTHOR]

Background: Pullorum disease is a serious problem in many countries. Caused by Salmonella enterica serovar Gallinarum biovar Pullorum (S. Pullorum), it creates huge economic losses in the poultry industry. Although pullorum disease has been well-controlled in many developed countries, it is still a critical problem in developing countries. However, there is still a lack of information on S. Pullorum strains isolated from different regions and sources in China. The objective of this study was to supply the antimicrobial resistance patterns and clonal relationships of S. Pullorum from breeder chicken farms. Methods: In this study, a total of 114 S. Pullorum strains recovered from 11 provinces and municipalities in China between 2020 and 2021 were selected. These 114 S. Pullorum strains were analyzed using whole genome sequencing (WGS). Antimicrobial resistance (AMR) was tested both by genotypic prediction using the WGS method and using disc diffusion to assess phenotypic AMR. Results: These 114 sequenced S. Pullorum strains were divided into three sequence types (STs), the dominant STs was ST92 (104/114). Further core genome multi-locus sequence typing analysis indicated that 114 S. Pullorum strains may have a close relationship, which could be clonally transmitted among different provinces and municipalities. Our results showed a close relationship between the S. Pullorum strains found in different regions, indicating these strains may have been transmitted in China a long time ago. Nearly all S. Pullorum strains 94.74% (n = 108) were resistant to at least one antimicrobial class, and 35.96% of the examined Salmonella strains were considered multiple drug resistant. Conclusion: Overall, this study showed that S. Pullorum strains in China have a close genetic relationship in terms of antimicrobial resistance, suggesting widespread clonal transmission. [ABSTRACT FROM AUTHOR]