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Richter syndrome (RS) arising from chronic lymphocytic leukemia (CLL) exemplifies an aggressive malignancy that develops from an indolent neoplasm. To decipher the genetics underlying this transformation, we computationally deconvoluted admixtures of CLL and RS cells from 52 patients with RS, evaluating paired CLL-RS whole-exome sequencing data. We discovered RS-specific somatic driver mutations (including IRF2BP2, SRSF1, B2M, DNMT3A and CCND3), recurrent copy-number alterations beyond del(9p21)(CDKN2A/B), whole-genome duplication and chromothripsis, which were confirmed in 45 independent RS cases and in an external set of RS whole genomes. Through unsupervised clustering, clonally related RS was largely distinct from diffuse large B cell lymphoma. We distinguished pathways that were dysregulated in RS versus CLL, and detected clonal evolution of transformation at single-cell resolution, identifying intermediate cell states. Our study defines distinct molecular subtypes of RS and highlights cell-free DNA analysis as a potential tool for early diagnosis and monitoring.

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

Recent advances in cancer characterization have consistently revealed marked heterogeneity, impeding the completion of integrated molecular and clinical maps for each malignancy. Here, we focus on chronic lymphocytic leukemia (CLL), a B cell neoplasm with variable natural history that is conventionally categorized into two subtypes distinguished by extent of somatic mutations in the heavy-chain variable region of immunoglobulin genes (IGHV). To build the 'CLL map,' we integrated genomic, transcriptomic and epigenomic data from 1,148 patients. We identified 202 candidate genetic drivers of CLL (109 new) and refined the characterization of IGHV subtypes, which revealed distinct genomic landscapes and leukemogenic trajectories. Discovery of new gene expression subtypes further subcategorized this neoplasm and proved to be independent prognostic factors. Clinical outcomes were associated with a combination of genetic, epigenetic and gene expression features, further advancing our prognostic paradigm. Overall, this work reveals fresh insights into CLL oncogenesis and prognostication.

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the data would lead to large changes in the chosen feature subset, then many selected features are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metrics (MSE or AUC) with proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications with continuous or binary outcomes. We conclude that Stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selection method.

Anti-cancer therapies have been limited by emergence of mutations and other adaptations. In bacteria, antibiotics activate the SOS response, which mobilizes error-prone factors that allow for continuous replication at the cost of mutagenesis. We investigated whether treatment of lung cancer with EGFR inhibitors (EGFRi) similarly engages hypermutators. In cycling drug-tolerant persister (DTP) cells and in EGFRi-treated patients presenting residual disease we observed upregulation of GAS6, while ablation of GAS6's receptor, AXL, eradicated resistance. Reciprocally, AXL overexpression enhanced DTP survival and accelerated the emergence of T790M, an EGFR mutation typical to resistant cells. Mechanistically, AXL induces low-fidelity DNA polymerases and activates their organizer, RAD18, by promoting neddylation. Metabolomics uncovered another hypermutator, AXL-driven activation of MYC and increased purine synthesis that is unbalanced by pyrimidines. Aligning anti-AXL combination treatments with the transition from DTPs to resistant cells cured patient-derived xenografts. Hence, similar to bacteria, tumors tolerate therapy by engaging pharmacologically targetable endogenous mutators.

While cancers evolve during disease progression and in response to therapy, temporal dynamics remain difficult to study in humans due to the lack of consistent barcodes marking individual clones in vivo. We employ mitochondrial single-cell assay for transposase-accessible chromatin with sequencing to profile 163,279 cells from 9 patients with chronic lymphocytic leukemia (CLL) collected across disease course and utilize mitochondrial DNA (mtDNA) mutations as natural genetic markers of cancer clones. We observe stable propagation of mtDNA mutations over years in the absence of strong selective pressure, indicating clonal persistence, but dramatic changes following tight bottlenecks, including disease transformation and relapse posttherapy, paralleled by acquisition of copy-number variants and changes in chromatin accessibility and gene expression. Furthermore, we link CLL subclones to distinct chromatin states, providing insight into nongenetic sources of relapse. mtDNA mutations thus mirror disease history and provide naturally occurring genetic barcodes to enable patient-specific study of cancer subclonal dynamics.SignificanceSingle-cell multi-omic profiling of CLL reveals the utility of somatic mtDNA mutations as in vivo barcodes, which mark subclones that can evolve over time along with changes in accessible chromatin and gene expression profiles to capture dynamics of disease evolution. See related commentary by Hilton and Scott, p. 2965. This article is highlighted in the In This Issue feature, p. 2945.

BackgroundSARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting.MethodsWe collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model.ResultsSixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic.ConclusionsThese results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Nevertheless, benchmarking of metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that were analyzed and interpreted and clearly stating the strategy used for metagenomic profiling.

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality-including ordination integration and animations-alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of 'omic data.IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species' viability from total RNA sequencing.

Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person's age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site's microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases.

Here we propose that using shotgun sequencing to examine food leads to accurate authentication of ingredients and detection of contaminants. To demonstrate this, we developed a bioinformatic pipeline, FASER (Food Authentication from SEquencing Reads), designed to resolve the relative composition of mixtures of eukaryotic species using RNA or DNA sequencing. Our comprehensive database includes >6000 plants and animals that may be present in food. FASER accurately identified eukaryotic species with 0.4% median absolute difference between observed and expected proportions on sequence data from various sources including sausage meat, plants, and fish. FASER was applied to 31 high protein powder raw factory ingredient total RNA samples. The samples mostly contained the expected source ingredient, chicken, while three samples unexpectedly contained pork and beef. Our results demonstrate that DNA/RNA sequencing of food ingredients, combined with a robust analysis, can be used to find contaminants and authenticate food ingredients in a single assay.

Introduction In late 2019, several people in Wuhan, China, were presenting with severe pneumonia at the hospitals. As the number of patients rapidly increased, the Chinese government decided on 23 [...], Objective To analyse genome variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Methods Between 1 February and 1 May 2020, we downloaded 10 022 SARS CoV-2 genomes from four databases. The genomes were from infected patients in 68 countries. We identified variants by extracting pairwise alignment to the reference genome NC_045512, using the EMBOSS needle. Nucleotide variants in the coding regions were converted to corresponding encoded amino acid residues. For clade analysis, we used the open source software Bayesian evolutionary analysis by sampling trees, version 2.5. Findings We identified 5775 distinct genome variants, including 2969 missense mutations, 1965 synonymous mutations, 484 mutations in the non-coding regions, 142 non-coding deletions, 100 in-frame deletions, 66 non-coding insertions, 36 stop-gained variants, 11 frameshift deletions and two in-frame insertions. The most common variants were the synonymous 3037C > T (6334 samples), P4715L in the open reading frame 1ab (6319 samples) and D614G in the spike protein (6294 samples). We identified six major clades, (that is, basal, D614G, L84S, L3606F, D448del and G392D) and 14 subclades. Regarding the base changes, the C > T mutation was the most common with 1670 distinct variants. Conclusion We found that several variants of the SARS-CoV-2 genome exist and that the D614G clade has become the most common variant since December 2019. The evolutionary analysis indicated structured transmission, with the possibility of multiple introductions into the population. Objectif Analyser les variantes du genome de coronavirus 2 du syndrome respiratoire aigu severe (SARS-CoV-2). Methodes Entre le 1er fevrier et le 1er mai 2020, nous avons telecharge 10 022 genomes de SARS CoV-2 issus de quatre bases de donnees. Ces genomes provenaient de patients infectes originaires de 68 pays. Nous avons identifie les variantes en procedant a un alignement par paires avec la sequence de reference NC_045512, a l'aide de l'outil EMBOSS Needle. Les variantes de nucleotides dans les regions codantes ont ete converties en residus d'acides amines codes correspondants. Enfin, pour analyser le clade, nous avons employe un logiciel open source appele Bayesian Evolutionary Analysis by Sampling Trees, version 2.5. Resultats Nous avons detecte 5775 variantes de genome distinctes, dont 2969 mutations faux-sens, 1965 mutations synonymes, 484 mutations dans les regions non codantes, 142 deletions non codantes, 100 deletions sans decalage du cadre de lecture, 66 insertions non codantes, 36 variantes de codon stop, 11 deletions entrainant un decalage du cadre de lecture, et 2 insertions sans decalage du cadre de lecture. Les variantes les plus frequentes etaient les synonymes 3037C > T (6334 echantillons), P4715L dans le cadre ouvert de lecture 1ab (6319 echantillons) et D614G dans la proteine de spicule (6294 echantillons). Nous avons identifie six clades majeurs (a savoir, de base, D614G, L84S, L3606F, D448del et G392D) et 14 sous-clades. Quant aux changements de base, la mutation C > T etait la plus repandue avec 1670 variantes distinctes. Conclusion Nous avons constate qu'il existait de nombreuses variantes du genome de SARS-CoV-2, et que le clade D614G etait devenu la variante la plus commune depuis decembre 2019. Lanalyse evolutive a indique une transmission structuree, avec une possibilite d'introductions multiples au sein de la population. Objetivo Analizar las variantes del genoma del coronavirus tipo 2 del sindrome respiratorio agudo grave (SARS-CoV-2). Metodos Entre el 1 de febrero y el 1 de mayo de 2020, se registraron 10 022 genomas del CoV-2 del SARS en cuatro bases de datos. Los genomas eran de pacientes infectados ubicados en 68 paises. Se identificaron variantes al extraer la alineacion por pares del genoma de referencia NC_045512, por medio de EMBOSS Needle. Las variantes de los nucleotidos en las regiones codificantes se convirtieron en los correspondientes residuos de aminoacidos codificados. Para analizar los clados, se utilizo el programa informatico de codigo abierto Bayesian evolutionary analysis by sampling trees, version 2.5. Resultados Se identificaron 5775 variaciones diferentes del genoma, incluidas 2969 mutaciones con cambio de sentido, 1965 mutaciones sinonimas, 484 mutaciones en las regiones no codificantes, 142 supresiones no codificantes, 100 supresiones en la fase, 66 inserciones no codificantes, 36 variaciones de parada prematuras (stop-gained), 11 supresiones de desplazamiento de fase y dos inserciones en la fase. Las variaciones mas comunes eran las sinonimas 3037C > T (6334 muestras), P4715L en la fase abierta de lectura 1ab (6319 muestras) y D614G en la proteina S (6294 muestras). Se identificaron seis clados principales, (es decir, basal, D614G, L84S, L3606F, D448del y G392D) y 14 subclados. En relacion con los cambios de base, la mutacion C > T fue la mas comun con 1670 variaciones diferentes. Conclusion Se determino que existen diversas variaciones del genoma del SARS-CoV-2 y que el clado D614G es la variante mas comun desde diciembre de 2019. El analisis evolutivo indico una transmision estructurada, en la que existe la posibilidad de que se realicen multiples inserciones en la poblacion. [phrase omitted]