Benchmarking Metagenomics Tools for Taxonomic Classification.

Metagenomic sequencing is revolutionizing the detection and characterization of microbial species, and a wide variety of software tools are available to perform taxonomic classification of these data. The fast pace of development of these tools and the complexity of metagenomic data make it important that researchers are able to benchmark their performance. Here, we review current approaches for metagenomic analysis and evaluate the performance of 20 metagenomic classifiers using simulated and experimental datasets. We describe the key metrics used to assess performance, offer a framework for the comparison of additional classifiers, and discuss the future of metagenomic data analysis.

Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay.

Although studies have identified hundreds of loci associated with human traits and diseases, pinpointing causal alleles remains difficult, particularly for non-coding variants. To address this challenge, we adapted the massively parallel reporter assay (MPRA) to identify variants that directly modulate gene expression. We applied it to 32,373 variants from 3,642 cis-expression quantitative trait loci and control regions. Detection by MPRA was strongly correlated with measures of regulatory function. We demonstrate MPRA's capabilities for pinpointing causal alleles, using it to identify 842 variants showing differential expression between alleles, including 53 well-annotated variants associated with diseases and traits. We investigated one in detail, a risk allele for ankylosing spondylitis, and provide direct evidence of a non-coding variant that alters expression of the prostaglandin EP4 receptor. These results create a resource of concrete leads and illustrate the promise of this approach for comprehensively interrogating how non-coding polymorphism shapes human biology.

Ebola Virus Epidemiology, Transmission, and Evolution during Seven Months in Sierra Leone.

The 2013-2015 Ebola virus disease (EVD) epidemic is caused by the Makona variant of Ebola virus (EBOV). Early in the epidemic, genome sequencing provided insights into virus evolution and transmission and offered important information for outbreak response. Here, we analyze sequences from 232 patients sampled over 7 months in Sierra Leone, along with 86 previously released genomes from earlier in the epidemic. We confirm sustained human-to-human transmission within Sierra Leone and find no evidence for import or export of EBOV across national borders after its initial introduction. Using high-depth replicate sequencing, we observe both host-to-host transmission and recurrent emergence of intrahost genetic variants. We trace the increasing impact of purifying selection in suppressing the accumulation of nonsynonymous mutations over time. Finally, we note changes in the mucin-like domain of EBOV glycoprotein that merit further investigation. These findings clarify the movement of EBOV within the region and describe viral evolution during prolonged human-to-human transmission.

High-throughput Oligopaint screen identifies druggable 3D genome regulators.

The human genome functions as a three-dimensional chromatin polymer, driven by a complex collection of chromosome interactions1-3. Although the molecular rules governing these interactions are being quickly elucidated, relatively few proteins regulating this process have been identified. Here, to address this gap, we developed high-throughput DNA or RNA labelling with optimized Oligopaints (HiDRO)-an automated imaging pipeline that enables the quantitative measurement of chromatin interactions in single cells across thousands of samples. By screening the human druggable genome, we identified more than 300 factors that influence genome folding during interphase. Among these, 43 genes were validated as either increasing or decreasing interactions between topologically associating domains. Our findings show that genetic or chemical inhibition of the ubiquitous kinase GSK3A leads to increased long-range chromatin looping interactions in a genome-wide and cohesin-dependent manner. These results demonstrate the importance of GSK3A signalling in nuclear architecture and the use of HiDRO for identifying mechanisms of spatial genome organization.

Identifying recent adaptations in large-scale genomic data.

Although several hundred regions of the human genome harbor signals of positive natural selection, few of the relevant adaptive traits and variants have been elucidated. Using full-genome sequence variation from the 1000 Genomes (1000G) Project and the composite of multiple signals (CMS) test, we investigated 412 candidate signals and leveraged functional annotation, protein structure modeling, epigenetics, and association studies to identify and extensively annotate candidate causal variants. The resulting catalog provides a tractable list for experimental follow-up; it includes 35 high-scoring nonsynonymous variants, 59 variants associated with expression levels of a nearby coding gene or lincRNA, and numerous variants associated with susceptibility to infectious disease and other phenotypes. We experimentally characterized one candidate nonsynonymous variant in Toll-like receptor 5 (TLR5) and show that it leads to altered NF-κB signaling in response to bacterial flagellin. PAPERFLICK:

How deregulation, drought and increasing fire impact Amazonian biodiversity.

Biodiversity contributes to the ecological and climatic stability of the Amazon Basin1,2, but is increasingly threatened by deforestation and fire3,4. Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079-189,755 km2 of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3-85.2% of species that are listed as threatened in this region5. The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253-10,343 km2 of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. These results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.

Development of an amplicon-based sequencing approach in response to the global emergence of mpox.

The 2022 multicountry mpox outbreak concurrent with the ongoing Coronavirus Disease 2019 (COVID-19) pandemic further highlighted the need for genomic surveillance and rapid pathogen whole-genome sequencing. While metagenomic sequencing approaches have been used to sequence many of the early mpox infections, these methods are resource intensive and require samples with high viral DNA concentrations. Given the atypical clinical presentation of cases associated with the outbreak and uncertainty regarding viral load across both the course of infection and anatomical body sites, there was an urgent need for a more sensitive and broadly applicable sequencing approach. Highly multiplexed amplicon-based sequencing (PrimalSeq) was initially developed for sequencing of Zika virus, and later adapted as the main sequencing approach for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Here, we used PrimalScheme to develop a primer scheme for human monkeypox virus that can be used with many sequencing and bioinformatics pipelines implemented in public health laboratories during the COVID-19 pandemic. We sequenced clinical specimens that tested presumptively positive for human monkeypox virus with amplicon-based and metagenomic sequencing approaches. We found notably higher genome coverage across the virus genome, with minimal amplicon drop-outs, in using the amplicon-based sequencing approach, particularly in higher PCR cycle threshold (Ct) (lower DNA titer) samples. Further testing demonstrated that Ct value correlated with the number of sequencing reads and influenced the percent genome coverage. To maximize genome coverage when resources are limited, we recommend selecting samples with a PCR Ct below 31 Ct and generating 1 million sequencing reads per sample. To support national and international public health genomic surveillance efforts, we sent out primer pool aliquots to 10 laboratories across the United States, United Kingdom, Brazil, and Portugal. These public health laboratories successfully implemented the human monkeypox virus primer scheme in various amplicon sequencing workflows and with different sample types across a range of Ct values. Thus, we show that amplicon-based sequencing can provide a rapidly deployable, cost-effective, and flexible approach to pathogen whole-genome sequencing in response to newly emerging pathogens. Importantly, through the implementation of our primer scheme into existing SARS-CoV-2 workflows and across a range of sample types and sequencing platforms, we further demonstrate the potential of this approach for rapid outbreak response.

Sex-dependent phenological responses to climate vary across species' ranges.

Anthropogenic climate change has significantly altered the flowering times (i.e., phenology) of plants worldwide, affecting their reproduction, survival, and interactions. Recent studies utilizing herbarium specimens have uncovered significant intra- and inter-specific variation in flowering phenology and its response to changes in climate but have mostly been limited to animal-pollinated species. Thus, despite their economic and ecological importance, variation in phenological responses to climate remain largely unexplored among and within wind-pollinated dioecious species and across their sexes. Using both herbarium specimens and volunteer observations of cottonwood (Populus) species, we examined how phenological sensitivity to climate varies across species, their ranges, sexes, and phenophases. The timing of flowering varied significantly across and within species, as did their sensitivity to spring temperature. In particular, male flowering generally happened earlier in the season and was more sensitive to warming than female flowering. Further, the onset of flowering was more sensitive to changes in temperature than leaf out. Increased temporal gaps between male and female flowering time and between the first open flower date and leaf out date were predicted for the future under two climate change scenarios. These shifts will impact the efficacy of sexual reproduction and gene flow among species. Our study demonstrates significant inter- and intra-specific variation in phenology and its responses to environmental cues, across species' ranges, phenophases, and sex, in wind-pollinated species. These variations need to be considered to predict accurately the effects of climate change and assess their ecological and evolutionary consequences.

Co-depletion of NIPBL and WAPL balance cohesin activity to correct gene misexpression.

The relationship between cohesin-mediated chromatin looping and gene expression remains unclear. NIPBL and WAPL are two opposing regulators of cohesin activity; depletion of either is associated with changes in both chromatin folding and transcription across a wide range of cell types. However, a direct comparison of their individual and combined effects on gene expression in the same cell type is lacking. We find that NIPBL or WAPL depletion in human HCT116 cells each alter the expression of ~2,000 genes, with only ~30% of the genes shared between the conditions. We find that clusters of differentially expressed genes within the same topologically associated domain (TAD) show coordinated misexpression, suggesting some genomic domains are especially sensitive to both more or less cohesin. Finally, co-depletion of NIPBL and WAPL restores the majority of gene misexpression as compared to either knockdown alone. A similar set of NIPBL-sensitive genes are rescued following CTCF co-depletion. Together, this indicates that altered transcription due to reduced cohesin activity can be functionally offset by removal of either its negative regulator (WAPL) or the physical barriers (CTCF) that restrict loop-extrusion events.

Association of oral microbiome with oral human papillomavirus infection: a population study of the National Health and Nutrition Examination Survey, 2009-2012.

BACKGROUND: Oral human papillomavirus(HPV) infection and the oral microbiome are associated with oropharyngeal cancer. However, population-based data on the association of oral microbiome with oral HPV infection are limited. METHOD: We performed a cross-sectional analysis of 5,496 participants aged 20-59 in National Health and Nutrition Examination Surveys(NHANES):2009-2012. The association between either oral microbiome alpha diversity or beta diversity and oral HPV infection was assessed using multivariable logistic regression or principal coordinate analyses(PCoA) and multivariate analysis of variance(PERMANOVA). RESULTS: For alpha diversity, we found a lower number of observed Amplicon sequence variants(ASVs) (adjusted odds ratio[aOR] = 0.996; 95%CI = 0.992-0.999) and reduced Faith's Phylogenetic Diversity(aOR = 0.95; 95%CI = 0.90-0.99) associated with high-risk oral HPV infection in the overall population. This trend was observed in males for both high-risk and any oral HPV infection. Beta diversity showed differentiation of oral microbiome community by high-risk oral HPV infection as measured by Bray-Curtis dissimilarity (R2 = 0.054%; P = .029) and unweighted UniFrac distance (R2 = 0.046%; P = .045) among the overall population, and associations were driven by males. CONCLUSIONS: Both oral microbiome alpha diversity(within-sample richness and phylogenetic diversity) and beta diversity(heterogeneous dispersion of oral microbiome community) are associated with HPV infection. Longitudinal studies are needed to characterize the role of the microbiome in the natural history of oral HPV infection.

Eddy current-induced artifact correction in high b-value ex vivo human brain diffusion MRI with dynamic field monitoring.

PURPOSE: To investigate whether spatiotemporal magnetic field monitoring can correct pronounced eddy current-induced artifacts incurred by strong diffusion-sensitizing gradients up to 300 mT/m used in high b-value diffusion-weighted (DW) EPI. METHODS: A dynamic field camera equipped with 16 1 H NMR field probes was first used to characterize field perturbations caused by residual eddy currents from diffusion gradients waveforms in a 3D multi-shot EPI sequence on a 3T Connectom scanner for different gradient strengths (up to 300 mT/m), diffusion directions, and shots. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-gradient strength, submillimeter resolution whole-brain ex vivo diffusion MRI. A 3D multi-shot image reconstruction framework was developed that incorporated the nonlinear phase evolution measured with the dynamic field camera. RESULTS: Phase perturbations in the readout induced by residual eddy currents from strong diffusion gradients are highly nonlinear in space and time, vary among diffusion directions, and interfere significantly with the image encoding gradients, changing the k-space trajectory. During the readout, phase modulations between odd and even EPI echoes become non-static and diffusion encoding direction-dependent. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting reduction approaches such as navigator- and structured low-rank-based methods or MUSE followed by image-based distortion correction with the FSL tool "eddy." CONCLUSION: Strong eddy current artifacts characteristic of high-gradient strength DW-EPI can be well corrected with dynamic field monitoring-based image reconstruction.

Impact of surgical risk factors for non-union on lumbar spinal fusion outcomes using cellular bone allograft at 24-months follow-up.

BACKGROUND: The current report investigates fusion rates and patient-reported outcomes following lumbar spinal surgery using cellular bone allograft (CBA) in patients with risk factors for non-union. METHODS: A prospective, open label study was conducted in subjects undergoing lumbar spinal fusion with CBA (NCT02969616) to assess fusion success rates and patient-reported outcomes in subjects with risk factors for non-union. Subjects were categorized into low-risk (≤ 1 risk factors) and high-risk (> 1 risk factors) groups. Radiographic fusion status was evaluated by an independent review of dynamic radiographs and CT scans. Patient-reported outcome measures included quality of life (EQ-5D), Oswestry Disability Index (ODI) and Visual Analog Scales (VAS) for back and leg pain. Adverse event reporting was conducted throughout 24-months of follow-up. RESULTS: A total of 274 subjects were enrolled: 140 subjects (51.1%) were categorized into the high-risk group (> 1 risk factor) and 134 subjects (48.9%) into the low-risk group (≤ 1 risk factors). The overall mean age at screening was 58.8 years (SD 12.5) with a higher distribution of females (63.1%) than males (36.9%). No statistical difference in fusion rates were observed between the low-risk (90.0%) and high-risk (93.9%) groups (p > 0.05). A statistically significant improvement in patient-reported outcomes (EQ-5D, ODI and VAS) was observed at all time points (p < 0.05) in both low and high-risk groups. The low-risk group showed enhanced improvement at multiple timepoints in EQ-5D, ODI, VAS-Back pain and VAS-Leg pain scores compared to the high-risk group (p < 0.05). The number of AEs were similar among risk groups. CONCLUSIONS: This study demonstrates high fusion rates following lumbar spinal surgery using CBA, regardless of associated risk factors. Patient reported outcomes and fusion rates were not adversely affected by risk factor profiles. TRIAL REGISTRATION: NCT02969616 (21/11/2016).

The Invisible University Is COVID-19 Positive.

Within the ivory tower of academia, the coronavirus disease 2019 (COVID-19) pandemic stands to disproportionately impact the invisible workforce of postdoctoral researchers (postdocs). Faced with university closures, hiring freezes, and a general lack of support and benefits, an entire generation of postdocs and their knowledge and skills may be lost to academia without intervention.

Complex climate-mediated effects of urbanization on plant reproductive phenology and frost risk.

Urbanization can affect the timing of plant reproduction (i.e. flowering and fruiting) and associated ecosystem processes. However, our knowledge of how plant phenology responds to urbanization and its associated environmental changes is limited. Herbaria represent an important, but underutilized source of data for investigating this question. We harnessed phenological data from herbarium specimens representing 200 plant species collected across 120 yr from the eastern US to investigate the spatiotemporal effects of urbanization on flowering and fruiting phenology and frost risk (i.e. time between the last frost date and flowering). Effects of urbanization on plant reproductive phenology varied significantly in direction and magnitude across species ranges. Increased urbanization led to earlier flowering in colder and wetter regions and delayed fruiting in regions with wetter spring conditions. Frost risk was elevated with increased urbanization in regions with colder and wetter spring conditions. Our study demonstrates that predictions of phenological change and its associated impacts must account for both climatic and human effects, which are context dependent and do not necessarily coincide. We must move beyond phenological models that only incorporate temperature variables and consider multiple environmental factors and their interactions when estimating plant phenology, especially at larger spatial and taxonomic scales.

Combining genomics and epidemiology to track mumps virus transmission in the United States.

Unusually large outbreaks of mumps across the United States in 2016 and 2017 raised questions about the extent of mumps circulation and the relationship between these and prior outbreaks. We paired epidemiological data from public health investigations with analysis of mumps virus whole genome sequences from 201 infected individuals, focusing on Massachusetts university communities. Our analysis suggests continuous, undetected circulation of mumps locally and nationally, including multiple independent introductions into Massachusetts and into individual communities. Despite the presence of these multiple mumps virus lineages, the genomic data show that one lineage has dominated in the US since at least 2006. Widespread transmission was surprising given high vaccination rates, but we found no genetic evidence that variants arising during this outbreak contributed to vaccine escape. Viral genomic data allowed us to reconstruct mumps transmission links not evident from epidemiological data or standard single-gene surveillance efforts and also revealed connections between apparently unrelated mumps outbreaks.

Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach.

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.

Genomic epidemiology reveals multiple introductions of Zika virus into the United States.

Zika virus (ZIKV) is causing an unprecedented epidemic linked to severe congenital abnormalities. In July 2016, mosquito-borne ZIKV transmission was reported in the continental United States; since then, hundreds of locally acquired infections have been reported in Florida. To gain insights into the timing, source, and likely route(s) of ZIKV introduction, we tracked the virus from its first detection in Florida by sequencing ZIKV genomes from infected patients and Aedes aegypti mosquitoes. We show that at least 4 introductions, but potentially as many as 40, contributed to the outbreak in Florida and that local transmission is likely to have started in the spring of 2016-several months before its initial detection. By analysing surveillance and genetic data, we show that ZIKV moved among transmission zones in Miami. Our analyses show that most introductions were linked to the Caribbean, a finding corroborated by the high incidence rates and traffic volumes from the region into the Miami area. Our study provides an understanding of how ZIKV initiates transmission in new regions.

Zika virus evolution and spread in the Americas.

Although the recent Zika virus (ZIKV) epidemic in the Americas and its link to birth defects have attracted a great deal of attention, much remains unknown about ZIKV disease epidemiology and ZIKV evolution, in part owing to a lack of genomic data. Here we address this gap in knowledge by using multiple sequencing approaches to generate 110 ZIKV genomes from clinical and mosquito samples from 10 countries and territories, greatly expanding the observed viral genetic diversity from this outbreak. We analysed the timing and patterns of introductions into distinct geographic regions; our phylogenetic evidence suggests rapid expansion of the outbreak in Brazil and multiple introductions of outbreak strains into Puerto Rico, Honduras, Colombia, other Caribbean islands, and the continental United States. We find that ZIKV circulated undetected in multiple regions for many months before the first locally transmitted cases were confirmed, highlighting the importance of surveillance of viral infections. We identify mutations with possible functional implications for ZIKV biology and pathogenesis, as well as those that might be relevant to the effectiveness of diagnostic tests.

Virus genomes reveal factors that spread and sustained the Ebola epidemic.

The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics.