Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues toward truly quantitative and reproducible metagenomics measurements.

We present constraints on cosmological and astrophysical parameters from high-resolution microwave background maps at 148 GHz and 218 GHz made by the Atacama Cosmology Telescope (ACT) in three seasons of observations from 2008 to 2010. A model of primary cosmological and secondary foreground parameters is fit to the map power spectra and lensing deflection power spectrum, including contributions from both the thermal Sunyaev-Zeldovich (tSZ) effect and the kinematic Sunyaev-Zeldovich (kSZ) effect, Poisson and correlated anisotropy from unresolved infrared sources, radio sources, and the correlation between the tSZ effect and infrared sources. The power ℓ 2 C ℓ /2π of the thermal SZ power spectrum at 148 GHz is measured to be 3.4 ± 1.4 µK 2 at ℓ = 3000, while the corresponding amplitude of the kinematic SZ power spectrum has a 95% confidence level upper limit of 8.6 µK 2. Combining ACT power spectra with the WMAP 7-year temperature and polarization power spectra, we find excellent consistency with the LCDM model. We constrain the number of effective relativistic degrees of freedom in the early universe to be N eff = 2.79 ± 0.56, in agreement with the canonical value of N eff = 3.046 for three massless neutrinos. We constrain the sum of the neutrino masses to be Σm ν < 0.39 eV at 95% confidence when combining ACT and WMAP 7-year data with BAO and Hubble constant measurements. We constrain the amount of primordial helium to be Y p = 0.225 ± 0.034, and measure no variation in the fine structure constant α since recombination, with α/α 0 = 1.004 ± 0.005. We also find no evidence for any running of the scalar spectral index, dn s /d ln k = −0.004 ± 0.012.

Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased towards detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues towards truly quantitative and reproducible metagenomics measurements.

Background Surgery is the main modality of cure for solid cancers and was prioritised to continue during COVID-19 outbreaks. This study aimed to identify immediate areas for system strengthening by comparing the delivery of elective cancer surgery during the COVID-19 pandemic in periods of lockdown versus light restriction. Methods This international, prospective, cohort study enrolled 20 006 adult (≥18 years) patients from 466 hospitals in 61 countries with 15 cancer types, who had a decision for curative surgery during the COVID-19 pandemic and were followed up until the point of surgery or cessation of follow-up (Aug 31, 2020). Average national Oxford COVID-19 Stringency Index scores were calculated to define the government response to COVID-19 for each patient for the period they awaited surgery, and classified into light restrictions (index <20), moderate lockdowns (20–60), and full lockdowns (>60). The primary outcome was the non-operation rate (defined as the proportion of patients who did not undergo planned surgery). Cox proportional-hazards regression models were used to explore the associations between lockdowns and non-operation. Intervals from diagnosis to surgery were compared across COVID-19 government response index groups. This study was registered at ClinicalTrials.gov , NCT04384926 . Findings Of eligible patients awaiting surgery, 2003 (10·0%) of 20 006 did not receive surgery after a median follow-up of 23 weeks (IQR 16–30), all of whom had a COVID-19-related reason given for non-operation. Light restrictions were associated with a 0·6% non-operation rate (26 of 4521), moderate lockdowns with a 5·5% rate (201 of 3646; adjusted hazard ratio [HR] 0·81, 95% CI 0·77–0·84; p<0·0001), and full lockdowns with a 15·0% rate (1775 of 11 827; HR 0·51, 0·50–0·53; p<0·0001). In sensitivity analyses, including adjustment for SARS-CoV-2 case notification rates, moderate lockdowns (HR 0·84, 95% CI 0·80–0·88; p<0·001), and full lockdowns (0·57, 0·54–0·60; p<0·001), remained independently associated with non-operation. Surgery beyond 12 weeks from diagnosis in patients without neoadjuvant therapy increased during lockdowns (374 [9·1%] of 4521 in light restrictions, 317 [10·4%] of 3646 in moderate lockdowns, 2001 [23·8%] of 11 827 in full lockdowns), although there were no differences in resectability rates observed with longer delays. Interpretation Cancer surgery systems worldwide were fragile to lockdowns, with one in seven patients who were in regions with full lockdowns not undergoing planned surgery and experiencing longer preoperative delays. Although short-term oncological outcomes were not compromised in those selected for surgery, delays and non-operations might lead to long-term reductions in survival. During current and future periods of societal restriction, the resilience of elective surgery systems requires strengthening, which might include...

Clostridioides difficile is a bacterial pathogen that causes a range of clinical disease from mild to moderate diarrhea, pseudomembranous colitis, and toxic megacolon. Typically, C. difficile infections (CDIs) occur after antibiotic treatment, which alters the gut microbiota, decreasing colonization resistance against C. difficile. Disease is mediated by two large toxins and the expression of their genes is induced upon nutrient depletion via the alternative sigma factor TcdR. Here, we use tcdR mutants in two strains of C. difficile and omics to investigate how toxin-induced inflammation alters C. difficile metabolism, tissue gene expression and the gut microbiota, and to determine how inflammation by the host may be beneficial to C. difficile. We show that C. difficile metabolism is significantly different in the face of inflammation, with changes in many carbohydrate and amino acid uptake and utilization pathways. Host gene expression signatures suggest that degradation of collagen and other components of the extracellular matrix by matrix metalloproteinases is a major source of peptides and amino acids that supports C. difficile growth in vivo. Lastly, the inflammation induced by C. difficile toxin activity alters the gut microbiota, excluding members from the genus Bacteroides that are able to utilize the same essential nutrients released from collagen degradation.

We present a detection of the Sunyaev-Zel'dovich (SZ) decrement associated with the Luminous Red Galaxy (LRG) sample of the Sloan Digital Sky Survey. The SZ data come from 148 GHz maps of the equatorial region made by the Atacama Cosmology Telescope (ACT). The LRG sample is divided by luminosity into four bins, and estimates for the central SZ temperature decrement are calculated through a stacking process. We detect and account for a bias of the SZ signal due to weak radio sources. We use numerical simulations to relate the observed decrement to Y 200 and clustering properties to relate the galaxy luminosity to halo mass. We also use a relation between brightest cluster galaxy luminosity and cluster mass based on stacked gravitational lensing measurements to estimate the characteristic halo masses. The masses are found to be around 10 14 M ⊙ .

To survive, plants and animals must continually defend against pathogenic microbes that would invade and disrupt their tissues. Yet they do not attempt to extirpate all microbes. Instead, they tolerate and even encourage the growth of commensal microbes, which compete with pathogens for resources and via direct inhibition. We argue that hosts have evolved to cooperate with commensals in order to enhance the pathogen resistance this competition provides. We briefly describe competition between commensals and pathogens within the host, consider how natural selection might favour hosts that tilt this competition in favour of commensals, and describe examples of extant host traits that may serve this purpose. Finally, we consider ways that this cooperative immunity may have facilitated the adaptive evolution of non-pathogen-related host traits. On the basis of these observations, we argue that pathogen resistance vies with other commensal-provided benefits for being the principal evolutionary advantage provided by the microbiome to host lineages across the tree of life. This article is part of the theme issue ‘The role of the microbiome in host evolution’.

Abstract Human challenge trials (HCTs) have been proposed as a means to accelerate SARS-CoV-2 vaccine development. We identify and discuss three potential use cases of HCTs in the current pandemic: evaluating efficacy, converging on correlates of protection, and improving understanding of pathogenesis and the human immune response. We outline the limitations of HCTs and find that HCTs are likely to be most useful for vaccine candidates currently in preclinical stages of development. We conclude that, while currently limited in their application, there are scenarios in which HCTs would be extremely beneficial. Therefore, the option of conducting HCTs to accelerate SARS-CoV-2 vaccine development should be preserved. As HCTs require many months of preparation, we recommend an immediate effort to (1) establish guidelines for HCTs for COVID-19; (2) take the first steps toward HCTs, including preparing challenge virus and making preliminary logistical arrangements; and (3) commit to periodically re-evaluating the utility of HCTs.