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In temperate countries, influenza outbreaks are well correlated to seasonal changes in temperature and absolute humidity. However, tropical countries have much weaker annual climate cycles, and outbreaks show less seasonality and are more difficult to explain with environmental correlations. Here, we use convergent cross mapping, a robust test for causality that does not require correlation, to test alternative hypotheses about the global environmental drivers of influenza outbreaks from country-level epidemic time series. By moving beyond correlation, we show that despite the apparent differences in outbreak patterns between temperate and tropical countries, absolute humidity and, to a lesser extent, temperature drive influenza outbreaks globally. We also find a hypothesized U-shaped relationship between absolute humidity and influenza that is predicted by theory and experiment, but hitherto has not been documented at the population level. The balance between positive and negative effects of absolute humidity appears to be mediated by temperature, and the analysis reveals a key threshold around 75°F. The results indicate a unified explanation for environmental drivers of influenza that applies globally.epidemiology | empirical dynamic modeling | nonlinear dynamics | state-dependence | physical-biological coupling A diverse group of drivers and mechanisms has been put forward to explain the wintertime occurrence of seasonal influenza outbreaks. Laboratory experiments show that relative humidity controls droplet size and aerosol transmission rates (1). Experiments with mammalian models showed that viral shedding by hosts increases at low temperature (2). Strong laboratory evidence has emerged that absolute humidity has a controlling effect on airborne influenza transmission (3).Nevertheless, questions remain as to how these potential causal agents are expressed at the population level as epidemic control variables. At the population level, environmental factors covary, multiple mechanisms can coact, and infection dynamics are influenced by many other important processes (4), such as human crowding, rapid viral evolution, and international travel patterns. Perhaps not surprisingly, statistical analyses of population level data have produced contradictory results. Although correlations between influenza incidence and both temperature and absolute humidity are easy to find in temperate countries (5) and individual US states (6), such associations are weak or altogether absent in data from tropical countries (5).Here we use new methods appropriate for disease dynamics to identify the causal drivers of influenza acting at the population level. Using time series data across countries and latitudes, we find that absolute humidity drives influenza across latitudes, and that this effect is modulated by temperature. At low temperatures, absolute humidity negatively affects influenza incidence (drier conditions improve survival of the influenza virus when it is cold), but at high temperatures, absolute humidity positively a...

SUMMARY Mapping host-pathogen interactions has proven instrumental for understanding how viruses manipulate host machinery and how numerous cellular processes are regulated. DNA viruses such as herpesviruses have relatively large coding capacity and thus can target an extensive network of cellular proteins. To identify the host proteins hijacked by this pathogen, we systematically affinity tagged and purified all 89 proteins of Kaposi’s sarcoma-associated herpesvirus (KSHV) from human cells. Mass spectrometry of this material identified over 500 virus-host interactions. KSHV causes AIDS-associated cancers and its interaction network is enriched for proteins linked to cancer and overlaps with proteins that are also targeted by HIV-1. We found that the conserved KSHV protein ORF24 binds to RNA polymerase II and brings it to viral late promoters by mimicking and replacing cellular TATA-box-binding protein (TBP). This is required for herpesviral late gene expression, a complex and poorly understood phase of the viral lifecycle.

This is a PDF file of a peer-reviewed paper that has been accepted for publication. Although unedited, the content has been subjected to preliminary formatting. Nature is providing this early version of the typeset paper as a service to our authors and readers. The text and figures will undergo copyediting and a proof review before the paper is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.

Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.

The recently emerged SARS-CoV-2 Omicron variant harbors 37 amino acid substitutions in the spike (S) protein, 15 of which are in the receptor-binding domain (RBD), thereby raising concerns about the effectiveness of available vaccines and antibody therapeutics. Here, we show that the Omicron RBD binds to human ACE2 with enhanced affinity relative to the Wuhan-Hu-1 RBD and acquires binding to mouse ACE2. Severe reductions of plasma neutralizing activity were observed against Omicron compared to the ancestral pseudovirus for vaccinated and convalescent individuals. Most (26 out of 29) receptor-binding motif (RBM)-directed monoclonal antibodies (mAbs) lost in vitro neutralizing activity against Omicron, with only three mAbs, including the ACE2-mimicking S2K146 mAb, retaining unaltered potency. Furthermore, a fraction of broadly neutralizing sarbecovirus mAbs recognizing antigenic sites outside the RBM, including sotrovimab, S2X259 and S2H97, neutralized Omicron. The magnitude of Omicron-mediated immune evasion and the acquisition of binding to mouse ACE2 mark a major SARS-CoV-2 mutational shift. Broadly neutralizing sarbecovirus mAbs recognizing epitopes conserved among SARS-CoV-2 variants and other sarbecoviruses may prove key to controlling the ongoing pandemic and future zoonotic spillovers.

Although macrophages are armed with potent antibacterial functions, Mycobacterium tuberculosis (Mtb) replicates inside these innate immune cells. Determinants of macrophage intrinsic bacterial control, and the Mtb strategies to overcome them, are poorly understood. To further study these processes, we used an affinity tag purification mass spectrometry (AP-MS) approach to identify 187 Mtb-human protein-protein interactions (PPIs) involving 34 secreted Mtb proteins. This interaction map revealed two factors involved in Mtb pathogenesis-the secreted Mtb protein, LpqN, and its binding partner, the human ubiquitin ligase CBL. We discovered that an lpqN Mtb mutant is attenuated in macrophages, but growth is restored when CBL is removed. Conversely, Cbl macrophages are resistant to viral infection, indicating that CBL regulates cell-intrinsic polarization between antibacterial and antiviral immunity. Collectively, these findings illustrate the utility of this Mtb-human PPI map for developing a deeper understanding of the intricate interactions between Mtb and its host.

INTRODUCTIONSARS-CoV-2 evolution presents an ongoing challenge to public health. Tens of thousands of mutations have arisen in the SARS-CoV-2 genome as the pandemic has progressed. Understanding the relative importance of mutations in viral proteins, particularly those of relevance for antiviral immunity, is key to allocating preparedness efforts. Mutations in the viral Spike protein have received particular attention because Spike is the target of antibody-mediated immunity and is the primary antigen in current vaccines (1). As of December 1st, 2021, there are 10,381 distinct amino acid substitutions, insertions, or deletions in Spike sequences from the GISAID database (2). These mutations occur at all but one position in the protein, in different combinations, creating over 160,000 unique Spike protein sequences. A small subset of these mutations are components of "Variants Being Monitored" (VBMs), "Variants of Interest" (VOIs) or "Variants of Concern" (VOCs), as classified by the United States Centers for Disease Control (CDC) (3). The distinction between VOIs and the higher alert VOCs is whether a negative clinical impact is suspected or confirmed. VBMs are variants that would be classified as VOCs if not for low prevalence.Early statistical and algorithmic identification of the key Spike amino acid changes contributing to future putative VBM/VOI/VOCs are of clear benefit to public health strategy. Such predictions could enhance the identification of vulnerabilities for antibody-based therapeutics, vaccines, and diagnostics. Predicting future successful mutations would extend the time available to develop proactive responses at earlier stages of spread. It would also complement existing forecasting efforts which seek to predict overall SARS-CoV-2 incidence, hospitalizations, and death over time (4-6). Focus on the success of individual mutations rather than genomic variants also facilitates longer-term forecasting. The