A Phase 1a/1b Clinical Trial Design to Assess Safety, Acceptability, Pharmacokinetics and Tolerability of Intranasal Q-Griffithsin for COVID-19 Prophylaxis

Background: The COVID-19 pandemic remains an ongoing threat to global public health. Q-Griffithsin (Q-GRFT) is a lectin that has demonstrated potent broad-spectrum inhibitory activity in preclinical studies in models of Nipah virus and the beta coronaviruses SARS-CoV, MERS-CoV, and SARS-CoV-2. Methods: Here, we propose a clinical trial design to test the safety, pharmacokinetics (PK), and tolerability of intranasally administered Q-GRFT for the prevention of SARS-CoV-2 infection as a prophylaxis strategy. The initial Phase 1a study will assess the safety and PK of a single dose of intranasally administered Q-GRFT. If found safe, the safety, PK, and tolerability of multiple doses of intranasal Q-GRFT will be assessed in a Phase 1b study. Group 1 participants will receive 3 mg of intranasal Q-GRFT (200 μL/nostril) once daily for 7 days. If this dose is tolerated, participants will be enrolled in Group 2 to receive 3 mg twice daily for 7 days. Secondary endpoints of the study will be user perceptions, acceptability, and the impact of product use on participants’ olfactory sensation and quality of life. Discussion: Results from this study will support further development of Q-GRFT as a prophylactic against respiratory viral infections in future clinical trials

Reversing hypoxic cell chemoresistance in vitro using genetic and small molecule approaches targeting hypoxia inducible factor-1

ABSTRACT The resistance of hypoxic cells to conventional chemotherapy is well documented. Using both adenovirus-mediated gene delivery and small molecules targeting hypoxia-inducible factor-1 (HIF-1), we evaluated the impact of HIF-1 inhibition on the sensitivity of hypoxic tumor cells to etoposide. The genetic therapy exploited a truncated HIF-1␣ protein that acts as a dominant-negative HIF-1␣ (HIF-1␣-no-TAD). Its functionality was validated in six human tumor cell lines using HIF-1 reporter assays. An EGFP-fused protein demonstrated that the dominant-negative HIF-1␣ was nucleus-localized and constitutively expressed irrespective of oxygen tension. The small molecules studied were quinocarmycin monocitrate (KW2152), its analog 7-cyanoquinocarcinol (DX-52-1), and topotecan. DX-52-1 and topotecan have been previously established as HIF-1 inhibitors. HT1080 and HCT116 cells were treated with either AdHIF-1␣-no-TAD or nontoxic concentrations (0.1 M; ϽIC 10 ) of KW2152 and DX-52-1 and exposed to etoposide in air or anoxia (Ͻ0.01% oxygen). Topotecan inhibited HIF-1 activity only at cytotoxic concentrations and was not used in the combination study. Etoposide IC 50 values in anoxia were 3-fold higher than those in air for HT1080 (2.2 Ϯ 0.3 versus 0.7 Ϯ 0.2 M) and HCT116 (9 Ϯ 4 versus 3 Ϯ 2 M) cells. KW2152 and DX-52-1 significantly reduced the anoxic etoposide IC 50 in HT1080 cells, whereas only KW2152 yielded sensitization in HCT116 cells. In contrast, AdHIF-1␣-no-TAD (multiplicity of infection 50) ablated the anoxic resistance in both cell lines (IC 50 values: HT1080, 0.7 Ϯ 0.04 M; HCT116, 3 Ϯ 1 M). HIF-1␣-no-TAD expression inhibited HIF-1-mediated down-regulation of the proapoptotic protein Bid under anoxia. These data support the potential development of HIF-1 targeted approaches in combination with chemotherapy, where hypoxic cell resistance contributes to treatment failure

Patterns of Gene Flow Define Species of Thermophilic Archaea

A genomic view of speciation in Archaea shows higher rates of gene flow within coexisting microbial species than between them

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey

Background: Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. Methods: Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated crosssectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. Findings: Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29–0·54) to 0·06% (0·04–0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17–24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17–24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45–68%, dependent on calendar time. Interpretation: Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards