Homoeologs: What Are They and How Do We Infer Them?

The evolutionary history of nearly all flowering plants includes a polyploidization event. Homologous genes resulting from allopolyploidy are commonly referred to as 'homoeologs', although this term has not always been used precisely or consistently in the literature. With several allopolyploid genome sequencing projects under way, there is a pressing need for computational methods for homoeology inference. Here we review the definition of homoeology in historical and modern contexts and propose a precise and testable definition highlighting the connection between homoeologs and orthologs. In the second part, we survey experimental and computational methods of homoeolog inference, considering the strengths and limitations of each approach. Establishing a precise and evolutionarily meaningful definition of homoeology is essential for understanding the evolutionary consequences of polyploidization

Assigning confidence scores to homoeologs using fuzzy logic.

In polyploid genomes, homoeologs are a specific subtype of homologs, and can be thought of as orthologs between subgenomes. In Orthologous MAtrix, we infer homoeologs in three polyploid plant species: upland cotton (Gossypium hirsutum), rapeseed (Brassica napus), and bread wheat (Triticum aestivum). While we can typically recognize the features of a "good" homoeolog prediction (a consistent evolutionary distance, high synteny, and a one-to-one relationship), none of them is a hard-fast criterion. We devised a novel fuzzy logic-based method to assign confidence scores to each pair of predicted homoeologs. We inferred homoeolog pairs and used the new and improved method to assign confidence scores, which ranged from 0 to 100. Most confidence scores were between 70 and 100, but the distribution varied between genomes. The new confidence scores show an improvement over our previous method and were manually evaluated using a subset from various confidence ranges

Identifying orthologs with OMA: A primer.

The Orthologous Matrix (OMA) is a method and database that allows users to identify orthologs among many genomes. OMA provides three different types of orthologs: pairwise orthologs, OMA Groups and Hierarchical Orthologous Groups (HOGs). This Primer is organized in two parts. In the first part, we provide all the necessary background information to understand the concepts of orthology, how we infer them and the different subtypes of orthology in OMA, as well as what types of analyses they should be used for. In the second part, we describe protocols for using the OMA browser to find a specific gene and its various types of orthologs. By the end of the Primer, readers should be able to (i) understand homology and the different types of orthologs reported in OMA, (ii) understand the best type of orthologs to use for a particular analysis; (iii) find particular genes of interest in the OMA browser; and (iv) identify orthologs for a given gene. The data can be freely accessed from the OMA browser at https://omabrowser.org

OMA orthology in 2021: website overhaul, conserved isoforms, ancestral gene order and more.

OMA is an established resource to elucidate evolutionary relationships among genes from currently 2326 genomes covering all domains of life. OMA provides pairwise and groupwise orthologs, functional annotations, local and global gene order conservation (synteny) information, among many other functions. This update paper describes the reorganisation of the database into gene-, group- and genome-centric pages. Other new and improved features are detailed, such as reporting of the evolutionarily best conserved isoforms of alternatively spliced genes, the inferred local order of ancestral genes, phylogenetic profiling, better cross-references, fast genome mapping, semantic data sharing via RDF, as well as a special coronavirus OMA with 119 viruses from the Nidovirales order, including SARS-CoV-2, the agent of the COVID-19 pandemic. We conclude with improvements to the documentation of the resource through primers, tutorials and short videos. OMA is accessible at https://omabrowser.org

The HI/OH/Recombination line survey of the inner Milky Way (THOR): data release 2 and Hi overview

Context. The Galactic plane has been observed extensively by a large number of Galactic plane surveys from infrared to radio wavelengths at an angular resolution below 40". However, a 21 cm line and continuum survey with comparable spatial resolution is still missing. Aims. The first half of THOR data (l = 14.0 37.9, and l = 47.1 51.2, |b| < 1.25) has been published in our data release 1 paper (Beuther et al. 2016). With this data release 2 paper, we publish all the remaining spectral line data and Stokes I continuum data with high angular resolution (1000–4000) including a new H i dataset for the whole THOR survey region (l = 14.0 67.4 and |b| < 1.25). As we have published the results of OH lines and continuum emission elsewhere, we concentrate on the H i analysis in this paper. Methods. With the Karl G. Jansky Very Large Array (VLA) in C-configuration, we observed a large portion of the first Galactic quadrant achieving an angular resolution of < 40. At L Band, the WIDAR correlator at the VLA was set to cover the 21 cm H i line, four OH transitions, a series of Hn↵ radio recombination lines (RRLs; n = 151 to 186), and eight 128 MHz wide continuum spectral windows (SPWs) simultaneously. Results. We publish all OH and RRL data from the C-configuration observations, and a new H i dataset combining VLA C+D+GBT (VLA D-configuration and GBT data are from the VLA Galactic Plane Survey, Stil et al. 2006) for the whole survey. The H i emission shows clear filamentary substructures at negative velocities with low velocity crowding. The emission at positive velocities is more smeared-out likely due to higher spatial and velocity crowding of structures at the positive velocities. Comparing to the spiral arm model of the Milky Way, the atomic gas follows the Sagittarius and Perseus Arm well but with significant material in the inter-arm regions. With the C-configuration-only H i+continuum data, we produced a H i optical depth map of the THOR areal coverage from 228 absorption spectra with the nearest-neighbor method. With this ⌧ map, we corrected the H i emission for optical depth and the derived column density is 38% higher than the column density with optically thin assumption. The total H i mass with optical depth correction in the survey region is 4.7⇥108 M, 31% more than the mass derived assuming the emission is optically thin. If we apply this 31% correction to the whole Milky Way, the total atomic gas mass would be 9.4–10.5⇥109 M. Comparing the H i with existing CO data, we find a significant increase in the atomic-to-molecular gas ration from the spiral arms to the inter-arm regions. Conclusions. The high sensitivity and resolution THOR H i dataset provides an important new window on the physical and kinematic properties of gas in the inner Galaxy. Although the optical depth we derive is a lower limit, our study shows that the optical depth correction is significant for H i column density and mass estimation. Together with the OH, RRL and continuum emission from the THOR survey, these new H i data provide the basis for high angular-resolution studies of the interstellar medium (ISM) in different phases

The Quest for Orthologs benchmark service and consensus calls in 2020.

The identification of orthologs-genes in different species which descended from the same gene in their last common ancestor-is a prerequisite for many analyses in comparative genomics and molecular evolution. Numerous algorithms and resources have been conceived to address this problem, but benchmarking and interpreting them is fraught with difficulties (need to compare them on a common input dataset, absence of ground truth, computational cost of calling orthologs). To address this, the Quest for Orthologs consortium maintains a reference set of proteomes and provides a web server for continuous orthology benchmarking (http://orthology.benchmarkservice.org). Furthermore, consensus ortholog calls derived from public benchmark submissions are provided on the Alliance of Genome Resources website, the joint portal of NIH-funded model organism databases

Feedback in W94A diagnosed with Radio Recombination Lines and Models

We present images of radio recombination lines (RRLs) at wavelengths around 18 cm from the star-forming region W49A to determine the kinematics of ionized gas in the THOR survey (The Hi/OH/Recombination line survey of the inner Milky Way) at an angular resolution of 16:08 X 13:08. The distribution of ionized gas appears to be affected by feedback processes from the star clusters inW49A. The velocity structure of the RRLs shows a complex behaviour with respect to the molecular gas. We find a shell-like distribution of ionized gas as traced by RRL emission surrounding the central cluster of OB stars in W49A. We describe the evolution of the shell with the recent feedback model code warpfield that includes the important physical processes and has previously been applied to the 30 Doradus region in the Large Magellanic Cloud. The cloud structure and dynamics of W49A are in agreement with a feedbackdriven shell that is re-collapsing. The shell may have triggered star formation in other parts of W49A. We suggest that W49A is a potential candidate for star formation regulated by feedback-driven and re-collapsing shells

Accelarated immune ageing is associated with COVID-19 disease severity

Background The striking increase in COVID-19 severity in older adults provides a clear example of immunesenescence, the age-related remodelling of the immune system. To better characterise the association between convalescent immunesenescence and acute disease severity, we determined the immune phenotype of COVID-19 survivors and non-infected controls. Results We performed detailed immune phenotyping of peripheral blood mononuclear cells isolated from 103 COVID-19 survivors 3–5 months post recovery who were classified as having had severe (n = 56; age 53.12 ± 11.30 years), moderate (n = 32; age 52.28 ± 11.43 years) or mild (n = 15; age 49.67 ± 7.30 years) disease and compared with age and sex-matched healthy adults (n = 59; age 50.49 ± 10.68 years). We assessed a broad range of immune cell phenotypes to generate a composite score, IMM-AGE, to determine the degree of immune senescence. We found increased immunesenescence features in severe COVID-19 survivors compared to controls including: a reduced frequency and number of naïve CD4 and CD8 T cells (p < 0.0001); increased frequency of EMRA CD4 (p < 0.003) and CD8 T cells (p < 0.001); a higher frequency (p < 0.0001) and absolute numbers (p < 0.001) of CD28−ve CD57+ve senescent CD4 and CD8 T cells; higher frequency (p < 0.003) and absolute numbers (p < 0.02) of PD-1 expressing exhausted CD8 T cells; a two-fold increase in Th17 polarisation (p < 0.0001); higher frequency of memory B cells (p < 0.001) and increased frequency (p < 0.0001) and numbers (p < 0.001) of CD57+ve senescent NK cells. As a result, the IMM-AGE score was significantly higher in severe COVID-19 survivors than in controls (p < 0.001). Few differences were seen for those with moderate disease and none for mild disease. Regression analysis revealed the only pre-existing variable influencing the IMM-AGE score was South Asian ethnicity ( = 0.174, p = 0.043), with a major influence being disease severity ( = 0.188, p = 0.01). Conclusions Our analyses reveal a state of enhanced immune ageing in survivors of severe COVID-19 and suggest this could be related to SARS-Cov-2 infection. Our data support the rationale for trials of anti-immune ageing interventions for improving clinical outcomes in these patients with severe disease

Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK: a prospective multicentre cohort study

Background: Sleep disturbance is common following hospital admission both for COVID-19 and other causes. The clinical associations of this for recovery after hospital admission are poorly understood despite sleep disturbance contributing to morbidity in other scenarios. We aimed to investigate the prevalence and nature of sleep disturbance after discharge following hospital admission for COVID-19 and to assess whether this was associated with dyspnoea. Methods: CircCOVID was a prospective multicentre cohort substudy designed to investigate the effects of circadian disruption and sleep disturbance on recovery after COVID-19 in a cohort of participants aged 18 years or older, admitted to hospital for COVID-19 in the UK, and discharged between March, 2020, and October, 2021. Participants were recruited from the Post-hospitalisation COVID-19 study (PHOSP-COVID). Follow-up data were collected at two timepoints: an early time point 2–7 months after hospital discharge and a later time point 10–14 months after hospital discharge. Sleep quality was assessed subjectively using the Pittsburgh Sleep Quality Index questionnaire and a numerical rating scale. Sleep quality was also assessed with an accelerometer worn on the wrist (actigraphy) for 14 days. Participants were also clinically phenotyped, including assessment of symptoms (ie, anxiety [Generalised Anxiety Disorder 7-item scale questionnaire], muscle function [SARC-F questionnaire], dyspnoea [Dyspnoea-12 questionnaire] and measurement of lung function), at the early timepoint after discharge. Actigraphy results were also compared to a matched UK Biobank cohort (non-hospitalised individuals and recently hospitalised individuals). Multivariable linear regression was used to define associations of sleep disturbance with the primary outcome of breathlessness and the other clinical symptoms. PHOSP-COVID is registered on the ISRCTN Registry (ISRCTN10980107). Findings: 2320 of 2468 participants in the PHOSP-COVID study attended an early timepoint research visit a median of 5 months (IQR 4–6) following discharge from 83 hospitals in the UK. Data for sleep quality were assessed by subjective measures (the Pittsburgh Sleep Quality Index questionnaire and the numerical rating scale) for 638 participants at the early time point. Sleep quality was also assessed using device-based measures (actigraphy) a median of 7 months (IQR 5–8 months) after discharge from hospital for 729 participants. After discharge from hospital, the majority (396 [62%] of 638) of participants who had been admitted to hospital for COVID-19 reported poor sleep quality in response to the Pittsburgh Sleep Quality Index questionnaire. A comparable proportion (338 [53%] of 638) of participants felt their sleep quality had deteriorated following discharge after COVID-19 admission, as assessed by the numerical rating scale. Device-based measurements were compared to an age-matched, sex-matched, BMI-matched, and time from discharge-matched UK Biobank cohort who had recently been admitted to hospital. Compared to the recently hospitalised matched UK Biobank cohort, participants in our study slept on average 65 min (95% CI 59 to 71) longer, had a lower sleep regularity index (–19%; 95% CI –20 to –16), and a lower sleep efficiency (3·83 percentage points; 95% CI 3·40 to 4·26). Similar results were obtained when comparisons were made with the non-hospitalised UK Biobank cohort. Overall sleep quality (unadjusted effect estimate 3·94; 95% CI 2·78 to 5·10), deterioration in sleep quality following hospital admission (3·00; 1·82 to 4·28), and sleep regularity (4·38; 2·10 to 6·65) were associated with higher dyspnoea scores. Poor sleep quality, deterioration in sleep quality, and sleep regularity were also associated with impaired lung function, as assessed by forced vital capacity. Depending on the sleep metric, anxiety mediated 18–39% of the effect of sleep disturbance on dyspnoea, while muscle weakness mediated 27–41% of this effect. Interpretation: Sleep disturbance following hospital admission for COVID-19 is associated with dyspnoea, anxiety, and muscle weakness. Due to the association with multiple symptoms, targeting sleep disturbance might be beneficial in treating the post-COVID-19 condition. Funding: UK Research and Innovation, National Institute for Health Research, and Engineering and Physical Sciences Research Council

Constraints on cosmic strings using data from the first Advanced LIGO observing run

Cosmic strings are topological defects which can be formed in grand unified theory scale phase transitions in the early universe. They are also predicted to form in the context of string theory. The main mechanism for a network of Nambu-Goto cosmic strings to lose energy is through the production of loops and the subsequent emission of gravitational waves, thus offering an experimental signature for the existence of cosmic strings. Here we report on the analysis conducted to specifically search for gravitational-wave bursts from cosmic string loops in the data of Advanced LIGO 2015-2016 observing run (O1). No evidence of such signals was found in the data, and as a result we set upper limits on the cosmic string parameters for three recent loop distribution models. In this paper, we initially derive constraints on the string tension Gμ and the intercommutation probability, using not only the burst analysis performed on the O1 data set but also results from the previously published LIGO stochastic O1 analysis, pulsar timing arrays, cosmic microwave background and big-bang nucleosynthesis experiments. We show that these data sets are complementary in that they probe gravitational waves produced by cosmic string loops during very different epochs. Finally, we show that the data sets exclude large parts of the parameter space of the three loop distribution models we consider