Aridity could have driven the local extinction of a common and multivoltine butterfly

1.Identifying which species are being negatively impacted by climate change and the mechanisms driving their decline is essential to effectively protect biodiversity. 2.Coenonympha pamphilus is a common and generalist butterfly, widely distributed throughout the Western Palearctic, being multivoltine in southern Europe. Previous studies indicate that it will not be substantially affected by climate change; however, it has seemingly disappeared from the southeast of the Iberian Peninsula in the last decades. 3. Here, we aim to determine if it has effectively disappeared from this area, as well as identify the environmental conditions limiting its distribution and the potential causes behind this a priori local extinction. 4.We downloaded all the occurrence records of C. pamphilus and analysed their spatial and temporal trends. To identify the climatic variables driving the distribution of this butterfly in the Iberian Peninsula, we performed an ensemble species distribution model (SDM), combining 600 individual models produced with 6 algorithms. 5.We confirmed that C. pamphilus has not been observed in the southeast of the Iberian Peninsula since 2008. Aridity was the main factor limiting the distribution of C. pamphilus in our ensemble SDM, with areas with high aridity being unsuitable for this species. 6. We hypothesise that multivoltinism is the mechanism driving this local extirpation, as high aridity is causing host plants (Poaceae) to wither prematurely, precluding the development of the second and/or third generations of the butterfly. Even though generalist species are theoretically more resilient to climate change, other traits such as multivoltinism may increase their vulnerability and need to be further investigated

Harmonization of quality metrics and power calculation in multi-omic studies

Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data. Multi-omics studies are popular but lack rigorous criteria for experimental design. We define Figures of Merit across omics to comparatively describe their performance, and present new algorithms for sample size calculation in multi-omics experiments aiming either at feature selection or sample classification.Analytical BioScience

Origin of congenital coronary arterio-ventricular fistulae from anomalous epicardial and myocardial development.

Coronary Artery Fistulae (CAFs) are cardiac congenital anomalies consisting of an abnormal communication of a coronary artery with either a cardiac chamber or another cardiac vessel. In humans, these congenital anomalies can lead to complications such as myocardial hypertrophy, endocarditis, heart dilatation, and failure. Unfortunately, despite their clinical relevance, the aetiology of CAFs remains unknown. In this work, we have used two different species (mouse and avian embryos) to experimentally model CAFs morphogenesis. Both conditional Itga4 (alpha 4 integrin) epicardial deletion in mice and cryocauterisation of chick embryonic hearts disrupted epicardial development and ventricular wall growth, two essential events in coronary embryogenesis. Our results suggest that myocardial discontinuities in the embryonic ventricular wall promote the early contact of the endocardium with epicardial-derived coronary progenitors at the cardiac surface, leading to ventricular endocardial extrusion, precocious differentiation of coronary smooth muscle cells, and the formation of pouch-like aberrant coronary-like structures in direct connection with the ventricular lumen. The structure of these CAF-like anomalies was compared with histopathological data from a human CAF. Our results provide relevant information for the early diagnosis of these congenital anomalies and the molecular mechanisms that regulate their embryogenesis.The authors thank Dr. A. Rojas (CABIMER, Sevilla, Spain) and Prof. Thalia Papayannopoulou (University of Washington, WA, USA) for sharing with us the G2- Gata4-Cre and Itga4-floxed mouse lines, respectively. We also thank Vanessa Benhamo (Institut Imagine) for her expert support with HREM. Finally, we thank all members of “DeCA” laboratory (University of Málaga, Málaga, Spain), and the “Heart Morphogenesis” laboratory (Institut Imagine and Institut Pasteur, Paris, France) for their help and fruitful discussions on this paper. This work was supported by the Spanish Ministry of Science, R+D+i National Programme [grants RTI2018-095410-RBI00 and PID2021-122626-OB-I00], Spanish Ministry of Science-ISCIII [grant number RD16/0011/0030], and University of Málaga [grant number UMA18-FEDERJA-146] to [JMPP]; Consejería de Salud y Familias, Junta de Andalucía [grant number PIER-0084- 2019] to [JAGD]; University of Málaga [grant number I Plan Propio-UMA-A.4] to [ARV]; Spanish Ministry of Science, Innovation, and Universities (MCIU) (CIBER CV) [grant numbers PID2019-104776RB-I00 and CB16/11/00399] to [JLDLP].S

Non-HLA genes PTPN22, CDK6 and PADI4 are associated with specific autoantibodies in HLA-defined subgroups of rheumatoid arthritis

Introduction: Genetic susceptibility to complex diseases has been intensively studied during the last decade, yet only signals with small effect have been found leaving open the possibility that subgroups within complex traits show stronger association signals. In rheumatoid arthritis (RA), autoantibody production serves as a helpful discriminator in genetic studies and today anti-citrullinated cyclic peptide (anti-CCP) antibody positivity is employed for diagnosis of disease. The HLA-DRB1 locus is known as the most important genetic contributor for the risk of RA, but is not sufficient to drive autoimmunity and additional genetic and environmental factors are involved. Hence, we addressed the association of previously discovered RA loci with disease-specific autoantibody responses in RA patients stratified by HLA-DRB1*04. Methods: We investigated 2178 patients from three RA cohorts from Sweden and Spain for 41 genetic variants and four autoantibodies, including the generic anti-CCP as well as specific responses towards citrullinated peptides from vimentin, alpha-enolase and type II collagen. Results: Our data demonstrated different genetic associations of autoantibody-positive disease subgroups in relation to the presence of DRB1*04. Two specific subgroups of autoantibody-positive RA were identified. The SNP in PTPN22 was associated with presence of anti-citrullinated enolase peptide antibodies in carriers of HLA-DRB1*04 (Cochran-Mantel-Haenszel test P = 0.0001, P corrected <0.05), whereas SNPs in CDK6 and PADI4 were associated with anti-CCP status in DRB1*04 negative patients (Cochran-Mantel-Haenszel test P = 0.0004, P corrected <0.05 for both markers). Additionally we see allelic correlation with autoantibody titers for PTPN22 SNP rs2476601 and anti-citrullinated enolase peptide antibodies in carriers of HLA-DRB1*04 (Mann Whitney test P = 0.02) and between CDK6 SNP rs42041 and anti-CCP in non-carriers of HLA-DRB1*04 (Mann Whitney test P = 0.02). Conclusion: These data point to alternative pathways for disease development in clinically similar RA subgroups and suggest an approach for study of genetic complexity of disease with strong contribution of HLA

Population Genetic Structure of the Grasshopper Eyprepocnemis plorans in the South and East of the Iberian Peninsula

The grasshopper Eyprepocnemis plorans subsp. plorans harbors a very widespread polymorphism for supernumerary (B) chromosomes which appear to have arisen recently. These chromosomes behave as genomic parasites because they are harmful for the individuals carrying them and show meiotic drive in the initial stages of population invasion. The rapid increase in B chromosome frequency at intrapopulation level is thus granted by meiotic drive, but its spread among populations most likely depends on interpopulation gene flow. We analyze here the population genetic structure in 10 natural populations from two regions (in the south and east) of the Iberian Peninsula. The southern populations were coastal whereas the eastern ones were inland populations located at 260–655 m altitude. The analysis of 97 ISSR markers revealed significant genetic differentiation among populations (average GST = 0.129), and the Structure software and AMOVA indicated a significant genetic differentiation between southern and eastern populations. There was also significant isolation by distance (IBD) between populations. Remarkably, these results were roughly similar to those found when only the markers showing low or no dropout were included, suggesting that allelic dropout had negligible effects on population genetic analysis. We conclude that high gene flow helped this parasitic B chromosome to spread through most of the geographical range of the subspecies E. plorans plorans.This study was supported by a grant from the Spanish Ministerio de Ciencia e Innovación (CGL2009-11917), and was partially performed by FEDER funds. MIMP was supported by a fellowship (FPU) from the Spanish Ministerio de Ciencia e Innovación

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies

Ecuadorian Migration in Amsterdam and Madrid: The Structural Contexts

AbstractThe scope of this chapter is to outline the main characteristics of the two contexts that were the scenario of the phenomenon that is the object of this book. The analysis will centre on those structural features of the cities of Amsterdam and Madrid and, more in general, of the Netherlands and Spain, that may have had an influence on the experience of Ecuadorian irregular migrants

Mapping the human genetic architecture of COVID-19

Matters Arising to this article was published on 03 August 2022, available online at: https://doi.org/10.1038/s41586-022-04826-7 . A second Matters Arising to this article was published on 06 September 2023, available online at: https://doi.org/10.1038/s41586-023-06355-3 .Data availability: Summary statistics generated by the COVID-19 HGI are available at https://www.covid19hg.org/results/r5/ and are available in the GWAS Catalog (study code GCST011074). The analyses described here include the freeze-5 data. COVID-19 HGI continues to regularly release new data freezes. Summary statistics for non-European ancestry samples are not currently available due to the small individual sample sizes of these groups, but results for lead variants of 13 loci are reported in Supplementary Table 3. Individual level data can be requested directly from contributing studies, listed in Supplementary Table 1. We used publicly available data from GTEx (https://gtexportal.org/home/), the Neale lab (https://www.nealelab.is/uk-biobank/), Finucane lab (https://www.finucanelab.org), the FinnGen Freeze 4 cohort (https://www.finngen.fi/en/access_results) and the eQTL catalogue release 3 (https://www.ebi.ac.uk/eqtl/).Code availability: The code for summary statistics lift-over, the projection PCA pipeline including precomputed loadings and meta-analyses are available on GitHub (https://github.com/covid19-hg/) and the code for the Mendelian randomization and genetic correlation pipeline is available on GitHub at https://github.com/marcoralab/MRcovid.Reporting summary: Further information on research design is available in the Nature Research Reporting Summary linked to this paper online at: https://www.nature.com/articles/s41586-021-03767-x#MOESM2 .Supplementary information is available onlne at: https://www.nature.com/articles/s41586-021-03767-x#Sec24 .Extended data figures and tables are available online at: https://www.nature.com/articles/s41586-021-03767-x#Sec23 .Copyright © The Author(s) 2021. The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease

A second update on mapping the human genetic architecture of COVID-19

Matters Arising From: COVID-19 Host Genetics Initiative. Nature https://doi.org/10.1038/s41586-021-03767-x (2021)Data availability: Summary statistics generated by the COVID-19 HGI are available online, including per-ancestry summary statistics for African, admixed American, East Asian, European and South Asian ancestries (https://www.covid19hg.org/results/r7/). The analyses described here used the data release 7. If available, individual-level data can be requested directly from contributing studies, listed in Supplementary Table 1. We used publicly available data from GTEx (https://gtexportal.org/home/), the Neale laboratory (http://www.nealelab.is/uk-biobank/), the Finucane laboratory (https://www.finucanelab.org), the FinnGen Freeze 4 cohort (https://www.finngen.fi/en/access_results) and the eQTL catalogue release 3 (http://www.ebi.ac.uk/eqtl/).Code availability: The code for summary statistics lift-over, the projection PCA pipeline including precomputed loadings and meta-analyses (https://github.com/covid19-hg/); for heritability estimation (https://github.com/AndrewsLabUCSF/COVID19_heritability); for Mendelian randomization and genetic correlation (https://github.com/marcoralab/MRcovid); and subtype analyses (https://github.com/mjpirinen/covid19-hgi_subtypes) are available at GitHub.Reporting summary: Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article online at: https://www.nature.com/articles/s41586-023-06355-3#MOESM2 .Supplementary information is available online at: https://www.nature.com/articles/s41586-023-06355-3#Sec4 .Copyright © The Author(s) 2023. Investigating the role of host genetic factors in COVID-19 severity and susceptibility can inform our understanding of the underlying biological mechanisms that influence adverse outcomes and drug development1,2. Here we present a second updated genome-wide association study (GWAS) on COVID-19 severity and infection susceptibility to SARS-CoV-2 from the COVID-19 Host Genetic Initiative (data release 7). We performed a meta-analysis of up to 219,692 cases and over 3 million controls, identifying 51 distinct genome-wide significant loci—adding 28 loci from the previous data release2. The increased number of candidate genes at the identified loci helped to map three major biological pathways that are involved in susceptibility and severity: viral entry, airway defence in mucus and type I interferon