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The factors determining gradients of biodiversity are a fundamental yet unresolved topic in ecology. While diversity gradients have been analysed for numerous single taxa, progress towards general explanatory models has been hampered by limitations in the phylogenetic coverage of past studies. By parallel sampling of 25 major plant and animal taxa along a 3.7 km elevational gradient on Mt. Kilimanjaro, we quantify cross-taxon consensus in diversity gradients and evaluate predictors of diversity from single taxa to a multi-taxa community level. While single taxa show complex distribution patterns and respond to different environmental factors, scaling up diversity to the community level leads to an unambiguous support for temperature as the main predictor of species richness in both plants and animals. Our findings illuminate the influence of taxonomic coverage for models of diversity gradients and point to the importance of temperature for diversification and species coexistence in plant and animal communities.
Dilated cardiomyopathy (DCM) is a common cause of heart failure (HF) and is of familial origin in 20–40% of cases. Genetic testing by next-generation sequencing (NGS) has yielded a definite diagnosis in many cases; however, some remain elusive. In this study, we used a combination of NGS, human-induced pluripotent-stem-cell-derived cardiomyocytes (iPSC-CMs) and nanopore long-read sequencing to identify the causal variant in a multi-generational pedigree of DCM. A four-generation family with familial DCM was investigated. Next-generation sequencing (NGS) was performed on 22 family members. Skin biopsies from two affected family members were used to generate iPSCs, which were then differentiated into iPSC-CMs. Short-read RNA sequencing was used for the evaluation of the target gene expression, and long-read RNA nanopore sequencing was used to evaluate the relevance of the splice variants. The pedigree suggested a highly penetrant, autosomal dominant mode of inheritance. The phenotype of the family was suggestive of laminopathy, but previous genetic testing using both Sanger and panel sequencing only yielded conflicting evidence for LMNA p.R644C (rs142000963), which was not fully segregated. By re-sequencing four additional affected family members, further non-coding LMNA variants could be detected: rs149339264, rs199686967, rs201379016, and rs794728589. To explore the roles of these variants, iPSC-CMs were generated. RNA sequencing showed the LMNA expression levels to be significantly lower in the iPSC-CMs of the LMNA variant carriers. We demonstrated a dysregulated sarcomeric structure and altered calcium homeostasis in the iPSC-CMs of the LMNA variant carriers. Using targeted nanopore long-read sequencing, we revealed the biological significance of the variant c.356+1G>A, which generates a novel 5′ splice site in exon 1 of the cardiac isomer of LMNA, causing a nonsense mRNA product with almost complete RNA decay and haploinsufficiency. Using novel molecular analysis and nanopore technology, we demonstrated the pathogenesis of the rs794728589 (c.356+1G>A) splice variant in LMNA. This study highlights the importance of precise diagnostics in the clinical management and workup of cardiomyopathies.
Introduction: Familial dilated cardiomyopathy (DCM) is clinically variable and has been associated with mutations in more than 50 genes. Rapid improvements in DNA sequencing have led to the identification of diverse rare variants with unknown significance (VUS), which underlines the importance of functional analyses. In this study, by investigating human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), we evaluated the pathogenicity of the p.C335R sodium voltage-gated channel alpha subunit 5 (SCN5a) variant in a large family with familial DCM and conduction disease. Methods: A four-generation family with autosomal dominant familial DCM was investigated. Next-generation sequencing (NGS) was performed in all 16 family members. Clinical deep phenotyping, including endomyocardial biopsy, was performed. Skin biopsies from two patients and one healthy family member were used to generate human-induced pluripotent stem cells (iPSCs), which were then differentiated into cardiomyocytes. Patch-clamp analysis with Xenopus oocytes and iPSC-CMs were performed. Results: A SCN5a variant (c.1003T>C; p.C335R) could be detected in all family members with DCM or conduction disease. A novel truncating TTN variant (p.Ser24998LysfsTer28) could also be identified in two family members with DCM. Family members with the SCN5a variant (p.C335R) showed significantly longer PQ and QRS intervals and lower left ventricular ejection fractions (LV-EF). All four patients who received CRT-D were non-responders. Electrophysiological analysis with Xenopus oocytes showed a loss of function in SCN5a p.C335R. Na\(^+\) channel currents were also reduced in iPSC-CMs from DCM patients. Furthermore, iPSC-CM with compound heterozygosity (SCN5a p.C335R and TTNtv) showed significant dysregulation of sarcomere structures, which may be contributed to the severity of the disease and earlier onset of DCM. Conclusion: The SCN5a p.C335R variant is causing a loss of function of peak INa in patients with DCM and cardiac conduction disease. The co-existence of genetic variants in channels and structural genes (e.g., SCN5a p.C335R and TTNtv) increases the severity of the DCM phenotype.
The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.