Biology subjects

Perna, G.

Publications and source records attributed to Perna, G..

4 recordsLinked to original sources

bioRxiv2026

The genome of the reef-building coral Porites harrisoni from the southern Persian/Arabian Gulf

We present a genome assembly from the coral species Porites harrisoni from the southern Persian/Arabian Gulf, the hottest ocean basin where corals live. The assembly is 626.7 Mb in size, spanning 1,883 contigs with a contig N50 of 807.4 kb, including a single-contig mitochondrial genome. The assembly has a BUSCO completeness of 86.3% (single = 72.5%, duplicated = 13.7%, fragmented = 1.2%, missing = 12.5%) using the eukaryota_odb10 reference set (n = 255). A total of 59.23% of the nuclear genome consists of repeats, comprising 15.89% retroelements, 10.00% DNA transposons, and 31.71% unclassified repeats. Gene annotation of this nuclear genome assembly identified 27,823 protein-coding genes. The mitogenome has an assembly size of 18,639 bp with 13 protein-coding genes as well as 2 tRNAs and 2 rRNAs. The genome of P. harrisoni provides a valuable genomic resource of a coral from an extreme environment, which will enable comparative analyses, enhancing our understanding of the genomic architecture underlying thermal resilience. Such comparisons will contribute to elucidating the evolutionary basis of heat tolerance and adaptive capacity of corals in the context of rapid climate change.

genomics↗

bioRxiv2024

Resolving Symbiodiniaceae diversity across coral microhabitats and reef niches

Dinoflagellates of the family Symbiodiniaceae are main symbionts of diverse marine animals. A large diversity of Symbiodiniaceae also occur beyond the bounds of their multicellular hosts, occupying environmental niches on coral reefs. The link between spatial diversity at ecosystem scale to microhabitats of Symbiodiniaceae within the coral holobiont are largely unknown. Using ITS2 amplicon sequencing, we compared Symbiodiniaceae communities across four environments (seawater, near-reef and distant sediments, and turf algae mats) and two coral microhabitats (tissue and mucus) on a coral reef in the Red Sea. Analysis of ITS2 sequences revealed that coral and environmental habitats were both dominated by the genera Symbiodinium, Cladocopium, and Durusdinium, but environmental habitats additionally harbored Fugacium, Gerakladium, and Halluxium. Each environmental habitat harbored a distinct Symbiodiniaceae community, with 14-27 % exclusive ITS2 sequences. Nonetheless, 17 ITS2 sequences were shared among all habitat types and were variants defining nearly half of the ITS2 type profiles used to further resolve Symbiodiniaceae identity of coral-based communities. Tissues and surface mucus layers of 49 coral colonies from 17 genera had largely identical Symbiodiniaceae communities. Together with the large difference between environmental Symbiodiniaceae communities and those in the mucus, our results indicate a clear barrier between host-associated and environmental Symbiodiniaceae communities marked by only few shared complete type profiles under normal conditions. It remains to be determined how Symbiodiniaceae community dynamics between coral microhabitats and environmental reservoirs change during coral bleaching events. Monitoring coral colonies after mucus sampling confirmed its suitability for repeated long-term monitoring of coral-associated Symbiodiniaceae communities.

systems biology↗

bioRxiv2019

A Novel Ensemble-Based Machine Learning Algorithm To Predict The Conversion From Mild Cognitive Impairment To Alzheimer's Disease Using Socio-demographic Characteristics, Clinical Information And Neuropsychological Measures

BackgroundDespite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimers disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20-40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to full AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to full AD. MethodsThe algorithm was developed using the open dataset from the Alzheimers Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm. ResultsThe final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity. DiscussionThe algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.

neuroscience↗

bioRxiv2017

Aiptasia as a model to study metabolic diversity and specificity in cnidarian-dinoflagellate symbioses

The symbiosis between cnidarian hosts and microalgae of the genus Symbiodinium provides the foundation of coral reefs in oligotrophic waters. Understanding the nutrient-exchange between these partners is key to identifying the fundamental mechanisms behind this symbiosis. However, deciphering the individual role of host and algal partners in the uptake and cycling of nutrients has proven difficult, given the endosymbiotic nature of this relationship. In this study, we highlight the advantages of the emerging model system Aiptasia to investigate the metabolic diversity and specificity of cnidarian - dinoflagellate symbiosis. For this, we combined traditional measurements with nano-scale secondary ion mass spectrometry (NanoSIMS) and stable isotope labeling to investigate carbon and nitrogen cycling both at the organismal scale and the cellular scale. Our results suggest that the individual nutrient assimilation by hosts and symbionts depends on the identity of their respective symbiotic partner. Further,{delta} 13C enrichment patterns revealed that alterations in carbon fixation rates only affected carbon assimilation in the cnidarian host but not the algal symbiont, suggesting a selfish character of this symbiotic association. Based on our findings, we identify new venues for future research regarding the role and regulation of nutrient exchange in the cnidarian - dinoflagellate symbiosis. In this context, the model system approach outlined in this study constitutes a powerful tool set to address these questions.

physiology↗