Biology subjects

Melidis, D.

Publications and source records attributed to Melidis, D..

3 recordsLinked to original sources

bioRxiv2025

High-resolution taxonomic profiling and metatranscriptomics identify microbial, biochemical, host and ecological factors in peri-implant disease

Biofilm-associated diseases like peri-implant mucositis (PIM) and peri-implantitis (PI) are significant clinical challenges affecting millions of dental implant patients globally. Although studies have described the role of microbial, host, or environmental factors in disease development, their complex interplay, particularly during dysbiosis remains poorly understood. This cross-sectional study characterized the microbiome composition and metatranscriptomes of 125 peri-implant biofilms from 48 individuals uncovering molecular signatures linked to peri-implant health (PIH), PIM, and PI. Distinct variations were observed in biofilm amount, composition, activity, phage populations and host response. Biofilms were categorized into four community types (CTs) based on the bacterial transcriptional activity: one linked to PIH, one to PI, and two to PIM. PIH and PIM were primarily characterized by aerotolerant taxa with increased anabolic processes, while PI was dominated by obligate anaerobes with complex biofilm morphology, and heightened catabolic activity and virulence. PIM samples, relative to PIH were characterized by biofilm expansion with minimal functional changes, except for the Neisseria-rich PIM subtype showing higher pyruvate and lipoic acid metabolism. The phagome mirrored the bacterial compositional variations across disease states. Furthermore, human transcriptome responses varied indicating increased keratinization in PIH, enhanced expression of ribosome components in PIM, and inflammatory signaling and hypoxia in PI. Additionally, we identified complex species-enzyme, phage-bacteria, and host-microbe associations within the peri-implant ecosystem. Our integrative multi-omics approach provides a comprehensive view of microbial, biochemical, host, and ecological factors associated with dysbiosis, offering novel insights into peri-implant disease dynamics. ImportancePeri-implant mucositis and peri-implantitis are highly prevalent inflammatory conditions that compromise the long-term survival and success of dental implants, yet their underlying biological mechanisms are largely unresolved. While next-generation sequencing has advanced our understanding of microbial composition across health and peri-implant diseases, it falls short of capturing microbial activity and the broader molecular context of peri-implant dysbiosis. Metatranscriptomics overcomes this limitation by profiling actively transcribed genes within the biofilm, offering direct insights into microbial community functions. In this study, we integrated full-length 16S rRNA gene amplicon sequencing with metatranscriptomic profiling to simultaneously assess microbial taxonomy, functional activity, phage dynamics, and host gene expression in peri-implant biofilms. Importantly, we provide a systems-level view and report previously undescribed associations between different molecular signatures in peri-implant ecosystem.

microbiology↗

bioRxiv2021

GenOtoScope: Towards automating ACMG classification of variants associated with congenital hearing loss

Since next-generation sequencing (NGS) has become widely available, large gene panels containing up to several hundred genes can be sequenced cost-efficiently. However, the interpretation of the often large numbers of sequence variants detected when using NGS is laborious, prone to errors and often not comparable across laboratories. To overcome this challenge, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) introduced standards and guidelines for the interpretation of sequencing variants. Further gene- and disease-specific refinements regarding hereditary hearing loss have been developed since then. With more than 200 genes associated with hearing disorders, the manual inspection of possible causative variants is especially difficult and time consuming. We developed an open-source bioinformatics tool GenOtoScope, which automates all ACMG/AMP criteria that can be assessed without further individual patient information or human curator investigation, including the refined loss of function criterion ("PVS1"). Two types of interfaces are provided: (i) a command line application to classify sequence variants in batches for a set of patients and (ii) a user-friendly website to classify single variants. We compared the performance of our tool with two other variant classification tools using two hearing loss data sets, which were manually annotated either by the ClinGen Hearing Loss Gene Curation Expert Panel or the diagnostics unit of our human genetics department. GenOtoScope achieved the best average accuracy and precision for both data sets. Compared to the second-best tool, GenOtoScope improved accuracy metric by 25.75% and 4.57% and precision metric by 52.11% and 12.13% on the two data sets respectively. The web interface is freely accessible. The command line application along with all source code, documentation and example outputs can be found via the project GitHub page. Author summaryNew high-throughput sequencing technologies can produce massive amounts of information and are utilized by laboratories to explain the often complex genetic aetiology of hereditary diseases. The most common sensory disease, hearing loss, is often hereditary and has a high impact on a patients every-day life. To use these sequencing technologies effectively, software tools were developed that can aid researchers interpreting genetic data by semi-automatically classifying the biologic (and thus potentially medical) impact of detected variants (the alterations of the patients genome compared to the human reference genome). The available genetic variant classification tools are either not designed specifically for the interpretation of variants detected in subjects with hearing loss or they do not allow researchers to use them for batch classification of all variants detected, e. g. in a study group. To address this drawback, we developed GenOtoScope, an open-source tool that automates the pathogenicity classification of variants potentially associated with congenital hearing loss. GenOtoScope can be applied for the automatic classification of all variants detected in a set of probands.

bioinformatics↗

bioRxiv2017

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations.\n\nOur strategy towards accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics dataset against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and variants identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin, and release iPtgxDBs for B. henselae, Bradyrhozibium diazoefficiens and Escherichia coli as well as the software to generate such proteogenomics search databases for any prokaryote.

genomics↗