Biology subjects

Vos, R.

Publications and source records attributed to Vos, R..

7 recordsLinked to original sources

bioRxiv2023

Macroevolutionary dynamics in micro-organisms: generalists give rise to specialists across biomes in the ubiquitous bacterial phylum Myxococcota.

Prokaryotes dominate the Tree of Life, but our understanding of the macroevolutionary processes generating this diversity is still limited. Habitat transitions are thought to be a key driver of prokaryote diversity, but we still know relatively little about how prokaryotes successfully transition and persist across environments, and how this varies between biomes and lineages. Here, we investigate biome transitions and specialisation in natural populations of a focal bacterial phylum, the Myxococcota, sampled across a range of replicated soils and freshwater and marine sediments in Cornwall (UK). By targeted deep sequencing of the protein-coding gene rpoB, we found >2000 unique Myxococcota lineages, with the majority (77%) being biome specialists and <5% able to live across the salt barrier. Discrete character evolution models revealed that biome specialists very rarely transitioned to specialising in another biome. Instead, generalists mediated transitions between biome specialists. Multistate hidden-state speciation and extinction models found variation in speciation rate across the tree, but this variation was independent of biome association and specialisation. Overall, our results help explain how microbes transition between biomes and are consistent with "the jack-of-all-trades" trade-off, where generalists suffer a cost in any individual environment, resulting in rapid evolution of niche specialists.

microbiology↗

bioRxiv2023

A Multi-omic Analysis of the Human Lung Reveals Distinct Cell Specific Aging and Senescence Molecular Programs

Age is a major risk factor for lung disease. To understand the mechanisms underlying this association, we characterized the changing cellular, genomic, transcriptional, and epigenetic landscape of lung aging using bulk and single-cell RNAseq (scRNAseq) data. Our analysis revealed age-associated gene networks that reflected hallmarks of aging, including mitochondrial dysfunction, inflammation, and cellular senescence. Cell type deconvolution revealed age-associated changes in the cellular composition of the lung: decreased alveolar epithelial cells and increased fibroblasts and endothelial cells. In the alveolar microenvironment, aging is characterized by decreased AT2B cells and reduced surfactant production, a finding that was validated by scRNAseq and IHC. We showed that a previously reported senescence signature, SenMayo, captures cells expressing canonical senescence markers. SenMayo signature also identified cell-type specific senescence-associated co-expression modules that have distinct molecular functions, including ECM regulation, cell signaling, and damage response pathways. Analysis of somatic mutations showed that burden was highest in lymphocytes and endothelial cells and was associated with high expression of senescence signature. Finally, aging and senescence gene expression modules were associated with differentially methylated regions, with inflammatory markers such as IL1B, IL6R, and TNF being significantly regulated with age. Our findings provide new insights into the mechanisms underlying lung aging and may have implications for the development of interventions to prevent or treat age-related lung diseases.

genomics↗

bioRxiv2023

Single-cell genomics reveals the divergent mitochondrial genomes of Retaria (Foraminifera and Radiolaria)

Mitochondria originated from an ancient bacterial endosymbiont that underwent reductive evolution by gene loss and endosymbiont gene transfer to the nuclear genome. The diversity of mitochondrial genomes published to date has revealed that gene loss and transfer processes are ongoing in many lineages. Most well-studied eukaryotic lineages are represented in mitochondrial genome databases, except for the superphylum Retaria--the lineage comprising Foraminifera and Radiolaria. Using single-cell approaches, we present two complete mitochondrial genomes of Foraminifera and two near-complete mitochondrial genomes of radiolarians. We report the complete coding content of an additional 14 foram species. We show that foraminiferan and radiolarian mitochondrial genomes encode a nearly fully overlapping but reduced mitochondrial gene complement compared to other sequenced rhizarians. In contrast to animals and fungi, many protists encode a diverse set of proteins on their mitochondrial genomes, including several ribosomal genes; however, some aerobic eukaryotic lineages (euglenids, myzozoans, and chlamydomonas-like algae) have reduced mitochondrial gene content and lack all ribosomal genes. Similar to these reduced outliers, we show that retarian mitochondrial genomes lack ribosomal protein and tRNA genes, contain truncated and divergent small and large rRNA genes, and encode only 14-15 protein-coding genes, including nad1, 3, 4, 4L, 5, 7, cob, cox1, 2, 3, atp1, 6, and 9, with forams and radiolarians additionally encoding nad2 and nad6, respectively. In radiolarian mitogenomes, a non-canonical genetic code was identified in which all three stop codons encode amino acids. Collectively, these results add to our understanding of mitochondrial genome evolution and fill in one of the last major gaps in mitochondrial sequence databases.

genomics↗

bioRxiv2020

Multiple origins of insular woodiness on the Canary Islands are consistent with palaeoclimatic aridification

AimInsular woodiness, referring to the evolutionary transition from herbaceousness towards woodiness on islands, has arisen at least 38 times on the Canary Islands. Distribution patterns and physiological experiments have suggested a link between insular woodiness and increased drought stress resistance in current-day species, but we do not know in which palaeoclimatic conditions these insular woody lineages originated. Therefore, we estimated the timing of colonisation events and origin of woodiness of multiple Canary Island lineages and reviewed the palaeoclimate based on literature. LocationCanary Islands (Spain). Taxon37 lineages, including 24 insular woody and 13 non-insular woody (i.e. herbaceous, ancestrally woody, and derived woody). MethodsTo enable a simultaneous dating analysis for all 37 lineages, two chloroplast markers (matK and rbcL) for 135 Canary Island species and 103 closely related continental relatives were sequenced and aligned to an existing matK-rbcL dataset including ca 24,000 species that was calibrated with 42 fossils from outside the Canaries. After constraining the species to the family level, 200 RAxML runs were performed and dated with TreePL. ResultsWoodiness in 80-90% of the insular woody lineages originated within the last 7 Myr, coinciding with the onset of major aridification events nearby the Canaries (start of north African desertification, followed by Messinian salinity crisis); in ca 55-65% of the insular woody lineages studied, woodiness developed within the last 3.2 Myr during which Mediterranean seasonality (yearly summer droughts) became established on the Canaries, followed by dry Pleistocene glacial fluctuations. Main conclusionsAlthough details of the initial colonisation and settlement of many island plant lineages remain elusive, our results are consistent with palaeodrought as a potential driver for woodiness in most of the insular woody Canary Island lineages studied.

evolutionary biology↗

bioRxiv2019

Modelling the niches of wild and domesticated Ungulatespecies using deep learning

Knowledge of global biodiversity remains limited by geographic and taxonomic sampling biases. The scarcity of species data restricts our understanding of the underlying environmental factors shaping distributions, and the ability to draw comparisons among species. Species distribution models (SDMs) were developed in the early 2000s to address this issue. Although SDMs based on single layered Neural Networks have been experimented with in the past, these performed poorly. However, the past two decades have seen a strong increase in the use of Deep Learning (DL) approaches, such as Deep Neural Networks (DNNs). Despite the large improvement in predictive capacity DNNs provide over shallow networks, to our knowledge these have not yet been applied to SDM. The aim of this research was to provide a proof of concept of a DL-SDM1. We used a pre-existing dataset of the worlds ungulates and abiotic environmental predictors that had recently been used in MaxEnt SDM, to allow for a direct comparison of performance between both methods. Our DL-SDM consisted of a binary classification DNN containing 4 hidden layers and drop-out regularization between each layer. Performance of the DL-SDM was similar to MaxEnt for species with relatively large sample sizes and worse for species with relatively low sample sizes. Increasing the number of occurrences further improved DL-SDM performance for species that already had relatively high sample sizes. We then tried to further improve performance by altering the sampling procedure of negative instances and increasing the number of environmental predictors, including species interactions. This led to a large increase in model performance across the range of sample sizes in the species datasets. We conclude that DL-SDMs provide a suitable alternative to traditional SDMs such as MaxEnt and have the advantage of being both able to directly include species interactions, as well as being able to handle correlated input features. Further improvements to the model would include increasing its scalability by turning it into a multi-classification model, as well as developing a more user friendly DL-SDM Python package.

evolutionary biology↗

bioRxiv2019

Prediction of microscopic metastases in patients with metachronous oligo-metastases after curative treatment of Non-Small Cell Lung Cancer.

Metachronous oligo-metastatic disease is variably defined as one to five metastases detected after a disease-free interval and treatment of the primary tumour with curative intent. Oligo-metastases in non-small cell lung cancer (NSCLC) are often treated with curative intent. However additional metastases are often detected later in time, and 5-year survival is low. Burdensome surgical treatment in patients with undetected metastases may be avoided if patients with high versus low-risk of undetected metastases can be separated.\n\nBecause there is no clinical data on undetected metastases available, a microsimulation-model of the development and detection of metastases in 100.000 stage I NSCLC patients with a controlled primary tumour was constructed. The model uses data from the literature as well as patient-level data. Calibration was used for unobservable model parameters. Metastases can be detected by a scheduled scan, or an unplanned scan when the patient develops symptoms. The observable information at time of detection is used to identify subgroups of patients with different risk of undetectable metastases. We identified size and number of detected oligo-metastases, as well as presence of symptoms to be the most important risk predictors. Based on these predictors, patients could be divided into a low-risk and a high-risk group having a model-based predicted probability of 8.1% and 89.3% to have undetected metastases, respectively.\n\nCurrently, the model is based on a synthesis of literature data and individual patient-level data that was not collected for the purpose of this study. Optimisation and validation of the model is necessary to allow clinical usability. We describe the type of data that needs to be collected to update our model, as well as the design of such validation study.

cancer biology↗

bioRxiv2018

Inspecting Morphological Features of Mosquito Wings for Identification with Image Recognition Tools

Mosquitoes are important disease vectors. Different mosquito genera are associated with different diseases at varying levels of specificity. Hence, quick and low-cost methods of identification, even if relatively coarse and to genus level, will be of use in assessing risk and informing mitigation measures. Here we assess the extent to which digital photographs of mosquito wings taken with common cell phone cameras and clip-on lenses can be used to discriminate among mosquito genera when fed into image feature extraction algorithms. Our results show that genera may be distinguished on the basis of features extracted using the SURF algorithm. However, we also found that the naive features examined here require very standardized photography and that different phone cameras have different signatures that may need to be taken into account.

bioinformatics↗