Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.

MOTIVATION: World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available. RESULTS: We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 >80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score >75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions. AVAILABILITY: The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.

MEGARes and AMR++, v3.0: an updated comprehensive database of antimicrobial resistance determinants and an improved software pipeline for classification using high-throughput sequencing.

Antimicrobial resistance (AMR) is considered a critical threat to public health, and genomic/metagenomic investigations featuring high-throughput analysis of sequence data are increasingly common and important. We previously introduced MEGARes, a comprehensive AMR database with an acyclic hierarchical annotation structure that facilitates high-throughput computational analysis, as well as AMR++, a customized bioinformatic pipeline specifically designed to use MEGARes in high-throughput analysis for characterizing AMR genes (ARGs) in metagenomic sequence data. Here, we present MEGARes v3.0, a comprehensive database of published ARG sequences for antimicrobial drugs, biocides, and metals, and AMR++ v3.0, an update to our customized bioinformatic pipeline for high-throughput analysis of metagenomic data (available at MEGLab.org). Database annotations have been expanded to include information regarding specific genomic locations for single-nucleotide polymorphisms (SNPs) and insertions and/or deletions (indels) when required by specific ARGs for resistance expression, and the updated AMR++ pipeline uses this information to check for presence of resistance-conferring genetic variants in metagenomic sequenced reads. This new information encompasses 337 ARGs, whose resistance-conferring variants could not previously be confirmed in such a manner. In MEGARes 3.0, the nodes of the acyclic hierarchical ontology include 4 antimicrobial compound types, 59 resistance classes, 233 mechanisms and 1448 gene groups that classify the 8733 accessions.

Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing.

Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hidden Markov models (Meta-MARC). We use an extensive collection of 585 isolates with available AMR resistance profiles determined by phenotypic tests across nine antibiotic classes. We show how the prediction landscape of AMR classifiers is highly heterogeneous, with balanced accuracy varying from 0.40 to 0.92. Although some algorithms-ResFinder, KARGA and AMRPlusPlus-exhibit overall better balanced accuracy than others, the high per-AMR-class variance and related findings suggest that: (1) all algorithms might be subject to sampling bias both in data repositories used for training and experimental/clinical settings; and (2) a portion of clinical samples might contain uncharacterized AMR genes that the algorithms-mostly trained on known AMR genes-fail to generalize upon. These results lead us to formulate practical advice for software configuration and application, and give suggestions for future study designs to further develop AMR prediction tools from proof-of-concept to bedside.

Bacterial DnaK reduces the activity of anti-cancer drugs cisplatin and 5FU.

BACKGROUND: Chemotherapy is a primary treatment for cancer, but its efficacy is often limited by cancer-associated bacteria (CAB) that impair tumor suppressor functions. Our previous research found that Mycoplasma fermentans DnaK, a chaperone protein, impairs p53 activities, which are essential for most anti-cancer chemotherapeutic responses. METHODS: To investigate the role of DnaK in chemotherapy, we treated cancer cell lines with M. fermentans DnaK and then with commonly used p53-dependent anti-cancer drugs (cisplatin and 5FU). We evaluated the cells' survival in the presence or absence of a DnaK-binding peptide (ARV-1502). We also validated our findings using primary tumor cells from a novel DnaK knock-in mouse model. To provide a broader context for the clinical significance of these findings, we investigated human primary cancer sequencing datasets from The Cancer Genome Atlas (TCGA). We identified F. nucleatum as a CAB carrying DnaK with an amino acid composition highly similar to M. fermentans DnaK. Therefore, we investigated the effect of F. nucleatum DnaK on the anti-cancer activity of cisplatin and 5FU. RESULTS: Our results show that both M. fermentans and F. nucleatum DnaKs reduce the effectiveness of cisplatin and 5FU. However, the use of ARV-1502 effectively restored the drugs' anti-cancer efficacy. CONCLUSIONS: Our findings offer a practical framework for designing and implementing novel personalized anti-cancer strategies by targeting specific bacterial DnaKs in patients with poor response to chemotherapy, underscoring the potential for microbiome-based personalized cancer therapies.

Macrophage-mediated PDGF Activation Correlates With Regenerative Outcomes Following Musculoskeletal Trauma.

OBJECTIVE: Our objective was to identify macrophage subpopulations and gene signatures associated with regenerative or fibrotic healing across different musculoskeletal injury types. BACKGROUND: Subpopulations of macrophages are hypothesized to fine tune the immune response after damage, promoting either normal regenerative, or aberrant fibrotic healing. METHODS: Mouse single-cell RNA sequencing data before and after injury were assembled from models of musculoskeletal injury, including regenerative and fibrotic mouse volumetric muscle loss (VML), regenerative digit tip amputation, and fibrotic heterotopic ossification. R packages Harmony , MacSpectrum , and Seurat were used for data integration, analysis, and visualizations. RESULTS: There was a substantial overlap between macrophages from the regenerative VML (2 mm injury) and regenerative bone models, as well as a separate overlap between the fibrotic VML (3 mm injury) and fibrotic bone (heterotopic ossification) models. We identified 2 fibrotic-like (FL 1 and FL 2) along with 3 regenerative-like (RL 1, RL 2, and RL 3) subpopulations of macrophages, each of which was transcriptionally distinct. We found that regenerative and fibrotic conditions had similar compositions of proinflammatory and anti-inflammatory macrophages, suggesting that macrophage polarization state did not correlate with healing outcomes. Receptor/ligand analysis of macrophage-to-mesenchymal progenitor cell crosstalk showed enhanced transforming growth factor ß in fibrotic conditions and enhanced platelet-derived growth factor signaling in regenerative conditions. CONCLUSION: Characterization of macrophage subtypes could be used to predict fibrotic responses following injury and provide a therapeutic target to tune the healing microenvironment towards more regenerative conditions.

Optimizing viral genome subsampling by genetic diversity and temporal distribution (TARDiS) for phylogenetics.

SUMMARY: TARDiS is a novel phylogenetic tool for optimal genetic subsampling. It optimizes both genetic diversity and temporal distribution through a genetic algorithm. AVAILABILITY AND IMPLEMENTATION: TARDiS, along with example datasets and a user manual, is available at https://github.com/smarini/tardis-phylogenetics.

Severe Acute Respiratory Syndrome Coronavirus 2 Delta Vaccine Breakthrough Transmissibility in Alachua County, Florida.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant has caused a dramatic resurgence in infections in the United Sates, raising questions regarding potential transmissibility among vaccinated individuals. METHODS: Between October 2020 and July 2021, we sequenced 4439 SARS-CoV-2 full genomes, 23% of all known infections in Alachua County, Florida, including 109 vaccine breakthrough cases. Univariate and multivariate regression analyses were conducted to evaluate associations between viral RNA burden and patient characteristics. Contact tracing and phylogenetic analysis were used to investigate direct transmissions involving vaccinated individuals. RESULTS: The majority of breakthrough sequences with lineage assignment were classified as Delta variants (74.6%) and occurred, on average, about 3 months (104 ±â€…57.5 days) after full vaccination, at the same time (June-July 2021) of Delta variant exponential spread within the county. Six Delta variant transmission pairs between fully vaccinated individuals were identified through contact tracing, 3 of which were confirmed by phylogenetic analysis. Delta breakthroughs exhibited broad viral RNA copy number values during acute infection (interquartile range, 1.2-8.64 Log copies/mL), on average 38% lower than matched unvaccinated patients (3.29-10.81 Log copies/mL, P < .00001). Nevertheless, 49% to 50% of all breakthroughs, and 56% to 60% of Delta-infected breakthroughs exhibited viral RNA levels above the transmissibility threshold (4 Log copies/mL) irrespective of time after vaccination. CONCLUSIONS: Delta infection transmissibility and general viral RNA quantification patterns in vaccinated individuals suggest limited levels of sterilizing immunity that need to be considered by public health policies. In particular, ongoing evaluation of vaccine boosters should specifically address whether extra vaccine doses curb breakthrough contribution to epidemic spread.

Low-frequency variants in mildly symptomatic vaccine breakthrough infections presents a doubled-edged sword.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC) has raised questions regarding vaccine protection against SARS-CoV-2 infection, transmission, and ongoing virus evolution. Twenty-three mildly symptomatic "vaccination breakthrough" infections were identified as early as January 2021 in Alachua County, Florida, among individuals fully vaccinated with either the BNT162b2 (Pfizer) or the Ad26 (Janssen/J&J) vaccines. SARS-CoV-2 genomes were successfully generated for 11 of the vaccine breakthroughs, and 878 individuals in the surrounding area and were included for reference-based phylogenetic investigation. These 11 individuals were characterized by infection with VOCs, but also low-frequency variants present within the surrounding population. Low-frequency mutations were observed, which have been more recently identified as mutations of interest owing to their location within targeted immune epitopes (P812L) and association with increased replicative capacity (L18F). We present these results to posit the nature of the efficacy of vaccines in reducing symptoms as both a blessing and a curse-as vaccination becomes more widespread and self-motivated testing reduced owing to the absence of severe symptoms, we face the challenge of early recognition of novel mutations of potential concern. This case study highlights the critical need for continued testing and monitoring of infection and transmission among individuals regardless of vaccination status.

Tuning Macrophage Phenotype to Mitigate Skeletal Muscle Fibrosis.

Myeloid cells are critical to the development of fibrosis following muscle injury; however, the mechanism of their role in fibrosis formation remains unclear. In this study, we demonstrate that myeloid cell-derived TGF-ß1 signaling is increased in a profibrotic ischemia reperfusion and cardiotoxin muscle injury model. We found that myeloid-specific deletion of Tgfb1 abrogates the fibrotic response in this injury model and reduces fibro/adipogenic progenitor cell proliferation while simultaneously enhancing muscle regeneration, which is abrogated by adaptive transfer of normal macrophages. Similarly, a murine TGFBRII-Fc ligand trap administered after injury significantly reduced muscle fibrosis and improved muscle regeneration. This study ultimately demonstrates that infiltrating myeloid cell TGF-ß1 is responsible for the development of traumatic muscle fibrosis, and its blockade offers a promising therapeutic target for preventing muscle fibrosis after ischemic injury.

Correction: Lopez-Vazquez et al. Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories. Sensors 2020, 20, 726.

The authors wish to correct the following error in the original paper [...].

Fast and exact quantification of motif occurrences in biological sequences.

BACKGROUND: Identification of motifs and quantification of their occurrences are important for the study of genetic diseases, gene evolution, transcription sites, and other biological mechanisms. Exact formulae for estimating count distributions of motifs under Markovian assumptions have high computational complexity and are impractical to be used on large motif sets. Approximated formulae, e.g. based on compound Poisson, are faster, but reliable p value calculation remains challenging. Here, we introduce 'motif_prob', a fast implementation of an exact formula for motif count distribution through progressive approximation with arbitrary precision. Our implementation speeds up the exact calculation, usually impractical, making it feasible and posit to substitute currently employed heuristics. RESULTS: We implement motif_prob in both Perl and C+ + languages, using an efficient error-bound iterative process for the exact formula, providing comparison with state-of-the-art tools (e.g. MoSDi) in terms of precision, run time benchmarks, along with a real-world use case on bacterial motif characterization. Our software is able to process a million of motifs (13-31 bases) over genome lengths of 5 million bases within the minute on a regular laptop, and the run times for both the Perl and C+ + code are several orders of magnitude smaller (50-1000× faster) than MoSDi, even when using their fast compound Poisson approximation (60-120× faster). In the real-world use cases, we first show the consistency of motif_prob with MoSDi, and then how the p-value quantification is crucial for enrichment quantification when bacteria have different GC content, using motifs found in antimicrobial resistance genes. The software and the code sources are available under the MIT license at https://github.com/DataIntellSystLab/motif_prob . CONCLUSIONS: The motif_prob software is a multi-platform and efficient open source solution for calculating exact frequency distributions of motifs. It can be integrated with motif discovery/characterization tools for quantifying enrichment and deviation from expected frequency ranges with exact p values, without loss in data processing efficiency.

Toward more accurate prediction of caspase cleavage sites: a comprehensive review of current methods, tools and features.

As one of the few irreversible protein posttranslational modifications, proteolytic cleavage is involved in nearly all aspects of cellular activities, ranging from gene regulation to cell life-cycle regulation. Among the various protease-specific types of proteolytic cleavage, cleavages by casapses/granzyme B are considered as essential in the initiation and execution of programmed cell death and inflammation processes. Although a number of substrates for both types of proteolytic cleavage have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remains to be fully characterized. To tackle this issue and complement experimental efforts for substrate identification, systematic bioinformatics studies of known cleavage sites provide important insights into caspase/granzyme B substrate specificity, and facilitate the discovery of novel substrates. In this article, we review and benchmark 12 state-of-the-art sequence-based bioinformatics approaches and tools for caspases/granzyme B cleavage prediction. We evaluate and compare these methods in terms of their input/output, algorithms used, prediction performance, validation methods and software availability and utility. In addition, we construct independent data sets consisting of caspases/granzyme B substrates from different species and accordingly assess the predictive power of these different predictors for the identification of cleavage sites. We find that the prediction results are highly variable among different predictors. Furthermore, we experimentally validate the predictions of a case study by performing caspase cleavage assay. We anticipate that this comprehensive review and survey analysis will provide an insightful resource for biologists and bioinformaticians who are interested in using and/or developing tools for caspase/granzyme B cleavage prediction.

Perivascular Fibro-Adipogenic Progenitor Tracing during Post-Traumatic Osteoarthritis.

Perivascular mural cells surround capillaries and microvessels and have diverse regenerative or fibrotic functions after tissue injury. Subsynovial fibrosis is a well-known pathologic feature of osteoarthritis, yet transgenic animals for use in visualizing perivascular cell contribution to fibrosis during arthritic changes have not been developed. Here, inducible Pdgfra-CreERT2 reporter mice were subjected to joint-destabilization surgery to induce arthritic changes, and cell lineage was traced over an 8-week period with a focus on the joint-associated fat pad. Results showed that, at baseline, inducible Pdgfra reporter activity highlighted adventitial and, to a lesser extent, pericytic cells within the infrapatellar fat pad. Joint-destabilization surgery was associated with marked fibrosis of the infrapatellar fat pad, accompanied by an expansion of perivascular Pdgfra-expressing cellular descendants, many of which adopted α-smooth muscle actin expression. Gene expression analysis of microdissected infrapatellar fat pad confirmed enrichment in membrane-bound green fluorescent protein/Pdgfra-expressing cells, along with a gene signature that corresponded with injury-associated fibro-adipogenic progenitors. Our results highlight dynamic changes in joint-associated perivascular fibro-adipogenic progenitors during osteoarthritis.

Brain tissue transcriptomic analysis of SIV-infected macaques identifies several altered metabolic pathways linked to neuropathogenesis and poly (ADP-ribose) polymerases (PARPs) as potential therapeutic targets.

Despite improvements in antiretroviral therapy, human immunodeficiency virus type 1 (HIV-1)-associated neurocognitive disorders (HAND) remain prevalent in subjects undergoing therapy. HAND significantly affects individuals' quality of life, as well as adherence to therapy, and, despite the increasing understanding of neuropathogenesis, no definitive diagnostic or prognostic marker has been identified. We investigated transcriptomic profiles in frontal cortex tissues of Simian immunodeficiency virus (SIV)-infected Rhesus macaques sacrificed at different stages of infection. Gene expression was compared among SIV-infected animals (n = 11), with or without CD8+ lymphocyte depletion, based on detectable (n = 6) or non-detectable (n = 5) presence of the virus in frontal cortex tissues. Significant enrichment in activation of monocyte and macrophage cellular pathways was found in animals with detectable brain infection, independently from CD8+ lymphocyte depletion. In addition, transcripts of four poly (ADP-ribose) polymerases (PARPs) were up-regulated in the frontal cortex, which was confirmed by real-time polymerase chain reaction. Our results shed light on involvement of PARPs in SIV infection of the brain and their role in SIV-associated neurodegenerative processes. Inhibition of PARPs may provide an effective novel therapeutic target for HIV-related neuropathology.

The role of neutrophil extracellular traps and TLR signaling in skeletal muscle ischemia reperfusion injury.

Ischemia reperfusion (IR) injury results in devastating skeletal muscle fibrosis. Here, we recapitulate this injury with a mouse model of hindlimb IR injury which leads to skeletal muscle fibrosis. Injury resulted in extensive immune infiltration with robust neutrophil extracellular trap (NET) formation in the skeletal muscle, however, direct targeting of NETs via the peptidylarginine deiminase 4 (PAD4) mechanism was insufficient to reduce muscle fibrosis. Circulating levels of IL-10 and TNFα were significantly elevated post injury, indicating toll-like receptor (TLR) signaling may be involved in muscle injury. Administration of hydroxychloroquine (HCQ), a small molecule inhibitor of TLR7/8/9, following injury reduced NET formation, IL-10, and TNFα levels and ultimately mitigated muscle fibrosis and improved myofiber regeneration following IR injury. HCQ treatment decreased fibroadipogenic progenitor cell proliferation and partially inhibited ERK1/2 phosphorylation in the injured tissue, suggesting it may act through a combination of TLR7/8/9 and ERK signaling mechanisms. We demonstrate that treatment with FDA-approved HCQ leads to decreased muscle fibrosis and increased myofiber regeneration following IR injury, suggesting short-term HCQ treatment may be a viable treatment to prevent muscle fibrosis in ischemia reperfusion and traumatic extremity injury.

A high-throughput and open-source platform for embryo phenomics.

Phenomics has the potential to facilitate significant advances in biology but requires the development of high-throughput technologies capable of generating and analysing high-dimensional data. There are significant challenges associated with building such technologies, not least those required for investigating dynamic processes such as embryonic development, during which high rates of temporal, spatial, and functional change are inherently difficult to capture. Here, we present EmbryoPhenomics, an accessible high-throughput platform for phenomics in aquatic embryos comprising an Open-source Video Microscope (OpenVIM) that produces high-resolution videos of multiple embryos under tightly controlled environmental conditions. These videos are then analysed by the Python package Embryo Computer Vision (EmbryoCV), which extracts phenomic data for morphological, physiological, behavioural, and proxy traits during the process of embryonic development. We demonstrate the broad-scale applicability of EmbryoPhenomics in a series of experiments assessing chronic, acute, and multistressor responses to environmental change (temperature and salinity) in >30 million images of >600 embryos of two species with markedly different patterns of development-the pond snail Radix balthica and the marine amphipod Orchestia gammarellus. The challenge of phenomics is significant but so too are the rewards, and it is particularly relevant to the urgent task of assessing complex organismal responses to current rates of environmental change. EmbryoPhenomics can acquire and process data capturing functional, temporal, and spatial responses in the earliest, most dynamic life stages and is potentially game changing for those interested in studying development and phenomics more widely.

Beneath the surface: hyper-connectivity between caudate and salience regions in ADHD fMRI at rest.

Attention-Deficit/Hyperactivity Disorder (ADHD) comprises disturbances in attention, emotional regulation, and reward-related processes. In spite of the active efforts in researching neurofunctional correlates of these symptoms, how the activity of subcortical regions-such as basal ganglia-is related to ADHD has yet to be clarified. More specifically, how age may influence the critical changes observed in functional dynamics from childhood to adulthood remains relatively unexplored. We hence selected five core subcortical regions (amygdala, caudate, putamen, pallidum and hippocampus) as regions of interest from the previous literature, measuring their whole-brain voxel-wise rsFC in a sample of 95 ADHD and 90 neurotypical children and adolescents aged from 7 to 18. The only subcortical structure showing significant differences in rsFC was the caudate nucleus. Specifically, we measured increased rsFC with anterior cingulate and right insula, two mesolimbic regions pertaining to the Salience Network. The degree of hyper-rsFC positively correlated with ADHD symptomatology, and showed different patterns of evolution in ADHD vs neurotypical subjects. Finally, the rsFC scores allowed a fair discrimination of the ADHD group (Area Under the Curve ≥ 0.7). These findings shed further light on the fundamental role covered by subcortical structures in ADHD pathogenesis and neurodevelopment, providing new evidence to fill the gap between neurofunctional and clinical expressions of ADHD.

Research Trends and Future Perspectives in Marine Biomimicking Robotics.

Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950-2020), evidencing a sharp research increase in 2003-2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.

Protease target prediction via matrix factorization.

MOTIVATION: Protein cleavage is an important cellular event, involved in a myriad of processes, from apoptosis to immune response. Bioinformatics provides in silico tools, such as machine learning-based models, to guide the discovery of targets for the proteases responsible for protein cleavage. State-of-the-art models have a scope limited to specific protease families (such as Caspases), and do not explicitly include biological or medical knowledge (such as the hierarchical protein domain similarity or gene-gene interactions). To fill this gap, we present a novel approach for protease target prediction based on data integration. RESULTS: By representing protease-protein target information in the form of relational matrices, we design a model (i) that is general and not limited to a single protease family, and (b) leverages on the available knowledge, managing extremely sparse data from heterogeneous data sources, including primary sequence, pathways, domains and interactions. When compared with other algorithms on test data, our approach provides a better performance even for models specifically focusing on a single protease family. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/smarini/MaDDA/ (Matlab code and utilized data.). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms.

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.