Computational approaches for analysing and engineering micropollutant degradation in microbial communities

PhD ThesisThe presence of micropollutants in wastewater is problematic, as many micropollutants exert negative ecological and toxicological effects in their environment. A well-known effect of micropollutants is the feminisation of aquatic wildlife by environmental estrogens, a proportion of which enter water courses from municipal sources via wastewater treatment plants (WWTPs). While WWTPs remove some micropollutants, they are not designed to do so. Given that WWTPs already have high operating costs (both financially and energetically), there is a need for novel approaches to micropollutant removal that are both cost-effective and environmentally sustainable. One proposed approach is to use enzymes to degrade micropollutants, which requires an understanding of metabolic pathways for the desired micropollutant, and a strategy for deploying the enzymes in the environment. Although tools exist to assist with metabolic pathway prediction and enzyme discovery, there are currently no computational approaches that are able to identify enzymes from a user’s collection of proteins (given a query compound and expected change to that query compound). To address this research gap, we developed EnSeP, a data-driven, transformation-specific approach to enzyme discovery. Using EnSeP, we then identified candidate enzymes involved in estradiol degradation. Recent advances in synthetic biology mean that deploying a single synthetic construct in multiple microorganisms is feasible. In the context of micropollutant metabolism, this means that a biodegradative pathway could be introduced into multiple organisms in a community simultaneously, providing more opportunities for the construct (and its functionality) to persist in the population long-term. However, current design tools have not yet been adapted for multiple organism applications. To address this research gap, we developed an evolutionary algorithm (EA) that optimises a single coding sequence (CDS) for multiple hosts. Finally, based on insights from developing the EA, we developed an improved version of the single-organism CDS optimisation algorithm that the EA is based on

Capturing Multicellular System Designs Using Synthetic Biology Open Language (SBOL)

8 Pág.Synthetic biology aims to develop novel biological systems and increase their reproducibility using engineering principles such as standardization and modularization. It is important that these systems can be represented and shared in a standard way to ensure they can be easily understood, reproduced, and utilized by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterization. Previously, this standard has only been used to represent designs in systems where the same design is implemented in every cell; however, there is also much interest in multicellular systems, in which designs involve a mixture of different types of cells with differing genotype and phenotype. Here, we show how the SBOL standard can be used to represent multicellular systems, and, hence, how researchers can better share designs with the community and reliably document intended system functionality.This work was supported in part by NSF Expeditions in Computing Program Award No. 1522074 as part of the Living Computing Project and by the Defense Advanced Research Projects Agency under Contract No. W911NF-17-2-0098. The views, opinions, and/or findings expressed are of the author(s) and should not be interpreted as representing official views or policies of the Department of Defense or the U.S. Government. A.G.-M. was supported by the SynBio3D project of the UK Engineering and Physical Sciences Research Council (No.EP/R019002/1) and the European CSA on biological standardization BIOROBOOST (EU Grant No. 820699)Peer reviewe

The gene-rich genome of the scallop Pecten maximus.

BACKGROUND: The king scallop, Pecten maximus, is distributed in shallow waters along the Atlantic coast of Europe. It forms the basis of a valuable commercial fishery and plays a key role in coastal ecosystems and food webs. Like other filter feeding bivalves it can accumulate potent phytotoxins, to which it has evolved some immunity. The molecular origins of this immunity are of interest to evolutionary biologists, pharmaceutical companies, and fisheries management. FINDINGS: Here we report the genome assembly of this species, conducted as part of the Wellcome Sanger 25 Genomes Project. This genome was assembled from PacBio reads and scaffolded with 10X Chromium and Hi-C data. Its 3,983 scaffolds have an N50 of 44.8 Mb (longest scaffold 60.1 Mb), with 92% of the assembly sequence contained in 19 scaffolds, corresponding to the 19 chromosomes found in this species. The total assembly spans 918.3 Mb and is the best-scaffolded marine bivalve genome published to date, exhibiting 95.5% recovery of the metazoan BUSCO set. Gene annotation resulted in 67,741 gene models. Analysis of gene content revealed large numbers of gene duplicates, as previously seen in bivalves, with little gene loss, in comparison with the sequenced genomes of other marine bivalve species. CONCLUSIONS: The genome assembly of P. maximus and its annotated gene set provide a high-quality platform for studies on such disparate topics as shell biomineralization, pigmentation, vision, and resistance to algal toxins. As a result of our findings we highlight the sodium channel gene Nav1, known to confer resistance to saxitoxin and tetrodotoxin, as a candidate for further studies investigating immunity to domoic acid

Drug repurposing prediction for COVID-19 using probabilistic networks and crowdsourced curation

Severe acute respiratory syndrome coronavirus two (SARS-CoV-2), the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic, represents an unprecedented global health challenge. Consequently, a large amount of research into the disease pathogenesis and potential treatments has been carried out in a short time frame. However, developing novel drugs is a costly and lengthy process, and is unlikely to deliver a timely treatment for the pandemic. Drug repurposing, by contrast, provides an attractive alternative, as existing drugs have already undergone many of the regulatory requirements. In this work we used a combination of network algorithms and human curation to search integrated knowledge graphs, identifying drug repurposing opportunities for COVID-19. We demonstrate the value of this approach, reporting on eight potential repurposing opportunities identified, and discuss how this approach could be incorporated into future studies

The genome sequence of the Eurasian river otter, Lutra lutra Linnaeus 1758 [version 1; peer review: 2 approved]

We present a genome assembly from an individual male Lutra lutra (the Eurasian river otter; Vertebrata; Mammalia; Eutheria; Carnivora; Mustelidae). The genome sequence is 2.44 gigabases in span. The majority of the assembly is scaffolded into 20 chromosomal pseudomolecules, with both X and Y sex chromosomes assembled

The genome sequence of the Eurasian river otter, Lutra lutra Linnaeus 1758.

We present a genome assembly from an individual male Lutra lutra (the Eurasian river otter; Vertebrata; Mammalia; Eutheria; Carnivora; Mustelidae). The genome sequence is 2.44 gigabases in span. The majority of the assembly is scaffolded into 20 chromosomal pseudomolecules, with both X and Y sex chromosomes assembled

A data science roadmap for open science organizations engaged in early-stage drug discovery

The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design

Freshwater ecoregions of the world: A new map of biogeographic units for freshwater biodiversity conservation

We present a new map depicting the first global biogeographic regionalization of Earth's freshwater systems. This map of freshwater ecoregions is based on the distributions and compositions of freshwater fish species and incorporates major ecological and evolutionary patterns. Covering virtually all freshwater habitats on Earth, this ecoregion map, together with associated species data, is a useful tool for underpinning global and regional conservation planning efforts (particularly to identify outstanding and imperiled freshwater systems); for serving as a logical framework for large-scale conservation strategies; and for providing a global-scale knowledge base for increasing freshwater biogeographic literacy. Preliminary data for fish species compiled by ecoregion reveal some previously unrecognized areas of high biodiversity, highlighting the benefit of looking at the world's freshwaters through a new framework.La lista completa de autores que integran el documento puede consultarse en el archivo.Facultad de Ciencias Naturales y Muse

Freshwater ecoregions of the world: A new map of biogeographic units for freshwater biodiversity conservation

We present a new map depicting the first global biogeographic regionalization of Earth's freshwater systems. This map of freshwater ecoregions is based on the distributions and compositions of freshwater fish species and incorporates major ecological and evolutionary patterns. Covering virtually all freshwater habitats on Earth, this ecoregion map, together with associated species data, is a useful tool for underpinning global and regional conservation planning efforts (particularly to identify outstanding and imperiled freshwater systems); for serving as a logical framework for large-scale conservation strategies; and for providing a global-scale knowledge base for increasing freshwater biogeographic literacy. Preliminary data for fish species compiled by ecoregion reveal some previously unrecognized areas of high biodiversity, highlighting the benefit of looking at the world's freshwaters through a new framework.La lista completa de autores que integran el documento puede consultarse en el archivo.Facultad de Ciencias Naturales y Muse