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bioRxiv2020

Machine Learning Maps Research Needs in COVID-19 Literature

SummaryManually assessing the scope of the thousands of publications on the COVID-19 (coronavirus disease 2019) pandemic is an overwhelming task. Shortcuts through metadata analysis (e.g., keywords) assume that studies are properly tagged. However, machine learning approaches can rapidly survey the actual text of coronavirus abstracts to identify research overlap between COVID-19 and other coronavirus diseases, research hotspots, and areas warranting exploration. We propose a fast, scalable, and reusable framework to parse novel disease literature. When applied to the COVID-19 Open Research Dataset (CORD-19), dimensionality reduction suggested that COVID-19 studies to date are primarily clinical-, modeling- or field-based, in contrast to the vast quantity of laboratory-driven research for other (non-COVID-19) coronavirus diseases. Topic modeling also indicated that COVID-19 publications have thus far focused primarily on public health, outbreak reporting, clinical care, and testing for coronaviruses, as opposed to the more limited number focused on basic microbiology, including pathogenesis and transmission.

scientific communication and education↗

bioRxiv2020

Campaign-Based Citizen Science for Environmental Mycology: the "Science Solstice" and "Summer Soil-stice" Projects to Assess Drug Resistance in Air and Soilborne Aspergillus fumigatus

Citizen science projects are often undertaken for ecological and environmental research purposes but also have great potential for use in microbiology research to track the emergence and spread of pathogens in the environment. Science Solstice and Summer Soil-stice are mycology citizen science projects aimed at assessing drug resistance in Aspergillus fumigatus fungal spores found in air and soil, respectively, in the United Kingdom (UK). A. fumigatus plays an important role in the environment as a decomposer of plant material, but is also an opportunistic human lung pathogen. Infection with drug-resistant spores can lead to a worse clinical outcome for the patient. On the first four solstice and equinox days between June 2018 and June 2019, volunteers were asked to collect air samples from their homes and workplaces and return them to our lab in Freepost envelopes. An additional round of samples was requested from volunteers gardens and/or compost on the June 2019 solstice. In total, 787 volunteers returned 2,132 air samples and 509 soil samples, which grew a total of 7,991 A. fumigatus colonies. The estimated total cost of the study was {pound}2,650; the equivalent of 33 pence per A. fumigatus colony grown. Incorporating citizen science into the environmental surveillance of drug-resistant A. fumigatus allowed for the simultaneous collection of hundreds of environmental samples across the entire UK on the same day. The insights generated from this study would not be practical in the absence of public participation and offers opportunities to ask scientific questions that were previously unaskable.

scientific communication and education↗

bioRxiv2020

Segmentation-less, automated vascular vectorization robustly extracts neurovascular network statistics from in vivo two-photon images

Recent advances in two-photon microscopy (2PM) have allowed large scale imaging and analysis of blood vessel networks in living mice. However, extracting a network graph and vector representations for vessels remain bottlenecks in many applications. Vascular vectorization is algorithmically difficult because blood vessels have many shapes and sizes, the samples are often unevenly illuminated, and large image volumes are required to achieve good statistical power. State-of-the-art, three-dimensional, vascular vectorization approaches often require a segmented (binary) image, relying on manual or supervised-machine annotation. Therefore, voxel-by-voxel image segmentation is biased by the human annotator or trainer. Furthermore, segmented images oftentimes require remedial morphological filtering before skeletonization or vectorization. To address these limitations, we present a vectorization method to extract vascular objects directly from unsegmented images without the need for machine learning or training. The Segmentation-Less, Automated, Vascular Vectorization (SLAVV) source code in MATLAB is openly available on GitHub. This novel method uses simple models of vascular anatomy, efficient linear filtering, and low-complexity vector extraction algorithms to remove the image segmentation requirement, replacing it with manual or automated vector classification. SLAVV is demonstrated on three in vivo 2PM image volumes of microvascular networks (capillaries, arterioles and venules) in the mouse cortex. Vectorization performance is proven robust to the choice of plasma- or endothelial-labeled contrast, and processing costs are shown to scale with input image volume. Fully-automated SLAVV performance is evaluated on simulated 2PM images of varying quality all based on the large (1.4x0.9x0.6 mm3 and 1.6x108 voxel) input image. Vascular statistics of interest (e.g. volume fraction, surface area density) calculated from automatically vectorized images show greater robustness to image quality than those calculated from intensity-thresholded images. Author summarySamuel Mihelic is a PhD candidate in the Biomedical Engineering Department at the University of Texas at Austin. He graduated from Oregon State University (Chemical Engineering BS, Mathematics BS). He hosts the GitHub repository for the code used in this article: https://github.com/UTFOIL/Vectorization-Public. His research interests are in-vivo neural microvascular image analysis, anatomy, and plasticity. William Sikora graduated with a BS in Computational Biomedical Engineering from The University of Texas at Austin in May 2020. He is working with Dr. Yuan Yang and the Laureate Institute for Brain Research as a PhD student of Biomedical Engineering at the University of Oklahoma in Tulsa, researching the highly non-linear world of neural coupling and its link to common neurological pathologies such as stroke. Ahmed Hassan is a graduate of the University of California, Los Angeles and the University of Texas at Austin with a Bachelor's degree in Microbiology, Immunology, and Molecular Genetics and an MSE/PhD in Biomedical Engineering. His graduate research was concentrated in imaging and instrumentation, and his interests include developing optical and laser systems for neuroimaging, image processing and reconstruction, and advanced image analysis. Michael Williamson earned a BSc (Honours) in Neuroscience in 2016 from the University of Alberta, where he trained with Dr. Fred Colbourne. He is currently a doctoral student at the University of Texas at Austin working in the labs of Drs. Theresa Jones and Michael Drew. Theresa Jones is a Professor in the Department of Psychology and Neuroscience at The University of Texas at Austin. Her laboratory studies plasticity of neural structure and synaptic connectivity following brain damage and injury. Andrew K. Dunn is the Donald J. Douglass Centennial Professor of Engineering in the Department of Biomedical Engineering at The University of Texas at Austin and the Director of the Center for Emerging Imaging Technologies. His research focuses on the development of innovative optical imaging techniques for studying the brain.

bioengineering↗

bioRxiv2020

Identification of new insect Host Defense Peptides (HDP) from Dung Beetles (Coleoptera: Scarabaeidae) transcriptomes

IntroductionThe Coleoptera Scarabaeidae family is one of the most diverse groups of insects on the planet, living in complex microbiological environments. Their immune systems have evolved with the generation of host defense peptides but only a small number of these peptides have been characterized. MethodsIn this work two sources of information were retrieved: 1) De novo transcriptomic data from two species of neotropical Scarabidae (Dichotomius satanas and Ontophagus curvicornis); 2) Sequence data deposited in available databases. A BLAST-based search against the transcriptomes with a subset of sequences representative of the Host Defense Peptides (HDP). The HDP were described with the cecropin, defensin, attacin, and coleoptericin families; their physical/chemical and structural properties were described. ResultsThis work reports 155 novel sequences of HDP identified in 9 transcriptomes from seven species from the Coleoptera order: D. satanas (n= 76; 49.03%), O. curvicornis (n= 23; 14.83%), T. dichotomus (n= 18; 11.61%), O. nigriventris (n= 10; 6.45%), Heterochelus sp (n= 6; 3.87%), O. conspicillatum (n= 18; 11.61%) and P. japonica (n= 4; 2.58%). These sequences were identified based on similarity to known HDP insect families. New members of defensins (n= 58; 37.42%), cecropins (n= 18; 11.61%), attancins (n= 41; 26.45%) and coleoptericins (n= 38; 24.52%), with their physicochemical, structural characteristics, and sequence relationship to other insect HDP were analyzed. Conclusions155 new HDP could be identified, based on similarity to known HDP insect families on nine transcriptome sequences of seven beetle species. HighlightsO_LIThis work identified 155 novel sequences of HDP found in nine transcriptomes from seven Coleoptera species. C_LIO_LIDe novo transcriptomic data from two species of neotropical Scarabaeidae (Dichotomius satanas and Ontophagus curvicornis). C_LIO_LIIn silico prediction of physicochemical properties, structural features, sequence similarity, and antimicrobial activity of Scarabaeidae HDP. C_LI

molecular biology↗

bioRxiv2020

Benchtop micro-mashing: high-throughput, robust, experimental beer brewing

Brewing science is undergoing a renaissance with the use of modern analytical chemistry and microbiology techniques. However, these modern analytical tools and techniques are not necessarily aligned with the scale and scope of brewing science. In particular, brewing processes can be time consuming, ingredient intensive, and require specialised technical equipment. These drawbacks compound with the need for appropriate numbers of replicates for adequately powered experimental design. Here, we describe a micro-scale mash method that can be performed using a common laboratory benchtop shaker/incubator, allowing for high throughput mashing and easy sample replication for statistical analysis. Proteomic profiles at both the protein and peptide levels were consistent between the 1 mL micro-mash and a 23 L Braumeister mash, and both mash scales produced wort with equivalent fermentable sugar and free amino acid profiles. The experimental flexibility offered by our micro-mash method allowed us to investigate the effects of altered mash parameters on the beer brewing proteome.

biochemistry↗

bioRxiv2020

Forensic analysis of Soil Microbiomes: Linking Evidence to a Geographic Location

Over the past two decades, advances in molecular biology have greatly expanded our understanding of microbiomes - the diverse assemblages of microorganisms that inhabit the human body as well as the world around us, and applications in microbiome science have become an active area of research. Differences in the diversity (i.e., richness) and composition of microbiomes has been found to be informative in varied areas of science, including human health, agronomy, and forensic science. Soil harbors microbiomes that vary based on many factors, including the geology of the soil (e.g., sand, silt, or clay), climate, and use of the soil. As a result, the microbiological composition of any two soil samples will never be exactly alike. This inherent variation between microbiomes of different locations has proven to be specific enough to be potentially useful in forensic investigations to associate a person or piece of evidence to a source site. In this study, a soil microbiome was extracted from the sock of a criminal suspect and compared to the microbiome of soil samples taken from locations traveled to by the suspect. The locations analyzed varied in their soil microbiome composition, and the microbiome profiled from the sock was found to be most similar to the location where the suspect was thought to have left the body of a murder victim. These results provide a case study illustrating that information contained in a soil microbiome may be applied to link evidence to the location where a crime took place, potentially serving as an investigative tool in law enforcement.

bioinformatics↗

bioRxiv2020

Micro-biogeography greatly matters for competition: Continuous chaotic bioprinting of spatially-controlled bacterial microcosms

Cells do not work alone but instead function as collaborative micro-societies. The spatial distribution of different bacterial strains (micro-biogeography) in a shared volumetric space, and their degree of intimacy, greatly influences their societal behavior. Current microbiological techniques are commonly focused on the culture of well-mixed bacterial communities and fail to reproduce the micro-biogeography of polybacterial societies. Here, fine-scale bacterial microcosms are bioprinted using chaotic flows induced by a printhead containing a static mixer. This straightforward approach (i.e., continuous chaotic bioprinting) enables the fabrication of hydrogel constructs with intercalated layers of bacterial strains. These multi-layered constructs are used to analyze how the spatial distributions of bacteria affect their social behavior. Bacteria within these biological microsystems engage in either cooperation or competition, depending on the degree of shared interface. Remarkably, the extent of inhibition in predator-prey scenarios increases when bacteria are in greater intimacy. Furthermore, two Escherichia coli strains exhibit competitive behavior in well-mixed microenvironments, whereas stable coexistence prevails for longer times in spatially structured communities. Finally, the simultaneous extrusion of four inks is demonstrated, enabling the creation of higher complexity scenarios. Thus, chaotic bioprinting will contribute to the development of a greater complexity of polybacterial microsystems, tissue-microbiota models, and biomanufactured materials.

bioengineering↗

bioRxiv2020

Annotation of ribosomal protein mass peaks in MALDI-TOF mass spectra of bacterial species and their phylogenetic significance

Although MALDI-TOF mass spectrometry based microbial identification has achieved a level of accuracy that facilitate its use in classifying microbes to the species and strain level, questions remain on the identities of the mass peaks profiled from individual microbial species. Specifically, in the popular approach of comparing the mass spectrum of known and unknown microbes for identification purposes, the identities of the mass peaks were not taken into consideration. This study sought to determine if ribosomal proteins could account for some of the mass peaks profiled in MALDI-TOF mass spectra of different bacterial species. Using calculated molecular mass of ribosomal proteins for annotating mass peaks in bacterial species MALDI-TOF mass spectra downloaded from the SpectraBank database, this study revealed that ribosomal proteins could account for the low molecular weight mass peaks of <10000 Da. However, contrary to published reports, ribosomal proteins could not account for most of the mass peaks profiled. In particular, the data revealed that between 1 and 6 ribosomal protein mass peaks could be annotated in each mass spectrum. Annotated ribosomal proteins were S16, S17, S18, S20 and S21 from the small ribosome subunit, and L27, L28, L29, L30, L31, L31 Type B, L32, L33, L34, L35 and L36 from the large ribosome subunit. The ribosomal proteins with the most number of mass peak annotations were L36 and L29, with L34, L33, and L31 completing the list of ribosomal proteins with large number of annotations. Given the highly conserved nature of most ribosomal proteins, possible phylogenetic significance of the annotated ribosomal proteins were investigated through reconstruction of maximum likelihood phylogenetic trees. Results revealed that except for ribosomal protein L34, L31, L36 and S18, all annotated ribosomal proteins hold phylogenetic significance under the criteria of recapitulation of phylogenetic cluster groups present in the phylogeny of 16S rRNA. Phylogenetic significance of the annotated ribosomal proteins was further verified by the phylogenetic tree constructed based on the concatenated amino acid sequence of L29, S16, S20, S17, L27 and L35. Finally, analysis of the structure of the annotated ribosomal proteins did not reveal a high conservation of structure of the ribosomal proteins. Collectively, small low molecular weight (<10000 Da) ribosomal proteins could annotate some of the mass peaks in MALDI-TOF mass spectra of various bacterial species, and most of the ribosomal proteins hold phylogenetic significance. However, structural analysis did not identify a conserved structure for the annotated ribosomal proteins. Annotation of ribosomal protein mass peaks in MALDI-TOF mass spectra highlighted the deep biological basis inherent in the mass spectrometry-based microbial identification method. Subject areas biochemistry, biotechnology, microbiology, evolution, ecology Significance of the workWhile MALDI-TOF MS has been successfully used in identification of different microbes to the species and strain level through the comparison of mass spectra of known and unknown microbes, the approach (known as mass spectrum fingerprinting) remains lacking in the biological basis that underpins the technique. This study sought to uncover some of the biological basis that underpins MALDI-TOF MS microbial identification through the annotation of profiled mass peaks with ribosomal proteins. Previous studies have linked different ribosomal proteins to mass peaks in MALDI-TOF mass spectra of bacteria; however, broad spectrum verification of the finding across multiple species across different genera remain lacking. Using a collection of MALDI-TOF mass spectra of 110 bacterial species and strains catalogued in SpectraBank, this study sought to annotate ribosomal protein mass peaks in the mass spectra. Results revealed that small, low molecular weight ribosomal proteins of molecular mass < 10000 Da could annotate between 1 and 6 mass peaks in the catalogued mass spectra. This was smaller than the number of ribosomal proteins mass peaks postulated by previous studies. Overall, 16 ribosomal proteins (S16, S17, S18, S20, S21, L27, L28, L29, L30, L31, L31 Type B, L32, L33, L34, L35, and L36) were annotated with the most number of mass peaks annotations coming from L36 and L29. Reconstruction of phylogenetic trees of the annotated ribosomal proteins revealed that most of the ribosomal proteins hold phylogenetic significance with respect to the phylogeny of 16S rRNA. This provided further evidence that a deep biological basis is present in the approach of using mass spectrometry profiling of biomolecules for identifying bacterial species. HighlightsO_LIRibosomal protein mass peaks were annotated in MALDI-TOF mass spectra of bacterial species across multiple genera. C_LIO_LIAnnotated ribosomal proteins were S16, S17, S18, S20, S21 for the small ribosome subunit, and L27, L28, L29, L30, L31, L31 Type B, L32, L33, L34, L35, L36 for the large ribosome subunit. C_LIO_LIBetween 1 and 6 ribosomal protein mass peaks were annotated per mass spectrum, a number significantly lower than that implied by other studies. C_LIO_LIAnnotated ribosomal proteins were small, low molecular weight ribosomal proteins of molecular mass < 10000 Da. C_LIO_LIPhylogenetic tree reconstruction revealed the phylogenetic significance of most annotated ribosomal proteins except ribosomal protein L34, L31, L36 and S18. C_LIO_LIMulti-locus sequence typing of L29, S16, S20, S17, L27 and L35 further showed the phylogenetic significance of ribosomal proteins in recapitulating the phylogeny of 16S rRNA. C_LIO_LIStructural analysis of annotated ribosomal proteins did not find conserved structure. Thus, the reasons for the annotation of particular ribosomal proteins over others remain unknown. C_LI

bioinformatics↗

bioRxiv2020

Ribosomal proteins could explain the phylogeny of Bacillus species

Protein translation is a highly conserved process in biology. As participants of translation, ribosomal proteins in the large and small subunits of the ribosomes are likely to be highly conserved; thus, could they be endowed with sufficient sequence diversity to chronicle the evolutionary history of different species in the same or different genus? Using different Bacillus species as a model system, this study sought to examine if ribosomal proteins could reproduce the maximum likelihood phylogeny described by 16S rRNA of the investigated Bacillus species. Bacillus species investigated were Bacillus amyloliquefaciens, Bacillus cereus, Bacillus licheniformis, Bacillus megaterium, Bacilluspumilus, Bacillus subtilis, and Bacillus thuringiensis. Results revealed that ribosomal proteins could be categorized into four different groups depending on their extent in reproducing the 16S rRNA phylogeny of the different Bacillus species. The first group comprises ribosomal protein that could reproduce all the phylogenetic positions of the Bacillus species accurately. These ribosomal proteins were ribosomal protein L6, L7/12, L9, L13, L24, L32, S3, S9, S12, S15, S16, S17, and S18. Ribosomal proteins that hold partial phylogenetic significance constitutes the second group where the ribosomal proteins could reproduce the major branches of the 16S rRNA phylogenetic tree but differ in the placement of one or two Bacillus species. In general, this group of ribosomal proteins had difficulty differentiating B. licheniformis and B. pumilus at the sequence level. Members of this group of ribosomal protein include ribosomal protein L22, L29, L30, L31 Type B, L33, L35, S1, S4, S5, S6, S7, S8, S11, S13, S19, and S20. The third group of ribosomal proteins were those which were highly conserved at the sequence level, and which could not differentiate the different Bacillus species. These ribosomal proteins were ribosomal protein L5, L36, S2, S10, and S21. Finally, there were also ribosomal proteins that randomly placed the different Bacillus species into phylogenetic positions not in sync with those depicted by the 16S rRNA phylogenetic tree. These ribosomal proteins were ribosomal protein L7Ae, L17, L20, L23, L27, L28, L31, L34 and S14 Type Z. Overall, members of all four groups of ribosomal proteins came from both the large and small ribosome subunits which meant that evolutionary forces exerted selective pressure on both subunits but at differing extents. Collectively, specific ribosomal proteins could reproduce the phylogeny of different Bacillus species as described by the gold standard phylogenetic marker, 16S rRNA, which highlighted that co-evolutionary processes could be at work in shaping the evolution of ribosomal proteins and rRNA in close contact with each other in the ribosome. Subject areasecology, biochemistry, biotechnology, microbiology, cell biology Significance of the work16S rRNA is the gold standard phylogenetic marker used to inform the evolutionary relationships between different species across the three domains of life. Given that 16S rRNA is nestled in the ribosomes together with a consortium of ribosomal proteins each with unique structural and enzymatic functions, could ribosomal proteins be used similarly as phylogenetic markers for informing species divergence and relationships? Specifically, as part of the highly conserved ribosome important to protein translation, do ribosomal proteins possess sufficient sequence diversity to help chronicle the evolutionary relationships between different species? By reconstructing the maximum likelihood phylogenetic tree of different Bacillus species, this study revealed that ribosomal proteins fall into four categories concerning their utility for informing phylogeny between different species of the same genus. Specifically, there existed ribosomal proteins able to accurately reproduce the phylogenetic tree described by 16S rRNA. On the other hand, there were ribosomal proteins that hold only partial phylogenetic significance where they could reproduce the major branches of the reference phylogenetic tree but differ in the placement of one or two species along the tree. Besides the above two categories, they were also ribosomal proteins whose sequence diversity was not sufficient to help differentiate between different Bacillus species. Finally, another class of ribosomal proteins did not chronicle the evolutionary trajectories of the different species resulting in phylogenetic tree with random placement of the different species. Overall, evolutionary forces likely exerted different selection forces on different ribosome subunits as well as individual ribosomal protein that resulted in the differentiation of their utility as phylogenetic markers of different species of the same genus. Co-evolution between ribosomal proteins as well as between ribosomal proteins and rRNA might underpin part of the evolutionary history chronicled by individual ribosomal proteins, thereby, endowing them with phylogenetic significance. HighlightsO_LIRibosomal proteins were found to be useful in describing the phylogeny of different Bacillus species compared to the gold standard phylogenetic marker, 16S rRNA. C_LIO_LIBy examining the maximum likelihood phylogenetic tree reconstructed, ribosomal proteins could be categorized into four groups with differing phylogenetic significance. C_LIO_LIThe first group comprises ribosomal proteins able to accurately reproduce all the phylogenetic positions of different Bacillus species relative to 16S rRNA phylogenetic tree. This group include ribosomal protein L6, L7/12, L9, L13, L24, L32, S3, S9, S12, S15, S16, S17, and S18. C_LIO_LIThe second group refers to ribosomal proteins able to reproduce the major branches of the 16S rRNA phylogenetic tree but lacks in the correct placement of one or two Bacillus species. These ribosomal proteins were L22, L29, L30, L31 Type B, L33, L35, SI, S4, S5, S6, S7, S8, Sll, S13, S19, and S20. C_LIO_LIThe third group of ribosomal proteins are ones with highly conserved sequence unable to differentiate between different Bacillus species. It comprised ribosomal proteins L5, L36, S2, S10, and S21. C_LIO_LIThe final group of ribosomal proteins did not chronicle the evolutionary forces acting on the different Bacillus species and generated phylogenetic trees with random placement of the different Bacillus species. These ribosomal proteins were L7Ae, L17, L20, L23, L27, L28, L31, L34 and S14 Type Z. C_LI

evolutionary biology↗

bioRxiv2020

A computational strategy for rapid on-site 16S metabarcoding with Oxford Nanopore sequencing

The investigation of microbial communities through nucleotide sequencing has become an essential asset in environmental science, not only for research oriented activities but also for on-site monitoring; one technology, in particular, holds great promises for its application directly in the field: the Oxford Nanopore Technologies (ONT) MinION sequencer is a portable and affordable device, that produces long reads, with a remarkable sequencing output (in terms of bases/hour). One of the most common approaches in microbiological investigations through sequencing is the analysis of the 16S rRNA gene, known as 16S metabarcoding. Only recently the application of MinION has extended to 16S metabarcoding; to date, a limitation is still represented by the available computational protocols: due to the intrinsic, unique features of the technology ONT long reads cannot be adequately analyzed with tools developed for previous technologies (e.g. for Illumina). In this work a computational pipeline, specifically tailored to the usage of ONT reads in 16S metabarcoding, is developed, tested and discussed. This study is particularly addressed to on site evaluations, for environmental investigations or monitoring, where running time, costs and overall efficient usage of resources are particularly important.

genomics↗

bioRxiv2020

Attempted fractionation of LB Lennox medium via reversed phase high performance liquid chromatography

Mass balance analysis is a highly useful tool for chemical engineering analysis of biological processes. Specifically, composition and amounts of inputs to a cell could be correlated with measurement of metabolic byproducts and outputs for inferring metabolic fluxes flowing through specific cellular metabolic pathways. However, the composition of many common microbiological growth medium remains ill-defined with batch-to-batch variation. Thus, a need exists for developing methods for effective fractionation and separation of common growth medium. Using 5 g/L yeast extract, 10 g/L tryptone, and LB Lennox medium as model systems, this work attempted the fractionation of the three complex growth mixtures using C-18 reversed phase high performance liquid chromatography (RP-HPLC). Results revealed no effective fractionation of the three mixtures. More importantly, experiment results indicated that appropriate choice of detection wavelength for visualizing the chromatogram made a huge difference to understanding the effectiveness of fractionation achieved. Specifically, in the case of yeast extract, tryptone and LB Lennox medium, 194 nm may be a more appropriate detection wavelength compared to 280 nm. Collectively, C-18 RP-HPLC was not effective in separating 5 g/L yeast extract, 10 g/L tryptone and LB Lennox medium with hydrophilic mobile phases (ethanol/water mixture). Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY Short description: Visualization of the reversed phase HPLC chromatogram at 194 nm revealed broad peaks and lack of distinct peaks indicative of fractionation of LB Lennox medium by the chromatography method. More importantly, complex ensemble of different components in the growth medium present a significant separation challenge that precludes separation or fractionation by modern liquid chromatography methods. Overall, combinatorial use of both hydrophobic and hydrophilic mobile phase with a C-18 reversed phase column could reveal the presence of a couple of main fractions in a mixture otherwise unable to be separated into distinct components. C_FIG_DISPLAY

biochemistry↗

bioRxiv2020

Occurrence and distribution of fecal indicators and pathogenic bacteria in seawater and Perna perna mussel in the Gulf of Annaba (Southern Mediterranean)

The brown mussel Perna perna is a marine bivalve that is widely distributed and consumed along the east coast of Algeria. Due to its filter-feeding capacity, this mollusk can accumulate large quantities of pathogenic microorganisms from the surrounding waters, thus acting as bio-indicator of coastal environments. The objective of this study is to investigate the occurrence and distribution of fecal indicators and pathogenic bacteria in seawaters and mussels collected from four different sites in the Gulf of Annaba through physicochemical, biochemical and molecular analysis. The obtained results revealed that the levels of fecal indicator bacteria (FIB) were alarmingly high at Sidi Salem and Rezgui Rachid when compared with the two other sites (p < 0.05) and largely exceeded the permissible limits. Besides, P. perna collected from all sites were several fold more contaminated by these germs than seawater samples, notably, during the warm season of the study period. Biochemical and molecular analysis showed that isolated bacteria from both environmental compartments were mostly potentially pathogenic species such as E. coli, Salmonella, Staphylococcus, Klebsiella, Pseudomonas and Proteus. These principal findings demonstrate the strong involvement of anthropogenic activities on the microbiological quality of the Gulf and highlight the role of P. perna as an effective bio-indicator of the bacteriological quality of coastal waters.

ecology↗

bioRxiv2020

BugSeq: a highly accurate cloud platform for long-read metagenomic analyses

BackgroundAs the use of nanopore sequencing for metagenomic analysis increases, tools capable of performing long-read taxonomic classification in a fast and accurate manner are needed. Existing tools were either designed for short-read data (eg. Centrifuge) or take days to analyse modern sequencer outputs (eg. MetaMaps). ResultsWe present BugSeq, a novel, highly accurate metagenomic classifier for nanopore reads. BugSeq (F1=0.91-0.95) offers better read classification than MetaMaps (F1=0.89-0.94) in a fraction of the time. BugSeq significantly improves on the accuracy of Centrifuge (F1=0.79-0.93) while offering competitive run times. We apply BugSeq to metagenomic sequencing of 41 samples from patients with lower respiratory tract infections and show that it produces greater concordance with microbiological culture and qPCR compared with "Whats In My Pot" analysis. ConclusionBugSeq is deployed to the cloud for easy and scalable long-read metagenomic analyses. BugSeq is freely available for non-commercial use at https://bugseq.com/free. Contactsam@bugseq.com

bioinformatics↗

bioRxiv2020

Klebsiella MALDI TypeR: a web-based tool for Klebsiella identification based on MALDI-TOF mass spectrometry

MotivationKlebsiella species are increasingly multidrug resistant pathogens affecting human and animal health and are widely distributed in the environment. Among these, the Klebsiella pneumoniae species complex (KpSC), which includes seven phylogroups, is an important cause of community and hospital infections. In addition, the Klebsiella oxytoca species complex (KoSC) also causes hospital infections and antibiotic-associated haemorrhagic colitis. The unsuitability of widely used clinical microbiology methods to distinguish species within each of these species complexes leads to high rates of misidentifications that are masking the true clinical significance and potential epidemiological specificities of individual species. ResultsWe developed a web-based tool, Klebsiella MALDI TypeR, a platform-independent and user-friendly application that enables uploading raw data from MALDI-TOF mass spectrometer to identify Klebsiella isolates at the species complex and phylogroup levels. The tool is based on a database of previously identified biomarkers that are specific for either the species complex, individual phylogroups, or related phylogroups, and is available at https://maldityper.pasteur.fr.

bioinformatics↗

bioRxiv2020

Vaginal Microbiome Topic Modelling of Laboring Ugandan Women With and Without Fever

The composition of the maternal vaginal microbiome may influence the duration of pregnancy, onset of labor and even neonatal outcomes. Maternal microbiome research in sub Saharan-Africa has focused on non-pregnant and postpartum composition of the vaginal microbiome. We examined the vaginal microbiome composition of 99 laboring Ugandan women using routine microbiology and 16S ribosomal DNA sequencing from two hypervariable regions (V1-V2 and V3-V4), using standard hierarchical methods. We then introduce Grades of Membership (GoM) modeling for the vaginal microbiome, a method often used in the text mining machine learning literature. Leveraging GoM models, we create a basis composed of a small number of microbial topics whose linear combination optimally represents each patient yielding more accurate associations. We identified relationships between defined communities and the presentation or absence of intrapartum fever. Using a random forest model we showed that by including novel microbial topic models we improved upon clinical variables to predict maternal fever. We also show by integrating clinical variables with a microbial topic model into this model found young maternal age, fever report earlier in the current pregnancy, and longer labors, as well as a more diverse, less Lactobacillus dominated microbiome were features of labor associated with intrapartum fever. These results better define relationships between presentation or absence of intrapartum fever, demographics, peripartum course, and vaginal microbial communities, and improve our understanding of the impact of the microbiome on maternal and neonatal infection risk.

genomics↗

bioRxiv2020

GUNC: Detection of Chimerism and Contamination in Prokaryotic Genomes

Genomes are critical units in microbiology, yet ascertaining quality in prokaryotic genomes remains a formidable challenge. We present GUNC (the Genome UNClutterer), a tool that accurately detects and quantifies genome chimerism based on the lineage homogeneity of individual contigs using a genomes full complement of genes. GUNC complements existing approaches by targeting previously underdetected types of contamination: we conservatively estimate that 5.7% of genomes in GenBank, 5.2% in RefSeq, and 15-30% of pre-filtered high quality metagenome-assembled genomes in recent studies are undetected chimeras. GUNC provides a fast and robust tool to substantially improve prokaryotic genome quality. Source code (GPLv3+): https://github.com/grp-bork/gunc

bioinformatics↗

bioRxiv2020

Shedding Light on Microbial Dark Matter with A Universal Language of Life

The majority of microbial genomes have yet to be cultured, and most proteins predicted from microbial genomes or sequenced from the environment cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the full functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. The scientific community needs a means to capture the functionally and evolutionarily relevant features underlying biology, independent of our incomplete reference databases. Such a model can form the basis for transfer learning tasks, enabling downstream applications in environmental microbiology, medicine, and bioengineering. Here we present LookingGlass, a deep learning model capturing a "universal language of life". LookingGlass encodes contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, distinguishing reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass is the first contextually-aware, general purpose pre-trained "biological language" representation model for short-read DNA sequences. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth. AvailabilityThe pretrained LookingGlass model and the transfer learning-derived models demonstrated in this paper are available in the LookingGlass release v1.01. The open source fastBio Github repository and python package provides classes and functions for training and fine tuning deep learning models with biological data2. Code for reproducing analyses presented in this paper are available as an open source Github repository3.

bioinformatics↗

bioRxiv2021

Publication practices during the COVID-19 pandemic: Biomedical preprints and peer-reviewed literature

The coronavirus pandemic introduced many changes to our society, and deeply affected the established in biomedical sciences publication practices. In this article, we present a comprehensive study of the changes in scholarly publication landscape for biomedical sciences during the COVID-19 pandemic, with special emphasis on preprints posted on bioRxiv and medRxiv servers. We observe the emergence of a new category of preprint authors working in the fields of immunology, microbiology, infectious diseases, and epidemiology, who extensively used preprint platforms during the pandemic for sharing their immediate findings. The majority of these findings were works-in-progress unfitting for a prompt acceptance by refereed journals. The COVID-19 preprints that became peer-reviewed journal articles were often submitted to journals concurrently with the posting on a preprint server, and the entire publication cycle, from preprint to the online journal article, took on average 63 days. This included an expedited peer-review process of 43 days and journals production stage of 15 days, however there was a wide variation in publication delays between journals. Only one third of COVID-19 preprints posted during the first nine months of the pandemic appeared as peer-reviewed journal articles. These journal articles display high Altmetric Attention Scores further emphasizing a significance of COVID-19 research during 2020. This article will be relevant to editors, publishers, open science enthusiasts, and anyone interested in changes that the 2020 crisis transpired to publication practices and a culture of preprints in life sciences.

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