An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization

Background: Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology have facilitated an accelerated development in clinical microbiology and our understanding of pathogens. Objective: For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification. Method: The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple command-line tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample. Results: The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value. Conclusion: Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.

Download Full-text

UNAGI: an automated pipeline for nanopore full-length cDNA sequencing uncovers novel transcripts and isoforms in yeast

Functional & Integrative Genomics ◽

10.1007/s10142-020-00732-1 ◽

2020 ◽

Vol 20 (4) ◽

pp. 523-536

Author(s):

Mohamad Al kadi ◽

Nicolas Jung ◽

Shingo Ito ◽

Shoichiro Kameoka ◽

Takashi Hishida ◽

...

Keyword(s):

Full Length ◽

Cdna Sequencing ◽

Full Length Cdna ◽

Novel Transcripts ◽

Automated Pipeline

Download Full-text

TUP1, CPH1 and EFG1 Make Independent Contributions to Filamentation in Candida albicans

Genetics ◽

10.1093/genetics/155.1.57 ◽

2000 ◽

Vol 155 (1) ◽

pp. 57-67 ◽

Cited By ~ 4

Author(s):

Burkhard R Braun ◽

Alexander D Johnson

Keyword(s):

Candida Albicans ◽

Environmental Conditions ◽

Target Genes ◽

Single Cells ◽

Filamentous Growth ◽

Surface Proteins ◽

Pathway Expression ◽

Separate Pathway ◽

The Common ◽

Growth Pathway

Abstract The common fungal pathogen, Candida albicans, can grow either as single cells or as filaments (hyphae), depending on environmental conditions. Several transcriptional regulators have been identified as having key roles in controlling filamentous growth, including the products of the TUP1, CPH1, and EFG1 genes. We show, through a set of single, double, and triple mutants, that these genes act in an additive fashion to control filamentous growth, suggesting that each gene represents a separate pathway of control. We also show that environmentally induced filamentous growth can occur even in the absence of all three of these genes, providing evidence for a fourth regulatory pathway. Expression of a collection of structural genes associated with filamentous growth, including HYR1, ECE1, HWP1, ALS1, and CHS2, was monitored in strains lacking each combination of TUP1, EFG1, and CPH1. Different patterns of expression were observed among these target genes, supporting the hypothesis that these three regulatory proteins engage in a network of individual connections to downstream genes and arguing against a model whereby the target genes are regulated through a central filamentous growth pathway. The results suggest the existence of several distinct types of filamentous forms of C. albicans, each dependent on a particular set of environmental conditions and each expressing a unique set of surface proteins.

Download Full-text

A deep learning-based and fully automated pipeline for thoracic aorta geometric analysis and TEVAR planning from computed tomography

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeaa356.251 ◽

2021 ◽

Vol 22 (Supplement_1) ◽

Author(s):

S Saitta ◽

F Sturla ◽

A Caimi ◽

A Riva ◽

MC Palumbo ◽

...

Keyword(s):

Aortic Arch ◽

Thoracic Aorta ◽

Geometric Analysis ◽

Ground Truth ◽

Radius Of Curvature ◽

Geometric Features ◽

Anatomical Landmarks ◽

Landing Zone ◽

Increased Risk ◽

Automated Pipeline

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ministry of Publich Health - Ricerca Corrente Introduction Thoracic endovascular aortic repair (TEVAR) represents a well-established alternative to open repair in selected patients. Its preoperative feasibility assessment and planning requires a computational tomography (CT)-based analysis of the geometric aortic features to identify an adequate proximal and distal landing zone (LZ) for endograft deployment. Yet, controversies persist on the definition and methods of measurement of specific geometric features of the LZs, including angulation and tortuosity, which are associated with an increased risk of postoperative endograft failure. In this respect, the development of a preoperative image processing method that provides an automatic and highly reproducible 3D identification of critical geometric features and specific anatomical landmarks, thus reducing the time and uncertainties related to manual segmentation, remains a largely unmet clinical need. In this study, we developed and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, recognize the relevant anatomical landmarks and LZs, and quantifies the geometry of the aortic arch in each proximal LZ s (i.e. 0 to 3). Methods Ninety CT scans of healthy aortas were retrieved, being the study conceived as a proof of concept analysis. The thoracic aorta was manually segmented by five independent and expert operators. 72 scans with the corresponding ground truth segmentations were randomly selected and used to train the CNN, which was based on a 3D U-Net architecture. The other 18 scans were used to test the CNN-based segmentations. The fully automated pipeline was obtained by integrating the CNN, 3D geometry skeletonization, and processing of the aortic centerline and wall via computational geometry (Figure). The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation and tortuosity calculated according to previously published work. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA), and the different landing zones in each arch type. Results The CNN segmentation yielded a mean Dice score of 0.94 with respect to manual ground truth segmentations. Standard arches were characterized by significantly larger radius of curvature (p = 0.002) and lower tortuosity in zone 3 (p = 0.004) vs. CILCA arches. For both standard and CILCA arches, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p < 0.0001), angulation (p < 0.0001) and tortuosity (p < 0.0001). Conclusions We developed a CNN-based automated pipeline for the automated, and reliable geometric quantification of standard and CILCA aortic arches. This tool has the potential to support TEVAR pre-procedural planning in a real clinical setting. Abstract Figure. Automatic pipeline scheme

Download Full-text

Aggregate Preamble Sequence Design and Detection for Massive IoT with Deep Learning

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2021.3064868 ◽

2021 ◽

pp. 1-1

Author(s):

Ahmed Elhamy Mostafa ◽

Vincent W.S. Wong ◽

Yong Zhou ◽

Robert Schober ◽

Zhe Luo ◽

...

Keyword(s):

Deep Learning ◽

Sequence Design

Download Full-text

Increasing Solvent Tolerance to Improve Microbial Production of Alcohols, Terpenoids and Aromatics

Microorganisms ◽

10.3390/microorganisms9020249 ◽

2021 ◽

Vol 9 (2) ◽

pp. 249

Author(s):

Thomas Schalck ◽

Bram Van den Bergh ◽

Jan Michiels

Keyword(s):

Stress Responses ◽

Small Rnas ◽

Defense Mechanisms ◽

Industrial Processes ◽

Solvent Tolerance ◽

Adaptive Responses ◽

Environment Friendly ◽

Pathway Optimization ◽

Polymer Precursors ◽

Product Accumulation

Fuels and polymer precursors are widely used in daily life and in many industrial processes. Although these compounds are mainly derived from petrol, bacteria and yeast can produce them in an environment-friendly way. However, these molecules exhibit toxic solvent properties and reduce cell viability of the microbial producer which inevitably impedes high product titers. Hence, studying how product accumulation affects microbes and understanding how microbial adaptive responses counteract these harmful defects helps to maximize yields. Here, we specifically focus on the mode of toxicity of industry-relevant alcohols, terpenoids and aromatics and the associated stress-response mechanisms, encountered in several relevant bacterial and yeast producers. In practice, integrating heterologous defense mechanisms, overexpressing native stress responses or triggering multiple protection pathways by modifying the transcription machinery or small RNAs (sRNAs) are suitable strategies to improve solvent tolerance. Therefore, tolerance engineering, in combination with metabolic pathway optimization, shows high potential in developing superior microbial producers.

Download Full-text

Enhancing Terpene Yield from Sugars via Novel Routes to 1-Deoxy-d-Xylulose 5-Phosphate

Applied and Environmental Microbiology ◽

10.1128/aem.02920-14 ◽

2014 ◽

Vol 81 (1) ◽

pp. 130-138 ◽

Cited By ~ 30

Author(s):

James Kirby ◽

Minobu Nishimoto ◽

Ruthie W. N. Chow ◽

Edward E. K. Baidoo ◽

George Wang ◽

...

Keyword(s):

Bacterial Species ◽

Xylose Reductase ◽

Pentose Phosphate ◽

Terpene Biosynthesis ◽

Content Type ◽

E Coli ◽

Pathway Expression ◽

Terpene Synthesis ◽

Selection For

ABSTRACTTerpene synthesis in the majority of bacterial species, together with plant plastids, takes place via the 1-deoxy-d-xylulose 5-phosphate (DXP) pathway. The first step of this pathway involves the condensation of pyruvate and glyceraldehyde 3-phosphate by DXP synthase (Dxs), with one-sixth of the carbon lost as CO2. A hypothetical novel route from a pentose phosphate to DXP (nDXP) could enable a more direct pathway from C5sugars to terpenes and also circumvent regulatory mechanisms that control Dxs, but there is no enzyme known that can convert a sugar into its 1-deoxy equivalent. Employing a selection for complementation of adxsdeletion inEscherichia coligrown on xylose as the sole carbon source, we uncovered two candidate nDXP genes. Complementation was achieved either via overexpression of the wild-typeE. coliyajOgene, annotated as a putative xylose reductase, or via various mutations in the nativeribBgene.In vitroanalysis performed with purified YajO and mutant RibB proteins revealed that DXP was synthesized in both cases from ribulose 5-phosphate (Ru5P). We demonstrate the utility of these genes for microbial terpene biosynthesis by engineering the DXP pathway inE. colifor production of the sesquiterpene bisabolene, a candidate biodiesel. To further improve flux into the pathway from Ru5P, nDXP enzymes were expressed as fusions to DXP reductase (Dxr), the second enzyme in the DXP pathway. Expression of a Dxr-RibB(G108S) fusion improved bisabolene titers more than 4-fold and alleviated accumulation of intracellular DXP.

Download Full-text