A CRISPR-enhanced metagenomic NGS test to improve pandemic preparedness.

The lack of preparedness for detecting and responding to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogen (i.e., COVID-19) has caused enormous harm to public health and the economy. Testing strategies deployed on a population scale at day zero, i.e., the time of the first reported case, would be of significant value. Next-generation sequencing (NGS) has such capabilities; however, it has limited detection sensitivity for low-copy-number pathogens. Here, we leverage the CRISPR-Cas9 system to effectively remove abundant sequences not contributing to pathogen detection and show that NGS detection sensitivity of SARS-CoV-2 approaches that of RT-qPCR. The resulting sequence data can also be used for variant strain typing, co-infection detection, and individual human host response assessment, all in a single molecular and analysis workflow. This NGS work flow is pathogen agnostic and, therefore, has the potential to transform how large-scale pandemic response and focused clinical infectious disease testing are pursued in the future.

Tele-ophthalmology: Need of the hour.

Telemedicine and tele-ophthalmology have been in existence since many years, but have recently gained more importance in the present scenario of pandemic COVID-19. The attitude and perception of the doctors and patients has been changing gradually. Telemedicine has many advantages including providing care in inaccesible areas.In the present scenario, tele-ophthalmology gives an oppurtunity to patient for seeking consultation while also protecting against the contagion. There are many barriers faced by the patients and doctors that have restricted use of this technology in the past. However, with a systematic approach to designing the best suited technology, these barriers can be overcome and user friendly platforms can be created. Furthermore, the demand and use of teleconsulation had increased presently in this area of pandemic. Recent survey conducted by the All India Ophthalmological Society also reveals that many ophthalmologists who have not used tele-ophthalmology in the past are more keen to use it presently. In this article, we have reviewed telemedicine and tele-ophthalmology literature on Google and PubMed to get a holistic idea towards teleconsultation, its advantages, increased importance and prefrence during COVID-19 pandemic and various barriers faced so that the known challenges can be understood, which can pave way for better understanding and future incorporation into practice.

Rapid and highly efficient mammalian cell engineering via Cas9 protein transfection.

CRISPR-Cas9 systems provide a platform for high efficiency genome editing that are enabling innovative applications of mammalian cell engineering. However, the delivery of Cas9 and synthesis of guide RNA (gRNA) remain as steps that can limit overall efficiency and ease of use. Here we describe methods for rapid synthesis of gRNA and for delivery of Cas9 protein/gRNA ribonucleoprotein complexes (Cas9 RNPs) into a variety of mammalian cells through liposome-mediated transfection or electroporation. Using these methods, we report nuclease-mediated indel rates of up to 94% in Jurkat T cells and 87% in induced pluripotent stem cells (iPSC) for a single target. When we used this approach for multigene targeting in Jurkat cells we found that two-locus and three-locus indels were achieved in approximately 93% and 65% of the resulting isolated cell lines, respectively. Further, we found that the off-target cleavage rate is reduced using Cas9 protein when compared to plasmid DNA transfection. Taken together, we present a streamlined cell engineering workflow that enables gRNA design to analysis of edited cells in as little as four days and results in highly efficient genome modulation in hard-to-transfect cells. The reagent preparation and delivery to cells is amenable to high throughput, multiplexed genome-wide cell engineering.

An integrated computational and experimental study for overproducing fatty acids in Escherichia coli.

Increasing demands for petroleum have stimulated sustainable ways to produce chemicals and biofuels. Specifically, fatty acids of varying chain lengths (C6-C16) naturally synthesized in many organisms are promising starting points for the catalytic production of industrial chemicals and diesel-like biofuels. However, bio-production of fatty acids from plants and other microbial production hosts relies heavily on manipulating tightly regulated fatty acid biosynthetic pathways. In addition, precursors for fatty acids are used along other central metabolic pathways for the production of amino acids and biomass, which further complicates the engineering of microbial hosts for higher yields. Here, we demonstrate an iterative metabolic engineering effort that integrates computationally driven predictions and metabolic flux analysis techniques to meet this challenge. The OptForce procedure was used for suggesting and prioritizing genetic manipulations that overproduce fatty acids of different chain lengths from C6 to C16 starting with wild-type E. coli. We identified some common but mostly chain-specific genetic interventions alluding to the possibility of fine-tuning overproduction for specific fatty acid chain lengths. In accordance with the OptForce prioritization of interventions, fabZ and acyl-ACP thioesterase were upregulated and fadD was deleted to arrive at a strain that produces 1.70 g/L and 0.14 g fatty acid/g glucose (∼39% maximum theoretical yield) of C14₋16 fatty acids in minimal M9 medium. These results highlight the benefit of using computational strain design and flux analysis tools in the design of recombinant strains of E. coli to produce free fatty acids.

Mathematical optimization applications in metabolic networks.

Genome-scale metabolic models are increasingly becoming available for a variety of microorganisms. This has spurred the development of a wide array of computational tools, and in particular, mathematical optimization approaches, to assist in fundamental metabolic network analyses and redesign efforts. This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks. In particular, three major types of studies are covered here including exploring model predictions, correction and improvement of models of metabolism, and redesign of metabolic networks for the targeted overproduction of a desired compound. Overall, the methods reviewed in this paper highlight the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.

Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA.

Malonyl-coenzyme A is an important precursor metabolite for the biosynthesis of polyketides, flavonoids and biofuels. However, malonyl-CoA naturally synthesized in microorganisms is consumed for the production of fatty acids and phospholipids leaving only a small amount available for the production of other metabolic targets in recombinant biosynthesis. Here we present an integrated computational and experimental approach aimed at improving the intracellular availability of malonyl-CoA in Escherichia coli. We used a customized version of the recently developed OptForce methodology to predict a minimal set of genetic interventions that guarantee a prespecified yield of malonyl-CoA in E. coli strain BL21 Star™. In order to validate the model predictions, we have successfully constructed an E. coli recombinant strain that exhibits a 4-fold increase in the levels of intracellular malonyl-CoA compared to the wild type strain. Furthermore, we demonstrate the potential of this E. coli strain for the production of plant-specific secondary metabolites naringenin (474mg/L) with the highest yield ever achieved in a lab-scale fermentation process. Combined effect of the genetic interventions was found to be synergistic based on a developed analysis method that correlates genetic modification to cell phenotype, specifically the identified knockout targets (ΔfumC and ΔsucC) and overexpression targets (ACC, PGK, GAPD and PDH) can cooperatively force carbon flux towards malonyl-CoA. The presented strategy can also be readily expanded for the production of other malonyl-CoA-derived compounds like polyketides and biofuels.

Low-cost and disposable pressure sensor mat for non-invasive sleep and movement monitoring applications.

Sleep has profound effects on the physical and mental well-being of an individual. The National Institutes of Health (NIH) Sleep Disorder Research Plan gives particular emphasis to non-invasive sleep monitoring methods. Older adults experience sleep fragmentation due to sleep disorders. Unobtrusive non-contact monitoring can be the only realistic solution for long term home-based sleep monitoring. The demand for a low-cost and non-invasive sleep monitoring system for in-home use is more than before due to an increasingly stressful life style. Cost and complexity of current sensor elements hinder the development of low-cost sleep monitoring devices for in-home use. This paper presents the design, development and implementation of a low-cost and disposable pressure sensor mat that could be useful for in-home sleep and movement monitoring applications. The sensor mat design is based on a compressible foam sandwiched between two orthogonal arrays of cPaper capacitance sensors. A low-cost conducting paper has been developed for use as the capacitance sensor electrode. Typical mat design uses a 3 mm thick foam with 5 mm row/column grid array shows that it has a measurement resolution of 0.1 PSI pressure. The resolution can be controlled by both modifying properties of the conducting paper and the foam. Since this pressure mat design is based on low-cost paper, the sensor electrodes are disposable or semi-durable and hence it is ideal for the use in point-of-care physiological monitoring, pervasive healthcare and consumer electronic devices.

Microbial 1-butanol production: Identification of non-native production routes and in silico engineering interventions.

The potential of engineering microorganisms with non-native pathways for the synthesis of long-chain alcohols has been identified as a promising route to biofuels. We describe computationally derived predictions for assembling pathways for the production of biofuel candidate molecules and subsequent metabolic engineering modifications that optimize product yield. A graph-based algorithm illustrates that, by culling information from BRENDA and KEGG databases, all possible pathways that link the target product with metabolites present in the production host are identified. Subsequently, we apply our recent OptForce procedure to pinpoint reaction modifications that force the imposed product yield in Escherichia coli. We demonstrate this procedure by suggesting new pathways and genetic interventions for the overproduction of 1-butanol using the metabolic model for Escherichia coli. The graph-based search method recapitulates all recent discoveries based on the 2-ketovaline intermediate and hydroxybutyryl-CoA but also pinpoints one novel pathway through thiobutanoate intermediate that to the best of our knowledge has not been explored before.

OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions.

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis.