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Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological system. Adaptive Laboratory Evolution (ALE) leverages nature’s problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study presents a novel and first of its kind meta-analysis workflow to derive data-driven strain designs from aggregate ALE mutational data using rich mutation annotations, statistical and structural biology methods. The mutational dataset consolidated and utilized in this study contained 63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13,957 observed mutations. High-level trends across the entire dataset were established and revealed that ALE-derived strain designs will largely be gene-centric, as opposed to non-coding, and a relatively small number of variants (approx. 4) can significantly alter cellular states and provide benefits which range from an increase in fitness to a complete necessity for survival. Three novel experimentally validated designs relevant to metabolic engineering applications are presented as use cases for the workflow. Specifically, these designs increased growth rates with glycerol as a carbon source through a point mutation to glpK and a truncation to cyaA or increased tolerance to toxic levels of isobutyric acid through a pykF truncation. These results demonstrate how strain designs can be extracted from aggregated ALE data to enhance strain design efforts.Abstract Figure

We investigated the psychiatric symptomatology and the protracted symptoms in recently recovered COVID-19 patients. This cross-sectional study assessed 284 patients recruited from a tertiary hospital. Patients completed a web-based survey on socio-demographic data, past medical/psychiatric history, and additional information relevant to the outbreak conditions. The psychiatric status was assessed using the Impact of Events Scale-Revised (IES-R), Hospital Anxiety and Depression Scale (HADS), Pittsburgh Sleep Quality Index (PSQI), and MINI suicidality scale. Patients completed a checklist for the acute symptom burden and protracted symptoms that were experienced after the acute infection. After a mean of 50 days following the diagnosis of COVID-19, 98 patients (34.5%) reported clinically significant PTSD, anxiety, and/or depression, with PTSD being the most common condition reported (25.4%). One hundred and eighteen patients (44.3%) reported one or more protracted symptom(s), with fatigue, muscle aches, alteration of smell/taste, headache and difficulty in concentration, being the most common symptoms reported. Predictors of PTSD symptom severity were the female gender, past traumatic events, protracted symptoms, perceived stigmatization, and a negative view on the seriousness of the COVID-19 pandemic. Binary logistic regression analysis showed that PTSD symptom severity was the sole independent predictor of the presence of protracted symptoms. Our results suggest that COVID-19 patients may be prone to substantial psychological distress in the first months after the infection. The protracted symptoms were also frequent in this period, and these were related to the posttraumatic psychiatric morbidity. Both the psychiatric morbidity and the protracted symptoms were independent of the initial infection severity. Further research on the neurobiological commonalities between the protracted symptoms and PTSD in COVID-19 patients is warranted.

Tolerance to high product concentrations is a major barrier to achieving economically viable processes for bio-based chemical production. Chemical tolerance mechanisms are often unknown, thus their rational design is not achievable. To reveal unknown tolerance mechanisms we used an automated platform to evolveEscherichia colito grow in previously toxic concentrations of 11 chemicals that have applications as polymer precursors, chemical intermediates, or biofuels. Re-sequencing of isolates from 88 independently evolved populations, reconstruction of mutations, and cross-compound tolerance profiling was employed to uncover general and specific tolerance mechanisms. We found that: 1) the broad tolerance of strains towards chemicals varied significantly depending on the chemical stress condition under which the strain was evolved; 2) the strains that acquired high levels of NaCl tolerance also became broadly tolerant to most chemicals; 3) genetic tolerance mechanisms included alterations in regulatory, cell wall, transcriptional and translational functions, as well as more chemical-specific mechanisms related to transport and metabolism; 4) using pre-tolerized starting strains can significantly enhance subsequent production of chemicals when a production pathway is inserted; and 5) only a subset of the evolved isolates showed improved production indicating that this approach is especially useful when a large number of independently evolved isolates are screened for production. We provide a comprehensive genotype-phenotype map based on identified mutations and growth phenotypes for 224 chemical tolerant strains.