Isobutanol production from cellobionic acid in Escherichia coli.

BackgroundLiquid fuels needed for the global transportation industry can be produced from sugars derived from plant-based lignocellulosics. Lignocellulosics contain a range of sugars, only some of which (such as cellulose) have been shown to be utilizable by microorganisms capable of producing biofuels. Cellobionic acid makes up a small but significant portion of lignocellulosic degradation products, and had not previously been investigated as an utilizable substrate. However, aldonic acids such as cellobionic acid are the primary products of a promising new group of lignocellulosic-degrading enzymes, which makes this compound group worthy of study. Cellobionic acid doesn't inhibit cellulose degradation enzymes and so its inclusion would increase lignocellulosic degradation efficiency. Also, its use would increase overall product yield from lignocellulose substrate. For these reasons, cellobionic acid has gained increased attention for cellulosic biofuel production.ResultsThis study describes the discovery that Escherichia coli are naturally able to utilize cellobionic acid as a sole carbon source with efficiency comparable to that of glucose and the construction of an E. coli strain able to produce the drop-in biofuel candidate isobutanol from cellobionic acid. The gene primarily responsible for growth of E. coli on cellobionic acid is ascB, a gene previously thought to be cryptic (expressed only after incurring specific mutations in nearby regulatory genes). In addition to AscB, the ascB knockout strain can be complemented by the cellobionic acid phosphorylase from the fungus Neurospora crassa. An E. coli strain engineered to express the isobutanol production pathway was successfully able to convert cellobionic acid into isobutanol. Furthermore, to demonstrate potential application of this strain in a sequential two-step bioprocessing system, E. coli was grown on hydrolysate (that was degraded by a fungus) and was successfully able to produce isobutanol.ConclusionsThese results demonstrate that cellobionic acid is a viable carbon source for biofuel production. This work suggests that with further optimization, a bacteria-fungus co-culture could be used in decreased-cost biomass-based biofuel production systems

Two-dimensional isobutyl acetate production pathways to improve carbon yield

For an economically competitive biological process, achieving high carbon yield of a target chemical is crucial. In biochemical production, pyruvate and acetyl-CoA are primary building blocks. When sugar is used as the sole biosynthetic substrate, acetyl-CoA is commonly generated by pyruvate decarboxylation. However, pyruvate decarboxylation during acetyl-CoA formation limits the theoretical maximum carbon yield (TMCY) by releasing carbon, and in some cases also leads to redox imbalance. To avoid these problems, we describe here the construction of a metabolic pathway that simultaneously utilizes glucose and acetate. Acetate is utilized to produce acetyl-CoA without carbon loss or redox imbalance. We demonstrate the utility of this approach for isobutyl acetate (IBA) production, wherein IBA production with glucose and acetate achieves a higher carbon yield than with either sole carbon source. These results highlight the potential for this multiple carbon source approach to improve the TMCY and balance redox in biosynthetic pathways

Isobutanol production from cellobionic acid in Escherichia coli.

BackgroundLiquid fuels needed for the global transportation industry can be produced from sugars derived from plant-based lignocellulosics. Lignocellulosics contain a range of sugars, only some of which (such as cellulose) have been shown to be utilizable by microorganisms capable of producing biofuels. Cellobionic acid makes up a small but significant portion of lignocellulosic degradation products, and had not previously been investigated as an utilizable substrate. However, aldonic acids such as cellobionic acid are the primary products of a promising new group of lignocellulosic-degrading enzymes, which makes this compound group worthy of study. Cellobionic acid doesn't inhibit cellulose degradation enzymes and so its inclusion would increase lignocellulosic degradation efficiency. Also, its use would increase overall product yield from lignocellulose substrate. For these reasons, cellobionic acid has gained increased attention for cellulosic biofuel production.ResultsThis study describes the discovery that Escherichia coli are naturally able to utilize cellobionic acid as a sole carbon source with efficiency comparable to that of glucose and the construction of an E. coli strain able to produce the drop-in biofuel candidate isobutanol from cellobionic acid. The gene primarily responsible for growth of E. coli on cellobionic acid is ascB, a gene previously thought to be cryptic (expressed only after incurring specific mutations in nearby regulatory genes). In addition to AscB, the ascB knockout strain can be complemented by the cellobionic acid phosphorylase from the fungus Neurospora crassa. An E. coli strain engineered to express the isobutanol production pathway was successfully able to convert cellobionic acid into isobutanol. Furthermore, to demonstrate potential application of this strain in a sequential two-step bioprocessing system, E. coli was grown on hydrolysate (that was degraded by a fungus) and was successfully able to produce isobutanol.ConclusionsThese results demonstrate that cellobionic acid is a viable carbon source for biofuel production. This work suggests that with further optimization, a bacteria-fungus co-culture could be used in decreased-cost biomass-based biofuel production systems

Incidence of and Risk Factors for Venous Thromboembolism Among Hospitalized Patients with Cancer and COVID-19: Report from the COVID-19 and Cancer Consortium (CCC19) Registry

Introduction: Hospitalized patients with COVID-19 may have increased risk of venous thromboembolism (VTE) and pulmonary embolism (PE). Cancer and anti-cancer therapies are well-known additional risk factors for VTE. Nonetheless, the VTE risk in patients with both cancer and COVID-19 infection remains unknown as recent studies have not found an association due to sample size limitations. We report the incidence of and risk factors for VTE and PE among hospitalized patients with cancer and COVID-19. Methods: The COVID-19 and Cancer Consortium (CCC19) developed an international retrospective cohort study (NCT04354701) to investigate the clinical course and complications of COVID-19 among adult patients with an active or previous history of cancer. For the current study, cumulative incidences of clinically detected VTE and PE were analyzed among hospitalized patients with laboratory confirmed SARS-CoV-2. Pre-specified subgroup analysis was performed to examine the interaction between intensive care unit (ICU) admission and recent anti-cancer therapy on VTE outcomes. Bivariable logistic regression analyses were conducted to assess the association between baseline variables and VTE; unadjusted odds ratios (OR) and 95% confidence interval (CI) were reported. These variables included age, sex, obesity (BMI>30), race/ethnicity, performance status, comorbidities, blood type, history of VTE, recent surgery, recent anti-cancer therapy, cancer subtype VTE risk grouping (adapted from Khorana Score), pre-admission anticoagulant or antiplatelet use, and ICU admission status. Results: From March 17, 2020 to July 31, 2020, 3914 patients were enrolled in the CCC19 registry. For the present analysis, patients were excluded if they had inadequate follow-up <4 weeks (n=950), were not admitted to the hospital (n=1008), or had unknown VTE outcomes (n=327). Among the 1629 hospitalized patients, the median follow-up was 35 days. Patients were comprised from 3 countries (92% US, 6% Canada, 2% Spain), with a median age of 70, 45% female, and a median comorbidity score of 3. Racial/ethnic breakdown included 44% White, 26% Black, 14% Hispanic, and 13% Other. A past history of VTE was reported in 9% of patients; pre-admission anticoagulant use and antiplatelet use were reported in 25% and 35% of patients, respectively. The most common cancer types included prostate (18%), breast (15%), and lymphoma (14%). Based on the VTE risk grouping adapted from the original Khorana Score, 34% were low-risk, 29% were high-risk, and 6% were very high-risk. The receipt of anti-cancer therapy within 3 months of diagnosis was observed in 39% of patients (17% cytotoxic chemotherapy, 11% targeted therapy, 7% endocrine therapy, and 5% immunotherapy). The overall incidence of inhospital VTE and PE was 9.3% and 5.2%, respectively. The corresponding estimates were 13.4% and 7.9% among the ICU subgroup. On bivariable analysis, significant predictors of VTE included ICU admission, recent anti-cancer therapy, active cancer status, cancer subtype VTE risk grouping, and pre-admission antiplatelet use (Table 1). Pre-admission anticoagulant use had significant associations with PE but not VTE. Multivariable adjustment is ongoing to identify independent risk factor for VTE and clarify the impact of pre-admission anticoagulant/antiplatelet use controlled for other potential confounders. Both ICU admission status and anti-cancer therapy increased the risk of VTE independently. Non-ICU patients not on anti-cancer therapy had the lowest incidence of VTE (4.5%), whose estimate was similar to that reported in the non-cancer hospitalized population with COVID-19 infection. Patients with either ICU admission or recent anti-cancer therapy had the intermediate risk (11.0%), whereas ICU patients with recent anti-cancer therapy had the highest risk (16.7%). We did not observe confounding or effect modification by the ICU subgroup on the association between anti-cancer therapy and VTE. Conclusion: In this cohort study of hospitalized patients with cancer and COVID-19, recent anti-cancer therapy, active disease, high-risk VTE cancer subtypes, and ICU admission have increased risk of VTE and PE, while pre-admission anticoagulant/antiplatelet therapy may reduce the risk. This information will aid in developing a risk prediction tool for VTE in hospitalized patients with cancer and COVID-19. Disclosures Kuderer: G1 Therapeutics: Consultancy; Total Health: Consultancy; Invitae: Consultancy; Beyond Springs: Consultancy; Bristol-Myers Squibb: Consultancy; celldex: Consultancy; Bayer: Consultancy; Spectrum Pharmaceuticals: Consultancy; Janssen: Consultancy. Warner:HemOnc.orgLLC: Other: Shareholder/Stockholder/Stock options; IBM Watson Health: Consultancy; Westat: Consultancy; National Cancer Institute: Research Funding. Shah:American Cancer Society and the Hope Foundation for Cancer Research: Research Funding; National Cancer Institute: Research Funding. Zon:Amagma Therapeutics.: Consultancy, Other: stockholder. Shah:Aspen Pharma: Research Funding. Gulati:Puma Biotechnology: Consultancy; AstraZeneca: Research Funding; Isoray: Research Funding. Khaki:Merck: Other: share/stockholder; Pfizer: Other: share/stockholder. Thompson:AIM Specialty Health, BMS, GlaxoSmithKline, Takeda, Via Oncology: Membership on an entity's Board of Directors or advisory committees; Synapse Precision Medical Council: Other: Travel expenses; Doximity: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees. Grivas:Oncogenex: Research Funding; Immunomedics: Research Funding; Debiopharm: Research Funding; Bavarian Nordic,: Research Funding; QED Therapeutics: Honoraria; Seattle Genetics: Honoraria; Roche: Honoraria; Pfizer: Honoraria, Research Funding; Mirati Therapeutics: Honoraria, Research Funding; Merck: Honoraria, Research Funding; Janssen: Honoraria; Heron Therapeutics: Honoraria; GlaxoSmithKline: Honoraria; Genzyme: Honoraria; Genentech: Honoraria, Research Funding; Foundation Medicine: Honoraria; Exelixis: Honoraria; EMD Serono: Honoraria; Driver: Honoraria; Clovis Oncology: Honoraria, Research Funding; Bristol-Myers Squibb,: Consultancy, Honoraria, Research Funding, Speakers Bureau; Biocept: Honoraria; Bayer: Honoraria, Research Funding; Astra Zeneca: Honoraria, Research Funding. de Lima Lopes:Bavarian Nordic: Research Funding; NOVARTIS: Research Funding; Tesaro: Research Funding; GSK: Research Funding; G1 Therapeutics: Research Funding; adaptimmune: Research Funding; BMS: Research Funding; Lilly: Research Funding; Merck Sharp & Dohme: Research Funding; Astra Zeneca: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Boehringer Ingelheim: Honoraria; Janssen: Research Funding; rgenix: Research Funding; Blueprint Medicines: Research Funding; Genentech: Research Funding; Roche: Research Funding; EMD Serono: Research Funding. Shyr:Roche: Consultancy; Novartis: Consultancy; Pfizer: Consultancy; Johnson & Johnson: Consultancy; GlaxoSmithKline: Consultancy; AstraZeneca: Consultancy, Speakers Bureau; Boehringer Ingelheim: Speakers Bureau; Eisai: Speakers Bureau. Pennell:Merck: Consultancy; Cota: Consultancy; Inivata: Consultancy; G1 Therapeutics: Consultancy; Astrazeneca: Consultancy; BMS: Consultancy; Eli Lilly: Consultancy; Amgen: Consultancy; Genentech: Consultancy. Friese:Eli Lilly: Consultancy; Patient-Centered Outcomes Research Institute: Membership on an entity's Board of Directors or advisory committees; Agency for Healthcare Research and Quality: Research Funding; National Cancer Institute: Research Funding; Merck Foundation: Research Funding; National Comprehensive Cancer Network: Research Funding; Pfizer: Research Funding; Eli Lilly: Consultancy. Patel:reast Cancer Research Foundation: Research Funding; Sanofi: Research Funding; Odonate Therapeutics: Research Funding; Radius: Honoraria; Genentech: Research Funding. Halmos:Foundation Medicine: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Guardant Health: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Boehringer-Ingelheim: Consultancy, Research Funding; Merck: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; AstraZeneca: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Eli-Lilly: Research Funding; Advaxis: Research Funding; Mirati: Research Funding; Takeda: Research Funding; GSK: Research Funding; AbbVie: Research Funding; Genentech: Consultancy; TPT: Consultancy. Choueiri:Pfizer: Consultancy, Honoraria, Research Funding; Pionyr: Consultancy, Other; Merck: Consultancy, Honoraria, Research Funding; Roche Products Limited: Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; F. Hoffmann-La Roche: Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Lilly: Consultancy, Research Funding; Peloton: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Tempest: Consultancy, Other; Lilly Ventures: Consultancy; International Patent Application No. PCT/US2018/12209, entitled "PBRM1 Biomarkers Predictive of Anti-Immune Checkpoint Response," filed January 3, 2018, claiming priority to U.S. Provisional Patent Application No. 62/445,094, filed January 11, 2017: Patents & Royalties; Prometheus Labs: Consultancy, Honoraria, Research Funding; Corvus: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria, Research Funding; Alexion: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Honoraria, Research Funding; Bristol Myers-Squibb/ER Squibb and sons LLC: Consultancy, Honoraria, Research Funding; Cerulean: Consultancy, Honoraria, Research Funding; Eisai: Consultancy, Honoraria, Research Funding; oundation Medicine Inc.: Consultancy, Honoraria, Research Funding; International Patent Application No. PCT/US2018/058430, entitled "Biomarkers of Clinical Response and Ben

The CoVID- TE risk assessment model for venous thromboembolism in hospitalized patients with cancer and COVID- 19

BackgroundHospitalized patients with COVID- 19 have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well- known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID- 19 is lacking.ObjectivesTo assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID- 19.MethodsAmong patients with cancer in the COVID- 19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90 days of COVID- 19- associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap.ResultsFrom March 17, 2020 to November 30, 2020, 2804 hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti- cancer therapy. A simplified RAM for VTE was derived and named CoVID- TE (Cancer subtype high to very- high risk by original Khorana score +1, VTE history +2, ICU admission +2, D- dimer elevation +1, recent systemic anti- cancer Therapy +1, and non- Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low- risk, 0- 2 points, n = 1423 vs. high- risk, 3+ points, n = 1034) where VTE occurred in 4.1% low- risk and 11.3% high- risk patients (c statistic 0.67, 95% confidence interval 0.63- 0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission.ConclusionsHospitalized patients with cancer and COVID- 19 have elevated thrombotic risks. The CoVID- TE RAM for VTE prediction may help real- time data- driven decisions in this vulnerable population.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170302/1/jth15463_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170302/2/jth15463.pd

The CoVID-TE risk assessment model for venous thromboembolism in hospitalized patients with cancer and COVID-19

BACKGROUND: Hospitalized patients with COVID-19 have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well-known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID-19 is lacking. OBJECTIVES: To assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID-19. METHODS: Among patients with cancer in the COVID-19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90 days of COVID-19-associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap. RESULTS: From March 17, 2020 to November 30, 2020, 2804 hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti-cancer therapy. A simplified RAM for VTE was derived and named CoVID-TE (Cancer subtype high to very-high risk by original Khorana score +1, VTE history +2, ICU admission +2, D-dimer elevation +1, recent systemic anti-cancer Therapy +1, and non-Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low-risk, 0-2 points, n = 1423 vs. high-risk, 3+ points, n = 1034) where VTE occurred in 4.1% low-risk and 11.3% high-risk patients (c statistic 0.67, 95% confidence interval 0.63-0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission. CONCLUSIONS: Hospitalized patients with cancer and COVID-19 have elevated thrombotic risks. The CoVID-TE RAM for VTE prediction may help real-time data-driven decisions in this vulnerable population