Biology subjects

Schumann, C.

Publications and source records attributed to Schumann, C..

5 recordsLinked to original sources

bioRxiv2024

Structure-guided engineering of α-ketoisocaproate dioxygenase increases isobutene production in Synechocystis sp. PCC 6803

Isobutene is a promising precursor for jet fuel due to its high energy density and favorable combustion properties. Light-driven bioproduction of isobutene has recently been investigated as an alternative strategy to crude oil refinement or fermentation-based manufacturing processes by harnessing the unicellular cyanobacterium Synechocystis sp. PCC 6803 and the -ketoisocaproate dioxygenase (RnKICD) from Rattus norvegicus. However, the obtained production level was not sufficient, partially due the promiscuous activity of RnKICD. The enzyme catalyzes both the reaction with {rho}-hydroxyphenylpyruvate (HPP) for homogentisate formation, as well as the reaction with -ketoisocaproate (KIC), the precursor for isobutene synthesis. Here, to overcome this bottleneck step in the isobutene biosynthesis, protein engineering was employed to improve RnKICD activity and in vivo isobutene production. Purified RnKICD variants were characterized by measuring in vitro KIC and HPP consumption rates, as well as isobutene formation rate. The active site mutations F336V, N363A altered the KIC and HPP consumption rates, while the KIC-to-isobutene conversion ratio was only marginally affected. Besides, the RnKICD variants F336V, N363A and F336V/N363A exhibited a substantially enhanced substrate selectivity for KIC over HPP. Among the examined engineered Synechocystis strains, Syn-F336V showed a 4-fold improvement in isobutene production, compared to the base strain (Syn-RnKICD). Our findings reveal that residues F336 and N363 play a crucial role in substrate interactions, as targeted mutations at these sites shifted the substrate selectivity towards KIC and elevated the in vivo isobutene production levels significantly. We conclude that engineering the active site of RnKICD is a potent tool for improving isobutene bioproduction in Synechocystis. HighlightsO_LIPromiscuous -ketoisocaproate dioxygenase (KICD) from Rattus norvegicus was overproduced in Synechocystis sp. PCC 6803. C_LIO_LIProtein engineering was used to improve substrate selectivity. C_LIO_LIThe generated F336V variant led to a 4-fold increase in isobutene production in vivo. C_LI

bioengineering↗

bioRxiv2023

A phase-field model for non-small cell lung cancer under the effects of immunotherapy

Formulating tumor models that predict growth under therapy is vital for improving patient-specific treatment plans. In this context, we present our recent work on simulating non-small-scale cell lung cancer (NSCLC) in a simple, deterministic setting for two different patients receiving an immunotherapeutic treatment. At its core, our model consists of a Cahn-Hilliard-based phase-field model describing the evolution of proliferative and necrotic tumor cells. These are coupled to a simplified nutrient model that drives the growth of the proliferative cells and their decay into necrotic cells. The applied immunotherapy decreases the proliferative cell concentration. Here, we model the immunotherapeutic agent concentration in the entire lung over time by an ordinary differential equation (ODE). Finally, reaction terms provide a coupling between all these equations. By assuming spherical, symmetric tumor growth and constant nutrient inflow, we simplify this full 3D cancer simulation model to a reduced 1D model. We can then resort to patient data gathered from computed tomography (CT) scans over several years to calibrate our model. For the reduced 1D model, we show that our model can qualitatively describe observations during immunotherapy by fitting our model parameters to existing patient data. Our model covers cases in which the immunotherapy is successful and limits the tumor size, as well as cases predicting a sudden relapse, leading to exponential tumor growth. Finally, we move from the reduced model back to the full 3D cancer simulation in the lung tissue. Thereby, we show the predictive benefits a more detailed patient-specific simulation including spatial information could yield in the future. Author summaryLung cancer is one of the deadliest diseases, with low long-term survival rates. Its treatment is still very heuristic since patients respond to the same treatment plans differs significantly. Therefore, patient-specific models for predicting tumor growth and the treatment response are necessary for clinicians to make informed decisions about the patients therapy and avoid a trial and error based approach. We made a first small step in that direction by introducing a model for simulating cancer growth and its treatment inside a 3D lung geometry. In this model, we represented tumor cells by a volume fraction field that varies over space and time. We described their evolution by a system of partial differential equations, which include patient- and treatment-specific parameters capturing the different responses of patients to the therapies. Our simulation results were compared to clinical data and showed that we can quantitatively describe the tumors behavior with a suitable parameter set. This enabled us to change therapies in simulation runs and analyze how these changes could have impacted the patients health.

cell biology↗

bioRxiv2021

Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits - the Hispanic/Latino Anthropometry Consortium

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite notable anthropometric variability with ancestry proportions, and a high burden of growth stunting and overweight/obesity in Hispanic/Latino populations. This address this knowledge gap, we analyzed densely-imputed genetic data in a sample of Hispanic/Latino adults, to identify and fine-map common genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (Stage 1, n=59,769) and validated our findings in 9 additional studies (HISLA Stage 2, n=9,336). We conducted a trans-ethnic GWAS with summary statistics from HISLA Stage 1 and existing consortia of European and African ancestries. In our HISLA Stage 1+2 analyses, we discovered one novel BMI locus, as well two novel BMI signals and another novel height signal, each within established anthropometric loci. In our trans-ethnic meta- analysis, we identified three additional novel BMI loci, one novel height locus, and one novel WHRadjBMI locus. We also identified three secondary signals for BMI, 28 for height, and two for WHRadjBMI. We replicated >60 established anthropometric loci in Hispanic/Latino populations at genome-wide significance--representing up to 30% of previously-reported index SNP anthropometric associations. Trans-ethnic meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our novel findings demonstrate that future studies may also benefit from leveraging differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

genetics↗

bioRxiv2020

Alpha-1 antitrypsin inhibits SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). To identify factors of the respiratory tract that suppress SARS-CoV-2, we screened a peptide/protein library derived from bronchoalveolar lavage, and identified 1-antitrypsin (1-AT) as specific inhibitor of SARS-CoV-2. 1-AT targets the viral spike protein and blocks SARS-CoV-2 infection of human airway epithelium at physiological concentrations. Our findings show that endogenous 1-AT restricts SARS-CoV-2 and repurposes 1-AT-based drugs for COVID-19 therapy.

microbiology↗

bioRxiv2019

Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had [≥]1 predicted loss of function carrier, and most genes (>69%) had [≥]10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

genomics↗