Biology subjects

Land, H.

Publications and source records attributed to Land, H..

5 recordsLinked to original sources

bioRxiv2026

Self-Sufficient Maturation and Catalysis of a Clade E CODH Encoded in a CooCTJ-Operon from Clostridium pasteurianum BC1

Carbon monoxide dehydrogenases (CODHs) are metalloenzymes central to microbial CO metabolism and CO2 fixation. We report the heterologous production and characterisation of Clostridium pasteurianum BC1 CODH-III (CpBC1CODH-III), from the phylogenetic clade E, co-expressed with its maturation machinery CooCTJ. CpBC1CODH-III shows moderate CO oxidation (150 U/mg) and CO2 reduction (0.568 U/mg) activities. Electron paramagnetic resonance (EPR) spectroscopy under varying redox conditions identified a rhombic signal at g {approx} 2.0, characteristic of reduced B-clusters, and a C-clusters at different stages (g {approx} 1.75, g {approx} 1.72), indicative of a bound CO2. Investigation of maturation effects showed that co-expression of CooCTJ stabilised CpBC1CODH-III production, but did not enhance maximum activity, which was primarily influenced by nickel availability. Comparative operon analysis with the well-studied clade F Rhodospirillum rubrum CODH (RrCODH) revealed high structural similarity in CODH and CooC, but significant divergence in CooJ, with conserved metal-binding regions identified via AlphaFold3 modelling and dot plot analysis. CpBC1CODH-III represents a unique example of a clade E CODH within a clade F genomic context, demonstrating intrinsic robustness in maturation and activity

biochemistry↗

bioRxiv2025

Investigating the native functions of -carbon monoxide dehydrogenase through genomic context analysis

Carbon monoxide dehydrogenases containing nickel-iron active sites ([NiFe]-CODHs) catalyze the reversible oxidation of CO to CO2, representing key targets for biocatalytic CO2 reduction. Despite dramatic differences in catalytic rates and O2 tolerance between CODH variants, the molecular basis for this functional diversity remains poorly understood. We applied comparative genomics and synteny analysis to investigate the biochemical roles of CODH clades A-F using 1,376 CODH and 1,545 hybrid cluster protein sequences. Around 30% of genomes encode multiple CODH isoforms. Analysis revealed distinct gene clustering patterns correlating with biochemical function. Clades A, E, and F exhibit a degree of distributional exclusivity. Clades C and D frequently co-occur with active CODHs, suggesting auxiliary roles. Operon architecture analysis revealed functional specialization: clade A links to acetyl-CoA synthase; clades A, E and F contain essential maturation machinery (CooC, CooJ, CooT) correlating with catalytic activity; clade B associates with transporters; clade C with electron transfer partners; clade D with transcriptional regulators. High CODH-HCP co-occurrence (except clade A) suggests functional or environmental interdependency. These findings establish clades A, E and F as primary biocatalyst targets while defining regulatory functions for clades C and D, providing a genomics framework for predicting CODH phenotypes.

bioinformatics↗

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↗

bioRxiv2020

Gene network modeling via TopNet reveals robust epistatic interactions between functionally diverse tumor critical mediator genes

Malignant cell transformation and the underlying genomic scale reprogramming of gene expression require cooperation of multiple oncogenic mutations. Notably, this cooperation is reflected in the synergistic regulation of downstream genes, so-called cooperation response genes (CRGs). CRGs impact diverse hallmark features of cancer cells and are not known to be functionally connected. Yet, they act as critical mediators of the cancer phenotype at an unexpectedly high frequency of >50%, as indicated by genetic perturbations. Here we demonstrate that CRGs function within a network of strong genetic interdependencies that are critical to the robustness of the malignant state. Our approach, termed TopNet, utilizes attractor-based ternary network modeling that takes the novel approach of incorporating uncertainty in the underlying gene perturbation data and is capable of identifying non-linear gene interactions. TopNet reveals topological gene network architecture that effectively predicts previously unknown, functionally relevant epistatic gene interactions, and thus, among a broad range of applications, has utility for identification of non-mutant targets for cancer intervention.

cancer biology↗

bioRxiv2017

Statistical Approaches to Decreasing the Discrepancy of Non-detects in qPCR Data

Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization, and statistical analysis, little attention has been given to qPCR non-detects - those reactions failing to produce a minimum amount of signal. While most current software replaces these non-detects with a value representing the limit of detection, recent work suggests that this introduces substantial bias in estimation of both absolute and differential expression. Recently developed single imputation procedures, while better than previously used methods, underestimate residual variance, which can lead to anti-conservative inference. We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of relevant model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. In the proposed modeling framework, there are three sources of uncertainty: parameter estimation, the missing data mechanism, and measurement error. All three sources of variability are incorporated in the multiple imputation and direct estimation algorithms. We demonstrate the applicability of these methods on three real qPCR data sets and perform an extensive simulation study to assess model sensitivity to misspecification of the missing data mechanism, to the number of replicates within the sample, and to the overall size of the data set. The proposed methods result in unbiased estimates of the model parameters; therefore, these approaches may be beneficial when estimating both absolute and differential gene expression. The developed methods are implemented in the R/Bioconductor package nondetects. The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments, providing more confidence in generating scientific hypotheses and performing downstream analysis.

bioinformatics↗