Biology subjects

Kolkhof, P.

Publications and source records attributed to Kolkhof, P..

5 recordsLinked to original sources

bioRxiv2026

Easy-to-use whole-genome sequencing workflows and standardized practices to uncover hidden genetic variation in Synechocystis PCC 6803 wild-type and knock-out strains

Knock-out mutants are often used to study gene function by disrupting a specific gene and comparing the mutant to a wild-type strain. Reliable interpretation, however, requires that the two strains differ only by the intended mutation and that the observed phenotype is caused specifically by the deleted gene. In the highly polyploid cyanobacterium Synechocystis sp. PCC 6803, this is particularly challenging because incomplete segregation can mask genetic heterogeneity or secondary suppressor mutations. The genetic variation among laboratory wild-type lines can further confound phenotypic analyses. We show that these challenges can be addressed by routine strain validation via whole-genome sequencing (WGS). To this end, we developed and tested user friendly workflows for short-read (Illumina), long-read (Oxford Nanopore Technologies; ONT), and hybrid data, providing standardized quality control, variant calling, and structural variant detection. We benchmarked their performance in detecting single-nucleotide polymorphisms (SNPs), small indels, and structural variants using simulated datasets across different coverages and mixed populations. Applying the workflows to three Synechocystis sp. PCC 6803 wild-type lines revealed multiple sequence and structural differences relative to the reference genome, including previously undescribed genetic variants, underscoring the importance of documenting the strain background and the value of long-read sequencing. Characterization of two independent 6-phosphogluconate dehydrogenase (gnd) knock-out mutants and their complemented strains highlighted how a failed rescue can reveal a phenotype unrelated to the intended knock-out. An automated literature analysis revealed that only a minority of the investigated Synechocystis studies that used knock-out mutants included complementation as a control (39%), whereas this practice is more common in studies involving Escherichia coli (63%) and Saccharomyces cerevisiae (55%). Based on these results, we propose a practical guide for standardizing knock-out phenotyping in Synechocystis PCC 6803. Combined with accessible workflows for routine whole-genome validation, this framework aims to support more robust and reproducible knock-out studies in the future.

microbiology↗

bioRxiv2026

Live-cell imaging enables reporter-free monitoring of the circadian rhythm in individual Synechocystis cells

In vivo monitoring of circadian rhythms depends on reliable and non-invasive detection methods. This is often achieved by expressing reporter genes heterologously under the control of a circadian promoter. The activity or fluorescence of the gene product is then used as a readout. To avoid the need for generation of such reporter strains, we recently established a reporter-free detection method for cyanobacterial batch cultures. To determine whether these rhythms are driven at the level of individual cells or result from population-based effects, such as gating of cell division, we analyzed individual Synechocystis sp. PCC 6803 cells by combining a microfluidic cultivation technique with multipoint time-lapse microscopy imaging at the single-cell resolution. Hundreds of time-lapse image sequences, acquired over a period of up to ten days, were processed using our deep learning cell segmentation workflow. Although the cells had been entrained by a 12-hour light-dark cycle, neither cell size nor cell division displayed circadian rhythms. This indicates the absence of circadian gating of cell division in Synechocystis. Instead, we observed circadian oscillation in the average brightness of the phase contrast of individual Synechocystis cells. To demonstrate how phase-contrast analysis of single cells can be complemented by backscatter analysis of batch cultures, we investigated the wildtype, a deletion mutant known to affect circadian rhythms ({Delta}kaiC3) and complementation strains at both, the single-cell and batch levels. We concluded that phase contrast and backscatter likely measured the same rhythmic changes in the refractive index of the cells. The method presented here will advance circadian research by enabling the analysis of circadian rhythms in individual cells without the need for expression of reporter molecules.

microbiology↗

bioRxiv2023

Superposition of target structures enables design of bi-stable RNA molecules with deep learning

The ability to design RNA molecules with specific structures and functions could facilitate research and developments in biotechnology, biology and pharmacy. Here we present a flexible RNA design framework based on deep learning that locally optimizes sequences by gradient-guided search methods. We demonstrate its effectiveness by designing bi-stable RNA molecules by superimposing conformer target structures.

bioinformatics↗

bioRxiv2020

Sequestration to lipid droplets promotes histone availability by preventing turnover of excess histones

Because dearth and overabundance of histones result in cellular defects, histone synthesis and demand are typically tightly coupled. In Drosophila embryos, histones H2B/H2A/H2Av accumulate on lipid droplets (LDs), cytoplasmic fat storage organelles. Without this binding, maternally provided H2B/H2A/H2Av are absent; however, the molecular basis of how LDs ensure histone storage is unclear. Using quantitative imaging, we uncover when during oogenesis these histones accumulate, and which step of accumulation is LD-dependent. LDs originate in nurse cells and are transported to the oocyte. Although H2Av accumulates on LDs in nurse cells, the majority of the final H2Av pool is synthesized in oocytes. LDs promote intercellular transport of the histone-anchor Jabba and thus its presence in the ooplasm. Jabba prevents ooplasmic H2Av from degradation, safeguarding the H2Av stockpile. Our findings provide insight into the mechanism for establishing histone stores during Drosophila oogenesis and shed light on the function of LDs as protein-sequestration sites.

cell biology↗

bioRxiv2016

Predicting gene expression level in E. coli from mRNA sequence information

MotivationThe accurate characterization of the translational mechanism is crucial for enhancing our understanding of the relationship between genotype and phenotype. In particular, predicting the impact of the genetic variants on gene expression will allow to optimize specific pathways and functions for engineering new biological systems. In this context, the development of accurate methods for predicting translation efficiency from the nucleotide sequence is a key challenge in computational biology.\n\nMethodsIn this work we present PGExpress, a binary classifier to discriminate between mRNA sequences with low and high translation efficiency in E. coli. PGExpress algorithm takes as input 12 features corresponding to RNA folding and anti-Shine-Dalgarno hybridization free energies. The method was trained on a set of 1,772 sequence variants (WT-High) of 137 essential E. coli genes. For each gene, we considered 13 sequence variants of the first 33 nucleotides encoding for the same amino acids followed by the superfolder GFP. Each gene variant is represented sequence blocks that include the Ribosome Binding Site (RBS), the first 33 nucleotides of the coding region (C33), the remaining part of the coding region (CC), and their combinations.\n\nResultsOur logistic regression-based tool (PGExpress) was trained using a 20-fold gene-based cross-validation procedure on the WT-High dataset. In this test PGExpress achieved an overall accuracy of 74%, a Matthews correlation coefficient 0.49 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.81. Tested on 3 sets of sequences with different Ribosome Binding Sites, PGExpress reaches similar AUC. Finally, we validated our method by performing in-house experiments on five newly generated mRNA sequence variants. The predictions of the expression level of the new variants are in agreement with our experimental results in E. coli.\n\nAvailabilityhttp://folding.biofold.org/pgexpress\n\nContactmarkus.kollmann@hhu.de, emidio.capriotti@unibo.it

bioinformatics↗