Biology subjects

Sauer, D.

Publications and source records attributed to Sauer, D..

5 recordsLinked to original sources

bioRxiv2026

Reconstructing tissue culture to improve Agrobacterium-mediated transformation of maize

The ability to insert, delete, or modify genetic information is crucial for mechanistic studies and biotechnological applications. However, efficient genetic transformation remains a major bottleneck for research in maize and many other crops. Here, we report an optimized Agrobacterium-mediated transformation platform based on systematic reconstruction of tissue culture handling in the maize inbred line A188. Refinement of callus induction, selection, and regeneration substantially improved recovery of transgenic plantlets. To distinguish independent T-DNA insertion events, we developed TAFLP (T-DNA Amplified Fragment Length Polymorphism), a simple and inexpensive assay that amplifies T-DNA flanking sequences and can be performed using standard laboratory equipment. Our enhanced transformation pipeline was also applicable to the inbred line B104 as well as to hygromycin and G418 selection systems, demonstrating broad utility of our method. We validated the platform for CRISPR/Cas9 mutagenesis and reporter line generation. Using this approach, we isolated new loss-of-function alleles of MAC1 and ACOZ1 and generated reporter lines for analysis of meiotic protein dynamics. Together, these results provide a broadly applicable framework for improving maize transformation efficiency and recovering independent transgenic and genome-edited events.

plant biology↗

bioRxiv2022

Structural basis of ion - substrate coupling in the Na+-dependent dicarboxylate transporter VcINDY

The Na+-dependent dicarboxylate transporter from Vibrio cholerae (VcINDY) is a prototype for the divalent anion sodium symporter (DASS) family. While the utilization of an electrochemical Na+ gradient to power substrate transport is well established for VcINDY, the structural basis of this coupling between sodium and substrate binding is not currently understood. Here, using a combination of cryo-EM structure determination, succinate binding and site-directed cysteine alkylation assays, we demonstrate that the VcINDY protein couples sodium- and substrate-binding via a previously unseen induced-fit mechanism. In the absence of sodium, substrate binding is abolished, with the succinate binding regions exhibiting increased flexibility, including HPinb, TM10b and the substrate clamshell motifs. Upon sodium binding, these regions become structurally ordered and create a proper binding site for the substrate. Taken together, these results provide strong evidence that VcINDYs induced-fit mechanism is a result of the sodium-dependent formation of the substrate binding site.

biophysics↗

bioRxiv2021

Diversity and prevalence of colibactin- and yersiniabactin encoding mobile genetic elements in enterobacterial populations: insights into evolution and co-existence of two bacterial secondary metabolite determinants

1 AbstractThe bacterial genotoxin colibactin interferes with the eukaryotic cell cycle by causing double-stranded DNA breaks. It has been linked to bacterially induced colorectal cancer in humans. Colibactin is encoded by a 54-kb genomic region in Enterobacteriaceae. The colibactin genes commonly co-occur with the yersiniabactin biosynthetic determinant. Investigating the prevalence and sequence diversity of the colibactin determinant and its linkage to the yersiniabactin operon in prokaryotic genomes, we discovered mainly species-specific lineages of the colibactin determinant and classified three main structural settings of the colibactin-yersiniabactin genomic region in Enterobacteriaceae. The colibactin gene cluster has a similar but not identical evolutionary track to that of the yersiniabactin operon. Both determinants could have been acquired on several occasions and/or exchanged independently between enterobacteria by horizontal gene transfer. Integrative and conjugative elements play(ed) a central role in the evolution and structural diversity of the colibactin-yersiniabactin genomic region. Addition of an activating and regulating module (clbAR) to the biosynthesis and transport module (clbB-S) represents the most recent step in the evolution of the colibactin determinant. In a first attempt to correlate colibactin expression with individual lineages of colibactin determinants and different bacterial genetic backgrounds, we compared colibactin expression of selected enterobacterial isolates in vitro. Colibactin production in the tested Klebsiella spp. and Citrobacter koseri strains was more homogeneous and generally higher than that in most of the E. coli isolates studied. Our results improve the understanding of the diversity of colibactin determinants and its expression level, and may contribute to risk assessment of colibactin-producing enterobacteria.

microbiology↗

bioRxiv2019

Using machine learning to predict organismal growth temperatures from protein primary sequences

BackgroundThe link between protein or nucleic acid sequence and biochemical or organismal phenotype is essential for understanding the molecular mechanisms of evolution, reverse ecology, and designing proteins and genes with specific properties. However, it is difficult to practically make use of the relationship between sequence and phenotype due to the complex relationship between sequence and folding or activity. ResultsHere, we predict the originating species optimal growth temperatures of individual protein sequences using trained machine learning models. Both multilayer perceptron and k Nearest Neighbor regression outperformed linear regression could predict the originating species optimal growth temperature from protein sequences, achieving a root mean squared error of 3.6 {degrees}C. Similar machine learning models could predict organismal optimal growth pH and oxygen tolerance, and the quantitative properties of individual proteins or nucleic acids. ConclusionsUsing multilayer perceptron and k Nearest Neighbor regressions, we were able to build models specific to individual protein or nucleic acid families that can predict a variety of quantitative phenotypes. This methodology will be useful the in silico screening of individual mutations for particular properties, and also effective in the predicting the phenotypes of uncharacterized biological sequences and organisms.

bioinformatics↗

bioRxiv2018

Prediction of Optimal Growth Temperature using only Genome Derived Features

Optimal growth temperature is a fundamental characteristic of all living organisms. Knowledge of this temperature is central to the study the organism, the thermal stability and temperature dependent activity of its genes, and the bioprospecting of its genome for thermally adapted proteins. While high throughput sequencing methods have dramatically increased the availability of genomic information, the growth temperatures of the source organisms are often unknown. This limits the study and technological application of these species and their genomes. Here, we present a novel method for the prediction of growth temperatures of prokaryotes using only genomic sequences. By applying the reverse ecology principle that an organisms genome includes identifiable adaptations to its native environment, we can predict a species optimal growth temperature with an accuracy of 4.69 {degrees}C root-mean-square error and a correlation coefficient of 0.908. The accuracy can be further improved for specific taxonomic clades or by excluding psychrophiles. This method provides a valuable tool for the rapid calculation of organism growth temperature when only the genome sequence is known.

bioinformatics↗