Christoph Börlin

Cultivated meat is an emerging biotechnology that aims to produce edible tissues in an ethical and sustainable manner. However, the recreation of skeletal muscle tissue that replicates the protein composition and sensory characteristics of traditional meat is a major challenge. Skeletal muscle tissue engineering requires non-animal-based scaffolds which are inexpensive and food-safe, while meeting specific mechanical requirements with respect to viscosity, stress-relaxation and stiffness. While many of these characteristics can be fulfilled by alginate-based biomaterials, a key limitation of alginate is its lack of intrinsic attachment sites for animal cells, preventing efficient adhesion, differentiation and tissue formation. Here, we established a screening platform to evaluate extracellular matrix (ECM)-mimicking peptides as functionalisations of alginate scaffolds in 2D. Our platform enables high-throughput assessment of cell/peptide interactions, serving as a predictive tool for 3D tissue constructs. Our screen identified two RGD-containing sequences (vitronectin- and fibronectin-mimicking peptides) as most effective in promoting attachment and myogenic fusion of bovine satellite cells. Notably, these peptides outperformed more complex mixtures containing up to seven different ECM-mimicking peptides. Our findings provide a streamlined approach for optimising biomaterial functionalisations for cultivated meat applications, and lay the groundwork for future advancements in scalable, sustainable skeletal muscle tissue engineering.

Budding yeast Saccharomyces cerevisiae has great potential as a host organism for various biorefinery applications. Nevertheless, the utilization of renewable plant biomass as feedstock for yeast in industrial applications remains a bottleneck, largely due to the presence of inhibitory substances such as acetic acid that are released in the biomass pretreatment processes. Exposure to acetic acid leads to different cellular stress mechanisms, several of which are directed by transcription factors. In this work, the role of the transcription factors Pdr1 and Yap1 in acetic acid tolerance was investigated using ChIP-exo and CRISPR interference/activation (CRISPRi/a). Pdr1 is the main regulator of the pleiotropic drug response, whereas Yap1 governs the oxidative stress response. CRISPRa targeting YAP1 for overexpression conferred a higher specific growth rate of S. cerevisiae , whereas CRISPRi-based downregulation of PDR1 proved to be beneficial for growth in medium containing acetic acid. ChIP-exo experiments showed increased binding of Pdr1 or Yap1 to their target promoters in the presence of acetic acid, and a large number of promoters were bound by either transcription factor. Promoters of genes involved in amino acid synthesis or encoding ABC transporters had the highest level of binding enrichment in the presence of acetic acid. The results highlight the potential for developing more acetic acid-tolerant yeast by altering the expression of transcription factor-encoding genes and demonstrate how expression can be fine-tuned by CRISPRi/a. IMPORTANCE: Biotechnological conversion of plant biomass into a variety of commodity chemicals and specialty molecules is an important step towards a bioeconomy. This study highlights the importance of two transcription factors, Pdr1 and Yap1, in the tolerance of Saccharomyces cerevisiae to acetic acid, a common inhibitor in bioprocesses using lignocellulosic biomass. CRISPR interference/activation and ChIP-exo were used to manipulate the expression and binding of these transcription factors in response to acetic acid stress. The study provides new insights into adaptation to acetic acid and suggests ways to improve yeast performance in industrial applications.

In order to control gene expression, regulatory DNA variants are commonly designed using random synthetic approaches with mutagenesis and screening. This however limits the size of the designed DNA to span merely a part of a single regulatory region, whereas the whole gene regulatory structure including the coding and adjacent non-coding regions is involved in controlling gene expression. Here, we prototype a deep neural network strategy that models whole gene regulatory structures and generates de novo functional regulatory DNA with prespecified expression levels. By learning directly from natural genomic data, without the need for large synthetic DNA libraries, our ExpressionGAN can traverse the whole sequence-expression landscape to produce sequence variants with target mRNA levels as well as natural-like properties, including over 30% dissimilarity to any natural sequence. We experimentally demonstrate that this generative strategy is more efficient than a mutational one when using purely natural genomic data, as 57% of the newly-generated highly-expressed sequences surpass the expression levels of natural controls. We foresee this as a lucrative strategy to expand our knowledge of gene expression regulation as well as increase expression control in any desired organism for synthetic biology and metabolic engineering applications.

The main transcriptional regulator of leucine biosynthesis in the yeast Saccharomyces cerevisiae is the transcription factor Leu3. It has previously been reported that Leu3 always binds to its target genes, but requires activation to induce their expression. In a recent large-scale study of high-resolution transcription factor binding site identification, we showed that Leu3 has divergent binding sites in different cultivation conditions, thereby questioning the results of earlier studies. Here, we present a follow-up study using chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate the influence of leucine supplementation on Leu3 binding activity and strength. With this new data set we are able to show that Leu3 exhibits changes in binding activity in response to changing levels of leucine availability.

Prostatic luminal epithelial cells secrete high levels of acetylated polyamines into the prostatic lumen, sensitizing them to perturbations of connected metabolic pathways. Enhanced flux is driven by spermidine/spermine N1-acetyltransferase (SSAT) activity, which acetylates polyamines leading to their secretion and drives biosynthetic demand. The methionine salvage pathway recycles one-carbon units lost to polyamine biosynthesis to the methionine cycle to overcome stress. Prostate cancer (CaP) relies on methylthioadenosine phosphorylase (MTAP), the rate-limiting enzyme, to relieve strain. Here, we show that inhibition of MTAP alongside SSAT upregulation is synergistic in androgen sensitive and castration recurrent CaP models in vitro and in vivo. The combination treatment increases apoptosis in radical prostatectomy ex vivo explant samples. This unique high metabolic flux through polyamine biosynthesis and connected one carbon metabolism in CaP creates a metabolic dependency. Enhancing this flux while simultaneously targeting this dependency in prostate cancer results in an effective therapeutic approach potentially translatable to the clinic.

Transcription factors (TF) are central to transcriptional regulation, but they are often studied in relative isolation and without close control of the metabolic state of the cell. Here, we describe genome-wide binding (by ChIP-exo) of 15 yeast TFs in four chemostat conditions that cover a range of metabolic states. We integrate this data with transcriptomics and six additional recently mapped TFs to identify predictive models describing how TFs control gene expression in different metabolic conditions. Contributions by TFs to gene regulation are predicted to be mostly activating, additive and well approximated by assuming linear effects from TF binding signal. Notably, using TF binding peaks from peak finding algorithms gave distinctly worse predictions than simply summing the low-noise and high-resolution TF ChIP-exo reads on promoters. Finally, we discover indications of a novel functional role for three TFs; Gcn4, Ert1 and Sut1 during nitrogen limited aerobic fermentation. In only this condition, the three TFs have correlated binding to a large number of genes (enriched for glycolytic and translation processes) and a negative correlation to target gene transcript levels.

The decrease of sequencing cost in the recent years has made genome-wide studies of transcription factor (TF) binding through chromatin immunoprecipitation methods like ChIP-seq and chromatin immunoprecipitation with lambda exonuclease (ChIP-exo) more accessible to a broader group of users. Especially with ChIP-exo, it is now possible to map TF binding sites in more detail and with less noise than previously possible. These improvements came at the cost of making the analysis of the data more challenging, which is further complicated by the fact that to this date no complete pipeline is publicly available. Here we present a workflow developed specifically for ChIP-exo data and demonstrate its capabilities for data analysis. The pipeline, which is completely publicly available on GitHub, includes all necessary analytical steps to obtain a high confidence list of TF targets starting from raw sequencing reads. During the pipeline development, we emphasized the inclusion of different quality control measurements and we show how to use these so users can have confidence in their obtained results.

One of the fundamental processes that determine cellular fate is regulation of gene transcription. Understanding these regulatory processes is therefore essential for understanding cellular responses to changes in environmental conditions. At the core promoter, the regulatory region containing the transcription start site (TSS), all inputs regulating transcription are integrated. Here, we used Cap Analysis of Gene Expression (CAGE) to analyze the pattern of TSSs at four different environmental conditions (limited in ethanol, limited in nitrogen, limited in glucose and limited in glucose under anaerobic conditions) using the Saccharomyces cerevisiae strain CEN.PK113–7D. With this experimental setup, we were able to show that the TSS landscape in yeast is stable at different metabolic states of the cell. We also show that the spatial distribution of transcription initiation events, described by the shape index, has a surprisingly strong negative correlation with measured gene expression levels, meaning that genes with higher expression levels tend to have a broader distribution of TSSs. Our analysis supplies a set of high-quality TSS annotations useful for metabolic engineering and synthetic biology approaches in the industrially relevant laboratory strain CEN.PK113–7D, and provides novel insights into yeast TSS dynamics and gene regulation.

One of the fundamental processes that determine cellular fate is regulation of gene transcription. Understanding these regulatory processes is therefore essential for understanding cellular responses to changes in environmental conditions. At the core promoter, the regulatory region containing the transcription start site (TSS), all inputs regulating transcription are integrated. Here, we used Cap Analysis of Gene Expression (CAGE) to analyze the pattern of transcription start sites at four different environmental conditions (limited in ethanol, limited in nitrogen, limited in glucose and limited in glucose under anaerobic conditions) using the Saccharomyces cerevisiae strain CEN.PK113-7D. With this experimental setup we were able to show that the TSS landscape in yeast is stable at different metabolic states of the cell. We also show that the shape index, a characteristic feature of each TSS describing the spatial distribution of transcription initiation events, has a surprisingly strong negative correlation with the measured expression levels. Our analysis supplies a set of high quality TSS annotations useful for metabolic engineering and synthetic biology approaches in the industrially relevant laboratory strain CEN.PK113-7D, and provides novel insights into yeast TSS dynamics and gene regulation.

Dietary folate levels alter the kinetics and molecular mechanism of prostate cancer recurrence in the CWR22 model Dietary folate levels alter the kinetics and molecular mechanism of prostate cancer recurrence in the CWR22 model