Phototrophic microorganisms are promising resources for green biotechnology. Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic growth is still insufficiently understood. We provide a quantitative analysis of light-limited, light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were quantified to monitor proteome allocation as a function of growth rate. Among other physiological acclimations, we identify an upregulation of the translational machinery and downregulation of light harvesting components with increasing light intensity and growth rate. The resulting growth laws are discussed in the context of a coarse-grained model of phototrophic growth and available data obtained by a comprehensive literature search. Our insights into quantitative aspects of cyanobacterial acclimations to different growth rates have implications to understand and optimize photosynthetic productivity.

The prediction of protein localization, such as in the extracellular space, from high-throughput data is essential for functional downstream inference. It is well accepted that some secreted proteins go through the classic endoplasmic reticulum-Golgi pathway with the guidance of a signal peptide. However, a large number of proteins have been found to reach the extracellular space by following unconventional secretory pathways. There remains a demand for reliable prediction of unconventional protein secretion (UPS). Here, we present OutCyte, a fast and accurate tool for the prediction of UPS, which for the first time has been built upon experimentally determined UPS proteins. OutCyte mediates the prediction of protein secretion in two steps: first, proteins with N-terminal signals are accurately filtered out; second, proteins without N-terminal signals are classified as UPS or intracellular proteins based on physicochemical features directly generated from their amino acid sequences. We are convinced that OutCyte will play a relevant role in the annotation of experimental data and will therefore contribute to further characterization of the extracellular nature of proteins by considering the commonly neglected UPS proteins.OutCyte has been implemented as a web server atwww.outcyte.com.

Background: The molecular signature of endometrial receptivity still remains barely understood, especially when focused on the possible benefit of therapeutical interventions and implantation-related pathologies. Therefore, the protein composition of tissue and isolated primary cells (endometrial stromal cells, ESCs) from endometrial scratchings of ART (Assisted Reproductive Techniques) patients with repeated implantation failure (RIF) was compared to volunteers with proven fertility during the time of embryo implantation (LH + 7). Furthermore, an analysis of the endometrial tissue of fertile women infused with human chorionic gonadotropin (hCG) was conducted. Methods: Endometrial samples (n = 6 RIF, n = 10 fertile controls) were split into 3 pieces: 1/3 each was frozen in liquid nitrogen, 1/3 fixed in PFA and 1/3 cultured. Protein lysates prepared from fresh frozen tissue were processed for mass spectrometric analysis. Results: Three proteins (EPPK1, BCLAF1 and PTMA) showed a statistically altered abundance in the endometrial tissue of RIF patients. Furthermore, pathways like metabolism, immune system, ferroptosis and the endoplasmic reticulum were altered in RIF patients. Remarkably, endometrial tissues of RIF patients showed a significantly higher (p-value = 9 × 10⁻⁸) protein intensity correlation (Pearson’s correlation coefficient = 0.95) compared to fertile women (Pearson’s correlation coefficient = 0.88). The in vivo infusion of hCG stimulated proteins of endocytosis, HIF1 signalling and chemokine production. Notably, patients suffering from RIF had a clinical pregnancy rate of 19% after the intrauterine infusion of hCG before embryo transfer (ET) compared to their failed previous cycles. Conclusion: Our study showed for the first time that the endometrial proteome composition of RIF patients differs from fertile controls during the window of implantation. The intrauterine infusion of hCG prior to an embryo transfer might improve the chemokine triggered embryo-endometrial dialogue and intensify the angiogenesis and immune response. From a clinical point of view, the hCG infusion prior to an embryo transfer might increase the pregnancy rate of RIF patients.

Germ cell tumors (GCTs) are the most common solid malignancies found in young men. Although they generally have high cure rates, metastases, resistance to cisplatin-based therapy, and late toxicities still represent a lethal threat, arguing for the need of new therapeutic options. In a previous study, we identified downregulation of the chromatin-remodeling SWI/SNF complex member ARID1A as a key event in the mode of action of the histone deacetylase inhibitor romidepsin. Additionally, the loss-of-function mutations re-sensitize different tumor types to various drugs, like EZH2-, PARP-, HDAC-, HSP90- or ATR-inhibitors. Thus, ARID1A presents as a promising target for synthetic lethality and combination therapy. In this study, we deciphered the molecular function of ARID1A and screened for the potential of two pharmacological ARID1A inhibitors as a new therapeutic strategy to treat GCTs. By CRISPR/Cas9, we generated ARID1A-deficient GCT cells and demonstrate by mass spectrometry that ARID1A is putatively involved in regulating transcription, DNA repair and the epigenetic landscape via DNA Polymerase POLE and the DNA methyltransferase 1-associated protein DMAP1. Additionally, ARID1A/ARID1A deficiency or pharmacological inhibition increased the efficacy of romidepsin and considerably sensitized GCT cells, including cisplatin-resistant subclones, towards ATR inhibition. Thus, targeting ARID1A in combination with romidepsin and ATR inhibitors presents as a new putative option to treat GCTs.

Predicting unconventional protein secretion is a much harder problem than predicting signal peptide-based protein secretion, both due to the small number of examples and due to the heterogeneity and the limited knowledge of the pathways involved, especially in eukaryotes. However, the idea that secreted proteins share certain properties regardless of the secretion pathway used made it possible to construct the prediction method SecretomeP in 2004. Here, we take a critical look at SecretomeP and its successors, and we also discuss whether multi-category subcellular location predictors can be used to predict unconventional protein secretion in eukaryotes. A new benchmark shows SecretomeP to perform much worse than initially estimated, casting doubt on the underlying hypothesis. On a more positive note, recent developments in machine learning may have the potential to construct new methods which can not only predict unconventional protein secretion but also point out which parts of a sequence are important for secretion.