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Abstract Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions.

Abstract Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Abstract Development and progression of malignancies are accompanied and influenced by alterations in the surrounding immune microenvironment. Understanding the cellular and molecular interactions between immune cells and cancer cells has not only provided important fundamental insights into the disease, but has also led to the development of new immunotherapies. The C-type lectin Dendritic Cell ImmunoReceptor (DCIR) is primarily expressed by myeloid cells and is an important regulator of immune homeostasis, as demonstrated in various autoimmune, infectious and inflammatory contexts. Yet, the impact of DCIR on cancer development remains largely unknown. Analysis of available transcriptomic data of colorectal cancer (CRC) patients revealed that high DCIR gene expression is associated with improved patients’ survival, immunologically "hot" tumors and high immunologic constant of rejection, thus arguing for a protective and immunoregulatory role of DCIR in CRC. In line with these correlative data, we found that deficiency of DCIR1, the murine homologue of human DCIR, leads to the development of significantly larger tumors in an orthotopic murine model of CRC. This phenotype is accompanied by an altered phenotype of tumor-associated macrophages (TAMs) and a reduction in the percentage of activated effector CD4+ and CD8+ T cells in CRC tumors of DCIR1-deficient mice. Overall, our results show that DCIR promotes antitumor immunity in CRC, making it an attractive target for the future development of immunotherapies to fight the second deadliest cancer in the world.

Encystment is a process that allows free-living and parasitic amoebae to survive in unfavorable environments. Here, Bernard et al. provide detailed insights into the early stages of encystment of Acanthamoeba castellanii by integrating RNA-Seq, proteomics and phosphoproteomics data sets.

Amoebae found in aquatic and terrestrial environments encompass various pathogenic species, including the parasite Entamoeba histolytica and the free-living Acanthamoeba castellanii. Both microorganisms pose significant threats to public health, capable of inducing life-threatening effects on humans. These amoebae exist in two cellular forms: trophozoites and cysts. The trophozoite stage is the form used for growth and reproduction while the cyst stage is the resistant and disseminating form. Cysts occur after cellular metabolism slowdown due to nutritional deprivation or the appearance of environmental conditions unfavourable to the amoebae’s growth and division. The initiation of encystation is accompanied by the activation of stress responses, and scarce data indicate that encystation shares factors and mechanisms identified in stress responses occurring in trophozoites exposed to toxic compounds derived from human immune defence. Although some “omics” analyses have explored how amoebae respond to diverse stresses, these studies remain limited and rarely report post-translational modifications that would provide knowledge on the molecular mechanisms underlying amoebae-specific stress responses. In this review, we discuss ubiquitin-like proteins associated with encystation and cell survival during oxidative damage. We aim to shed light on the signalling pathways involved in amoebic defence mechanisms, with a focus on their potential clinical implications against pathogenic amoebae, addressing the pressing need for effective therapies.

The prevailing concept is that gestational alloimmune liver disease (GALD) is caused by maternal antibodies targeting a currently unknown antigen on the liver of the fetus. This leads to deposition of complement on the fetal hepatocytes and death of the fetal hepatocytes and extensive liver injury. In many cases, the newborn dies. In subsequent pregnancies early treatment of the woman with intravenous immunoglobulin can be instituted, and the prognosis for the fetus will be excellent. Without treatment the prognosis can be severe. Crucial improvements of diagnosis require identification of the target antigen. For this identification, this work was based on two hypotheses: 1. The GALD antigen is exclusively expressed in the fetal liver during normal fetal life in all pregnancies; 2. The GALD antigen is an alloantigen expressed in the fetal liver with the woman being homozygous for the minor allele and the father being, most frequently, homozygous for the major allele. We used three different experimental approaches to identify the liver target antigen of maternal antibodies from women who had given birth to a baby with the clinical GALD diagnosis: 1. Immunoprecipitation of antigens from either a human liver cell line or human fetal livers by immunoprecipitation with maternal antibodies followed by mass spectrometry analysis of captured antigens; 2. Construction of a cDNA expression library from human fetal liver mRNA and screening about 1.3 million recombinants in Escherichia coli using antibodies from mothers of babies diagnosed with GALD; 3. Exome/genome sequencing of DNA from 26 presumably unrelated women who had previously given birth to a child with GALD with husband controls and supplementary HLA typing. In conclusion, using the three experimental approaches we did not identify the GALD target antigen and the exome/genome sequencing results did not support the hypothesis that the GALD antigen is an alloantigen, but the results do not yield basis for excluding that the antigen is exclusively expressed during fetal life., which is the hypothesis we favor.

Hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Changes in gene expression can also be due to confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. We combined the analysis of 1,076 and 1,040 cell lines in five proteomics and three transcriptomics data sets to identify 157 genes that correlate with cell proliferation rates. These include actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. This signature of cell proliferation is a valuable resource when analyzing high-throughput data showing changes in proliferation across conditions. We show how to use this resource to help in interpretation of in vitro drug screens and tumor samples. It informs on differences of cell proliferation rates between conditions where such information is not directly available. The signature genes also highlight which hits in a screen may be due to proliferation changes; this can either contribute to biological interpretation or help focus on experiment-specific regulation events otherwise buried in the statistical analysis.