Analysis of Chest X-ray for COVID-19 Diagnosis as a Use Case for an HPC-Enabled Data Analysis and Machine Learning Platform for Medical Diagnosis Support.

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications.

A microfluidic Braille valve platform for on-demand production, combinatorial screening and sorting of chemically distinct droplets.

Droplet microfluidics is a powerful tool for a variety of biological applications including single-cell genetics, antibody discovery and directed evolution. All these applications make use of genetic libraries, illustrating the difficulty of generating chemically distinct droplets for screening applications. This protocol describes our Braille Display valving platform for on-demand generation of droplets with different chemical contents (16 different reagents and combinations thereof), as well as sorting droplets with different chemical properties, on the basis of fluorescence signals.

Phosphoproteomics of primary AML patient samples reveals rationale for AKT combination therapy and p53 context to overcome selinexor resistance.

Acute myeloid leukemia (AML) is a heterogeneous disease with variable patient responses to therapy. Selinexor, an inhibitor of nuclear export, has shown promising clinical activity for AML. To identify the molecular context for monotherapy sensitivity as well as rational drug combinations, we profile selinexor signaling responses using phosphoproteomics in primary AML patient samples and cell lines.

Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets.

Anti-cancer therapies often exhibit only short-term effects. Tumors typically develop drug resistance causing relapses that might be tackled with drug combinations. Identification of the right combination is challenging and would benefit from high-content, high-throughput combinatorial screens directly on patient biopsies.

Imbalanced gut microbiota fuels hepatocellular carcinoma development by shaping the hepatic inflammatory microenvironment.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, and therapeutic options for advanced HCC are limited. Here, we observe that intestinal dysbiosis affects antitumor immune surveillance and drives liver disease progression towards cancer. Dysbiotic microbiota, as seen in Nlrp6 mice, induces a Toll-like receptor 4 dependent expansion of hepatic monocytic myeloid-derived suppressor cells (mMDSC) and suppression of T-cell abundance.

Cancer-associated fibroblasts require proline synthesis by PYCR1 for the deposition of pro-tumorigenic extracellular matrix.

Elevated production of collagen-rich extracellular matrix is a hallmark of cancer-associated fibroblasts (CAFs) and a central driver of cancer aggressiveness. Here we find that proline, a highly abundant amino acid in collagen proteins, is newly synthesized from glutamine in CAFs to make tumour collagen in breast cancer xenografts. PYCR1 is a key enzyme for proline synthesis and highly expressed in the stroma of breast cancer patients and in CAFs.

Explainable multiview framework for dissecting spatial relationships from highly multiplexed data.

The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects.

Patient-specific Boolean models of signalling networks guide personalised treatments.

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients.

MAGED2 controls vasopressin-induced aquaporin-2 expression in collecting duct cells.

Mutations in the Melanoma-Associated Antigen D2 (MAGED2) cause antenatal Bartter syndrome type 5 (BARTS5). This rare disease is characterized by perinatal loss of urinary concentration capability and large urine volumes. The underlying molecular mechanisms of this disease are largely unclear.

Systems approach reveals distinct and shared signaling networks of the four PGE receptors in T cells.

Prostaglandin E (PGE) promotes an immunosuppressive microenvironment in cancer, partly by signaling through four receptors (EP, EP, EP, and EP) on T cells. Here, we comprehensively characterized PGE signaling networks in helper, cytotoxic, and regulatory T cells using a phosphoproteomics and phosphoflow cytometry approach. We identified ~1500 PGE-regulated phosphosites and several important EP signaling nodes, including PKC, CK2, PKA, PI3K, and Src.

Transcriptomic Cross-Species Analysis of Chronic Liver Disease Reveals Consistent Regulation Between Humans and Mice.

Mouse models are frequently used to study chronic liver diseases (CLDs). To assess their translational relevance, we quantified the similarity of commonly used mouse models to human CLDs based on transcriptome data. Gene-expression data from 372 patients were compared with data from acute and chronic mouse models consisting of 227 mice, and additionally to nine published gene sets of chronic mouse models.

Contextualization of causal regulatory networks from toxicogenomics data applied to drug-induced liver injury.

In recent years, network-based methods have become an attractive analytical approach for toxicogenomics studies. They can capture not only the global changes of regulatory gene networks but also the relationships between their components. Among them, a causal reasoning approach depicts the mechanisms of regulation that connect upstream regulators in signaling networks to their downstream gene targets.

A comprehensive database for integrated analysis of omics data in autoimmune diseases.

Background: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.

New insights into the mechanisms underlying 5-fluorouracil-induced intestinal toxicity based on transcriptomic and metabolomic responses in human intestinal organoids.

5-Fluorouracil (5-FU) is a widely used chemotherapeutical that induces acute toxicity in the small and large intestine of patients. Symptoms can be severe and lead to the interruption of cancer treatments. However, there is limited understanding of the molecular mechanisms underlying 5-FU-induced intestinal toxicity.

Integration of temporal single cell cellular stress response activity with logic-ODE modeling reveals activation of ATF4-CHOP axis as a critical predictor of drug-induced liver injury.

Drug-induced liver injury (DILI) is the most prevalent adversity encountered in drug development and clinical settings leading to urgent needs to understand the underlying mechanisms. In this study, we have systematically investigated the dynamics of the activation of cellular stress response pathways and cell death outcomes upon exposure of a panel of liver toxicants using live cell imaging of fluorescent reporter cell lines. We established a comprehensive temporal dynamic response profile of a large set of BAC-GFP HepG2 cell lines representing the following components of stress signaling: i) unfolded protein response (UPR) [ATF4, XBP1, BIP and CHOP]; ii) oxidative stress [NRF2, SRXN1, HMOX1]; iii) DNA damage [P53, P21, BTG2, MDM2]; and iv) NF-κB pathway [A20, ICAM1].

Deciphering the signaling network of breast cancer improves drug sensitivity prediction.

One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors.

Deficiency of myeloid PHD proteins aggravates atherogenesis via macrophage apoptosis and paracrine fibrotic signalling.

Aims: Atherosclerotic plaque hypoxia is detrimental for macrophage function. Prolyl hydroxylases (PHDs) initiate cellular hypoxic responses, possibly influencing macrophage function in plaque hypoxia. Thus, we aimed to elucidate the role of myeloid PHDs in atherosclerosis.

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney's glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease.

Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling.

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems.

Consensus Transcriptional Landscape of Human End-Stage Heart Failure.

Background Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end-stage HF.