Paving the way for precision medicine v2.0 in intensive care by profiling necroinflammation in biofluids

Personalized antiseizure medication therapy in critically ill adult patients

Precision medicine has the potential to have a significant impact on both drug development and patient care. It is crucial to not only provide prompt effective antiseizure treatment for critically ill patients after seizures start but also have a proactive mindset and concentrate on epileptogenesis and the underlying cause of the seizures or seizure disorders. Critical illness presents different treatment issues compared with the ambulatory population, which makes it challenging to choose the best antiseizure medications and to administer them at the right time and at the right dose. Since there is a paucity of information available on antiseizure medication dosing in critically ill patients, therapeutic drug monitoring is a useful tool for defining each patient's personal therapeutic range and assisting clinicians in decision-making. Use of pharmacogenomic information relating to pharmacokinetics, hepatic metabolism, and seizure etiology may improve safety and efficacy by individualizing therapy. Studies evaluating the clinical implementation of pharmacogenomic information at the point-of-care and identification of biomarkers are also needed. These studies may make it possible to avoid adverse drug reactions, maximize drug efficacy, reduce drug-drug interactions, and optimize medications for each individual patient. This review will discuss the available literature and provide future insights on precision medicine use with antiseizure therapy in critically ill adult patients.

Ferroptosis and pyroptosis signatures in critical COVID-19 patients

Critical COVID-19 patients admitted to the intensive care unit (ICU) frequently suffer from severe multiple organ dysfunction with underlying widespread cell death. Ferroptosis and pyroptosis are two detrimental forms of regulated cell death that could constitute new therapeutic targets. We enrolled 120 critical COVID-19 patients in a two-center prospective cohort study to monitor systemic markers of ferroptosis, iron dyshomeostasis, pyroptosis, pneumocyte cell death and cell damage on the first three consecutive days after ICU admission. Plasma of 20 post-operative ICU patients (PO) and 39 healthy controls (HC) without organ failure served as controls. Subsets of COVID-19 patients displayed increases in individual biomarkers compared to controls. Unsupervised clustering was used to discern latent clusters of COVID-19 patients based on biomarker profiles. Pyroptosis-related interleukin-18 accompanied by high pneumocyte cell death was independently associated with higher odds at mechanical ventilation, while the subgroup with high interleuking-1 beta (but limited pneumocyte cell death) displayed reduced odds at mechanical ventilation and lower mortality hazard. Meanwhile, iron dyshomeostasis with a tendency towards higher ferroptosis marker malondialdehyde had no association with outcome, except for the small subset of patients with very high catalytic iron independently associated with reduced survival. Forty percent of patients did not have a clear signature of the cell death mechanisms studied in this cohort. Moreover, repeated moderate levels of soluble receptor of advanced glycation end products and growth differentiation factor 15 during the first three days after ICU admission are independently associated with adverse clinical outcome compared to sustained lower levels. Altogether, the data point towards distinct subgroups in this cohort of critical COVID-19 patients with different systemic signatures of pyroptosis, iron dyshomeostasis, ferroptosis or pneumocyte cell death markers that have different outcomes in ICU. The distinct groups may allow 'personalized' treatment allocation in critical COVID-19 based on systemic biomarker profiles.

Serine and one-carbon metabolisms bring new therapeutic venues in prostate cancer

Serine and one-carbon unit metabolisms are essential biochemical pathways implicated in fundamental cellular functions such as proliferation, biosynthesis of important anabolic precursors and in general for the availability of methyl groups. These two distinct but interacting pathways are now becoming crucial in cancer, the de novo cytosolic serine pathway and the mitochondrial one-carbon metabolism. Apart from their role in physiological conditions, such as epithelial proliferation, the serine metabolism alterations are associated to several highly neoplastic proliferative pathologies. Accordingly, prostate cancer shows a deep rearrangement of its metabolism, driven by the dependency from the androgenic stimulus. Several new experimental evidence describes the role of a few of the enzymes involved in the serine metabolism in prostate cancer pathogenesis. The aim of this study is to analyze gene and protein expression data publicly available from large cancer specimens dataset, in order to further dissect the potential role of the abovementioned metabolism in the complex reshaping of the anabolic environment in this kind of neoplasm. The data suggest a potential role as biomarkers as well as in cancer therapy for the genes (and enzymes) belonging to the one-carbon metabolism in the context of prostatic cancer.

Emerging immune and cell death mechanisms in stroke: Saponins as therapeutic candidates

The complexity of the ischemic cascade is based on the integrated crosstalk of every cell type in the neurovascular unit. Depending on the features of the ischemic insult, several cell death mechanisms are triggered, such as apoptosis, necroptosis, ferroptosis/oxytosis, ETosis or pyroptosis, leading to reactive astrogliosis. However, emerging evidence demonstrates a dual role for the immune system in stroke pathophysiology, where it exerts both detrimental and also beneficial functions. In this review, we discuss the relevance of several cell death modalities and the dual role of the immune system in stroke pathophysiology. We also provide an overview of some emerging immunomodulatory therapeutic strategies, amongst which saponins, which are promising candidates that exert multiple pharmacological effects.

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Several cell death mechanisms coexist in stroke pathophysiology.

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Neurons are more vulnerable to necroptosis than glial cells.

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Inhibitors of receptor-interacting protein kinases and of ferroptosis induce neuroprotection.

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Saponins exert modulatory effects on inflammation and neuronal cell death in stroke.

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. Clinical methods alone can result in the under-recognition of this heterogeneous syndrome. The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms. Most of the work in the field of ML in ARDS has been in the development of prediction models that have comparable efficacies to that of traditional models. Prediction algorithms have been useful in identifying new variables that may be important to consider in the future, supplementing the unknown information with the help of available noninvasive parameters, as well as predicting mortality. Phenotype identification using an unsupervised ML algorithm has been pivotal in classifying the heterogeneous population into more homogenous classes. Big data generated from ventilators in the form of ventilator waveform analysis and images in the form of radiomics have also been leveraged for the identification of the syndrome and can be incorporated into a clinical decision support system. Although the results are promising, lack of generalizability, "black box" nature of algorithms and concerns about "alarm fatigue" should be addressed for more mainstream adoption of these models.

Dynamical modeling of pro- and anti-inflammatory cytokines in the early stage of septic shock

A dynamical model of the pathophysiological behaviors of IL18 and IL10 cytokines with their receptors is tested against data for the case of early sepsis. The proposed approach considers the surroundings (organs and bone marrow) and the different subsystems (cells and cyctokines). The interactions between blood cells, cytokines and the surroundings are described via mass balances. Cytokines are adsorbed onto associated receptors at the cell surface. The adsorption is described by the Langmuir model and gives rise to the production of more cytokines and associated receptors inside the cell. The quantities of pro and anti-inflammatory cytokines present in the body are combined to give global information via an inflammation level function which describes the patient’s state. Data for parameter estimation comes from the Sepsis 48 H database. Comparisons between patient data and simulations are presented and are in good agreement. For the IL18/IL10 cytokine pair, 5 key parameters have been found. They are linked to pro-inflammatory IL18 cytokine and show that the early sepsis is driven by components of inflammatory character.

DL4papers: a deep learning approach for the automatic interpretation of scientific articles

Motivation

In precision medicine, next-generation sequencing and novel preclinical reports have led to an increasingly large amount of results, published in the scientific literature. However, identifying novel treatments or predicting a drug response in, for example, cancer patients, from the huge amount of papers available remains a laborious and challenging work. This task can be considered a text mining problem that requires reading a lot of academic documents for identifying a small set of papers describing specific relations between key terms. Due to the infeasibility of the manual curation of these relations, computational methods that can automatically identify them from the available literature are urgently needed.

Results

We present DL4papers, a new method based on deep learning that is capable of analyzing and interpreting papers in order to automatically extract relevant relations between specific keywords. DL4papers receives as input a query with the desired keywords, and it returns a ranked list of papers that contain meaningful associations between the keywords. The comparison against related methods showed that our proposal outperformed them in a cancer corpus. The reliability of the DL4papers output list was also measured, revealing that 100% of the first two documents retrieved for a particular search have relevant relations, in average. This shows that our model can guarantee that in the top-2 papers of the ranked list, the relation can be effectively found. Furthermore, the model is capable of highlighting, within each document, the specific fragments that have the associations of the input keywords. This can be very useful in order to pay attention only to the highlighted text, instead of reading the full paper. We believe that our proposal could be used as an accurate tool for rapidly identifying relationships between genes and their mutations, drug responses and treatments in the context of a certain disease. This new approach can certainly be a very useful and valuable resource for the advancement of the precision medicine field.

Availability and implementation

A web-demo is available at: http://sinc.unl.edu.ar/web-demo/dl4papers/. Full source code and data are available at: https://sourceforge.net/projects/sourcesinc/files/dl4papers/.

Contact

lbugnon@sinc.unl.edu.ar

Supplementary information

Supplementary data are available at Bioinformatics online.

"Point of no return" in unilateral renal ischemia reperfusion injury in mice

Background

In the past years evidence has been growing about the interconnection of chronic kidney disease and acute kidney injury. The underlying pathophysiological mechanisms remain unclear. We hypothesized, that a threshold ischemia time in unilateral ischemia/reperfusion injury sets an extent of ischemic tubule necrosis, which as “point of no return” leads to progressive injury. This progress is temporarily associated by increased markers of inflammation and results in fibrosis and atrophy of the ischemic kidney.

Methods

Acute tubule necrosis was induced by unilateral ischemia/reperfusion injury in male C57BL/6 N mice with different ischemia times (15, 25, 35, and 45 min). At multiple time points between 15 min and 5 weeks we assessed gene expression of markers for injury, inflammation, and fibrosis, histologically the injury of tubules, cell death (TUNEL), macrophages, neutrophil influx and kidney atrophy.

Results

Unilateral ischemia for 15 and 25 min induced upregulation of markers for injury after reperfusion for 24 h but no upregulation after 5 weeks. None of the markers for inflammation or fibrosis were upregulated after ischemia for 15 and 25 min at 24 h or 5 weeks on a gene expression level, except for Il-6. Ischemia for 35 and 45 min consistently induced upregulation of markers for inflammation, injury, and partially of fibrosis (Tgf-β1 and Col1a1) at 24 h and 5 weeks. The threshold ischemia time for persistent injury of 35 min induced a temporal association of markers for inflammation and injury with peaks between 6 h and 7 d along the course of 10 d. This ischemia time also induced persistent cell death (TUNEL) throughout observation for 5 weeks with a peak at 6 h and progressing kidney atrophy beginning 7 d after ischemia.

Conclusions

This study confirms the evidence of a threshold extent of ischemic injury in which markers of injury, inflammation and fibrosis do not decline to baseline but remain upregulated assessed in long term outcome (5 weeks). Excess of this threshold as “point of no return” leads to persistent cell death and progressing atrophy and is characterized by a temporal association of markers for inflammation and injury.

The Role of Adipokines as Circulating Biomarkers in Critical Illness and Sepsis

Sepsis represents a major global health burden. Early diagnosis of sepsis as well as guiding early therapeutic decisions in septic patients still represent major clinical challenges. In this context, a whole plethora of different clinical and serum-based markers have been tested regarding their potential for early detection of sepsis and their ability to stratify patients according to their probability to survive critical illness and sepsis. Adipokines represent a fast-growing class of proteins that have gained an increasing interest with respect to their potential to modulate immune responses in inflammatory and infectious diseases. We review current knowledge on the role of different adipokines in diagnostic work-up and risk stratification of sepsis as well as critical illness. We discuss recent data from animal models as well as from clinical studies and finally highlight the limitations of these analyses that currently prevent the use of adipokines as biomarkers in daily practice.