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Population scale proteomics enables adaptive digital twin modelling in sepsis

ABSTRACT: 1364 plasma proteome samples taken at time-of-admission to the emergency department from patients suspected of sepsis. Used initially with the following abstract: Sepsis is one of the leading causes of mortality in the world. Currently, the heterogeneity of sepsis makes it challenging to determine the molecular mechanisms that define the syndrome. Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. We identified panels of proteins using explainable artificial intelligence that predict clinical outcomes and applied these panels to reduce high-dimensional proteomics data to a low-dimensional interpretable latent space (ILS). Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk patients using only data available at time-of-admission. In addition to being highly effective for investigating sepsis, this approach supports the flexible incorporation of new data and can generalize to other diseases to aid in translational research and the development of precision medicine.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Johan Malmstrom Adam Linder

PROVIDER: MSV000094486 | MassIVE | Mon Apr 08 03:25:00 BST 2024

SECONDARY ACCESSION(S): PXD051271

REPOSITORIES: MassIVE

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Project description:BACKGROUND: There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.METHODS: Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with acute viral infections, including COVID-19, bacterial infections, inflammatory conditions and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.FINDINGS: 3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral with 101 non-viral cases in the discovery cohort, ddhC was the most differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral with 40 bacterial cases, generating an AUC of 0.81 (95% CI: 0.708-0.915). Transcripts of viperin and CMPK2, enzymes responsible for ddhCTP synthesis, were amongst the 5 genes most highly correlated with ddhC abundance.CONCLUSIONS: The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes, viperin and CMPK2 are implicated in ddhC production in vivo. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness and acute infection management.FUNDING: National Institute for Health Research Imperial Biomedical Research Centre; Medical Research Council.

Project description:BACKGROUND: Despite best supportive intensive care, the development of severe sepsis and septic shock after trauma is still associated with a high mortality rate in intensive care units (ICU). Incomplete knowledge of the pathophysiological and biochemical mechanisms that lead to perturbations of the host system is a major cause for this clinical entity. This lack of knowledge is mirrored in a delay in both diagnosis of sepsis and stratification of patients at risk. The current diagnostic and classification methods do not provide an early and reliable evidence for the development of sepsis and consequently lead to uncertainty in the classification and a delay in the initiation of an adequate therapy. Here, we present a prospective study performed with a well-defined patient cohort suffering from severe multiple trauma (n=85), in which we aimed to uncover the biological reasons why patients with multiple trauma can have dramatically different outcomes after suffering similar traumatic insults. METHODS: We used peripheral whole blood obtained with the PaxGene system at the time of admission to the ICU, i.e., 12 hours after trauma, for transcriptome analysis and monitored clinically the sequence of events from the admission to the ICU, to the very onset of sepsis and finally to the full development of multiple organ dysfunction syndrome (MODS). Study protocols and standard operating procedures (SOPs) regarding ethics, patient recruitment, logistics in sample acquisition and storage, microarray experiments, data analysis and data management have been developed and provided for a high feasibility and reproducibility of the investigations. FINDINGS: The transcriptome analysis from peripheral blood revealed a strong host response upon severe injury and demonstrated the suitability of whole blood in the PaxGene system as a surrogate marker in clinical gene expression studies. It was evident that patients who developed sepsis in the course of the disease (3-5 days after admission) showed an imbalance in the TH1/TH2 response towards a pronounced TH2 response, soon after trauma. The imbalanced TH1/TH2 shift along with inactivation of T cells, neutrophils, monocytes and macrophages might be critical factors for the initiation of a septic program seen in these patients 12 hours after trauma. INTERPRETATION: First, in this study, we provide protocols for logistics and infrastructure that can be applied generally to successfully integrate gene expression analysis studies in a clinical routine and demonstrate that by following these protocols, dedicated and reproducible results can be obtained. Second, we showed that peripheral whole blood is a suitable surrogate marker for the host response as it contains lots of “information” and is easily accessible. Third, we identified mechanisms involved in the perturbations of the immune system that lead to sepsis. Keywords: Prospective study 159 consecutive traumatized patients were included in the study upon admission to the intensive care unit (ICU), fulfilling all of the following inclusion criteria: severe injuries to at least two body regions or three major fractures, between 18 and 65 years of age, an estimated Injury Severity Score (ISS) of ?15 points after thorough assessment of injuries, <12 hours between the occurrence of the accident and the time of admission to the ICU, and at least greater than 3 days of survival. None of the patients underwent neuro- or cardiac surgery. We excluded patients with severe intracranial head injuries, coagulation abnormalities known at the day of admission to the ICU, acute renal failure, liver failure, malignant disease, or hemofiltration in the patient`s history. The Patients were divided into two groups depending on their posttraumatic course: Patients not complicatd by sepsis (Group: N) and Patients complicated by sepsis (Group: S). Using the CodeLink UniSet Human 10 K Bioarrays (GE Healthcare, Freiburg, Germany), we searched for early differences in the transcriptome of these two groups. Only whole blood samples drawn upon admission to the ICU were used for this investigation. Microarray results were validated by quantification of mRNA by TaqMan® technology. Each patient sample was hybridized in duplicate or triplicate on microarrays (label-extract technical replicate). A total of 72 arrays (36 group N, 36 group S) were subjected to microarray quality analysis. Of these, 2 arrays (0 group N, 2 group S) were excluded from the data set due to quality reasons and a final set of 70 arrays (36 group N and 34 group S) were subjected to microarray analysis. Since the eliminated 2 arrays belonged to patients with three replicates, they were treated as two replicates in the further analysis. The arrays were designated N_xx_yy_z or S_xx_yy_z, with x for the patient-ID (1,2,3,4..) and z for the technical replicate number (1,2 or 3).

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Population scale proteomics enables adaptive digital twin modelling in sepsis

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