Multiple organ dysfunction syndrome (MODS) occurs in the setting of a variety of pathologies including infection and trauma. Some patients decompensate and require Veno-Arterial extra corporeal membrane oxygenation (ECMO) as a palliating manoeuvre for recovery of cardiopulmonary function. The molecular mechanisms driving progression from MODS to cardiopulmonary collapse remain incompletely understood, and no biomarkers have been defined to identify those MODS patients at highest risk for progression to requiring ECMO support. Whole blood RNA-seq profiling was performed for 23 MODS patients at three time points during their ICU stay (at diagnosis of MODS, 72 hours after, and 8 days later), as well as four healthy controls undergoing routine sedation. Of the 23 MODS patients, six required ECMO support (ECMO patients). The predictive power of conventional demographic and clinical features was quantified for differentiating the MODS and ECMO patients. We then compared the performance of markers derived from transcriptomic profiling including [1] transcriptomically imputed leukocyte subtype distribution, [2] relevant published gene signatures and [3] a novel differential gene expression signature computed from our data set. The predictive power of our novel gene expression signature was then validated using independently published datasets. None of the five demographic characteristics and 14 clinical features, including The Paediatric Logistic Organ Dysfunction (PELOD) score, could predict deterioration of MODS to ECMO at baseline. From previously published sepsis signatures, only the signatures positively associated with patient's mortality could differentiate ECMO patients from MODS patients, when applied to our transcriptomic dataset (P-value ranges from 0.01 to 0.04, Student's test). Deconvolution of bulk RNA-Seq samples suggested that lower neutrophil counts were associated with increased risk of progression from MODS to ECMO (P-value = 0.03, logistic regression, OR=2.82 [95% CI 0.63 - 12.45]). A total of 30 genes were differentially expressed between ECMO and MODS patients at baseline (log2 fold change ≥ 1 or ≤ -1 with false discovery rate ≤ 0.01). These genes are involved in protein maintenance and epigenetic-related processes. Further univariate analysis of these 30 genes suggested a signature of seven DE genes associated with ECMO (OR > 3.0, P-value ≤ 0.05, logistic regression). Notably, this contains a set of histone marker genes, including H1F0, HIST2H3C, HIST1H2AI, HIST1H4, HIST1H2BL and HIST1H1B, that were highly expressed in ECMO. A risk score derived from expression of these genes differentiated ECMO and MODS patients in our dataset (AUC = 0.91, 95% CI 0.79-1.00, P-value = 7e-04, logistic regression) as well as validation dataset (AUC= 0.73, 95% CI 0.53-0.93, P-value = 2e-02, logistic regression). This study demonstrates that transcriptomic features can serve as indicators of severity that could be superior to traditional methods of ascertaining acuity in MODS patients. Analysis of expression of signatures identified in this study could help clinicians in the diagnosis and prognostication of MODS patients after arrival to the Hospital.

Obesity is associated with increased breast cancer risk and poorer cancer outcomes; however, the precise etiology of these observations has not been fully identified. Our previous research suggests that adipose tissue-derived fibroblast growth factor-2 (FGF2) promotes the malignant transformation of epithelial cells through the activation of fibroblast growth factor receptor-1 (FGFR1). FGF2 is increased in the context of obesity, and increased sera levels have been associated with endocrine-resistant breast cancer. Leptin is a marker of obesity and promotes breast carcinogenesis through several mechanisms. In this study, we leverage public gene expression datasets to evaluate the associations between FGFR1, leptin, and the leptin receptor (LepR) in breast cancer. We show a positive association between FGFR1 and leptin protein copy number in primary breast tumors. These observations coincided with a positive association between Janus kinase 2 (Jak2) mRNA with both leptin receptor (LepR) mRNA and FGFR1 mRNA. Moreover, two separate Jak2 inhibitors attenuated both leptin+FGF2-stimulated and mouse adipose tissue-stimulated MCF-10A transformation. These results demonstrate how elevated sera FGF2 and leptin in obese patients may promote cancer progression in tumors that express elevated FGFR1 and LepR through Jak2 signaling. Therefore, Jak2 is a potential therapeutic target for FGFR1 amplified breast cancer, especially in the context of obesity.

Background and aims: Cell lines are widely used in biomedical research to investigate various biological processes, including gene expression, cancer progression, and drug responses. However, cross-contamination with bacteria, mycoplasma, and viruses remains a persistent challenge. While the detection of bacterial and mycoplasma contamination is relatively straightforward, identifying viral contamination is more difficult. To address this issue, we developed ViralCellDetector, a tool designed to detect viral contamination by mapping RNA-seq data to a comprehensive viral genome library.Methods: ViralCellDetector processes RNA-seq data from any host species by first aligning reads to the host reference genome, followed by mapping the unmapped reads to the NCBI viral genome database. Viral presence is determined using stringent criteria based on the number of mapped reads and viral genome coverage. To further enable the detection of viral contamination from unknown sources, we identified host genes that are differentially expressed during viral infection and used these markers to train a machine learning model for classification.Results: Using ViralCellDetector, we found that approximately 10% (110 samples) of RNA-seq datasets involving MCF7 cells were likely contaminated with viruses. The tool demonstrated high sensitivity in detecting viral sequences. Furthermore, the machine learning model effectively distinguished infected from non-infected samples based on human gene expression profiles, achieving an AUC of 0.91 and an accuracy of 0.93.Conclusion: Our mapping-based approach enables robust detection of viral contamination in RNA-seq data from any host organism, while the marker-based approach accurately identifies viral infections specifically in human cell lines. This capability can help researchers detect and avoid the use of contaminated cell lines, thereby improving the reliability of experimental outcomes.

Background: Multiple organ dysfunction syndrome (MODS) occurs in the setting of a variety of pathologies including infection and trauma. Some of these patients will further decompensate and require extra corporeal membrane oxygenation (ECMO) as a palliating maneuver to allow time for recovery of cardiopulmonary function. The molecular mechanisms driving progression from MODS to cardiopulmonary collapse remain incompletely understood, and no biomarkers have been defined to identify those MODS patients at highest risk for progression to requiring ECMO support. We hypothesize that molecular features derived from whole blood transcriptomic profiling either alone or in combination with traditional clinical and laboratory markers can prospectively identify these high risk MODS patients in the pediatric intensive care unit (PICU). Design/Methods: Whole blood RNA-seq profiling was performed for 23 MODS patients at three time points during their ICU stay (at diagnosis of MODS, 72 hours after, and 8 days later), as well as four healthy controls undergoing routine sedation. Of the 23 MODS patients, six required ECMO support (ECMO patients). The predictive power of conventional demographic and clinical features was quantified for differentiating the MODS and ECMO patients. We then compared the performance of markers derived from transcriptomic profiling including (1) transcriptomically imputed leukocyte subtype distribution, (2) relevant published gene signatures and (3) a novel differential gene expression signature computed from our data set. The predictive power of our novel gene expression signature was then validated using independently published datasets. Results: None of the five demographic characteristics and 14 clinical features, including The Pediatric Logistic Organ Dysfunction (PELOD) score, could predict deterioration of MODS to ECMO at baseline. From previously published sepsis signatures, only the signatures positively associated with patients mortality could differentiate ECMO patients from MODS patients, when applied to our transcriptomic dataset ( P -value ranges from 0.01 to 0.04). Deconvolution of bulk RNA-Seq samples suggested that lower neutrophil counts were associated with increased risk of progression from MODS to ECMO ( P -value = 0.03, OR=2.82 [95% CI 0.63– 12.45]). A total of 28 genes were differentially expressed between ECMO and MODS patients at baseline (log ₂ fold change ≥ 1 or ≤ -1 with false discovery rate ≤ 0.2). These genes are involved in protein maintenance and epigenetic-related processes. Further univariate analysis of these 28 genes suggested a signature of six DE genes associated with ECMO (OR > 3.0, P -value ≤ 0.05). Notably, this contains a set of histone marker genes, including H1F0, HIST2H3C, HIST1H2AI, HIST1H4 , and HIST1H1B , that were highly expressed in ECMO. A risk score derived from expression of these genes differentiated ECMO and MODS patients in our dataset (AUC = 0.91, 95% CI 0.79-0.1.00, P -value = 7e-04) as well as validation dataset (AUC= 0.73,95% CI 0.53-0.93, P -value = 2e-02). Conclusions: This study identified lower neutrophils and upregulation of specific histone related genes as a putative signature for deterioration of MODS to ECMO. This study demonstrates that transcriptomic features may be superior to traditional clinical methods of ascertaining severity in patients with MODS. Author summary: Why was this study done?: Multiple organ dysfunction syndrome (MODS) is a major cause of mortality and morbidity in critically ill pediatric patients who survive the initial physical insult. A variety of triggers including trauma and infections can lead to MODS in pediatric patients. The clinical condition of some MODS patients improve while others deteriorate, needing resource-intensive life support such as extracorporeal membrane oxygenation (ECMO). Mortality is uncommon in PICUs and the need for advanced life support devices, such as ECMO can serve as proxy for mortality. The decision to initiate ECMO in pediatric patients is often subjective made by a committee of physicians that include surgeons, intensivists and a variety of other subspecialists often in the absence of objective data. Despite decades of research, no diagnostic criteria or biomarker has been identified that comprehensively assesses severity in MODS patients who may need subsequent ECMO support in the hyperacute phase of injury. We systematically assessed clinical and transcriptional features as biomarkers for the prediction of the ECMO patients. What did the researcher do and find?: We investigated various clinical and transcriptional features in 27 patients with MODS at multiple time points (4 CT, 17 MODS, 6 ECMO) at baseline (0h). We observed that immune response pathways (monocytes, cytokines, NF-kB, and inflammation) were activated in the initiation of MODS, whereas neutrophil level was decreased during deterioration of MODS to ECMO. A total of 51 DE genes were identified in MODS compared to CT and 28 DE in ECMO compared to MODS at baseline (0h). We identified the enrichment of immune-related and glycogenolysis processes in MODS compared to CT and enrichment of protein maintenance, DNA repair and epigenetic-related processes in ECMO compared to MODS at baseline (0h). Logistic regression was used to identify a signature of 6 genes strongly associated with ECMO decision and this signature could help to diagnose MODS patients requiring ECMO. The transcriptomic signature-based risk scores were further evaluated in an independent cohort. What do these findings mean?: The compromised level of neutrophils and activation of gene markers including a few histone genes could be used as putative signature for diagnosing the deterioration of MODS to ECMO. A risk score derived from signature genes could be used to predict the need for ECMO. This score is superior to traditional clinical criteria and severity scores used in the Pediatric ICU. The transcriptional signature derived in this study could potentially be used to identify patients in the hyperacute phase of injury that may need higher levels of support like ECMO enabling the selection of an appropriate treatment plan.

Cell lines are commonly used in research to study biology, including gene expression regulation, cancer progression, and drug responses. However, cross-contaminations with bacteria, mycoplasma, and viruses are common issues in cell line experiments. Detection of bacteria and mycoplasma infections in cell lines is relatively easy but identifying viral infections in cell lines is difficult. Currently, there are no established methods or tools available for detecting viral infections in cell lines. To address this challenge, we developed a tool called ViralCellDetector that detects viruses through mapping RNA-seq data to a library of virus genome. Using this tool, we observed that around 10% of experiments with the MCF7 cell line were likely infected with viruses. Furthermore, to facilitate the detection of samples with unknown sources of viral infection, we identified the differentially expressed genes involved in viral infection from two different cell lines and used these genes in a machine learning approach to classify infected samples based on the host response gene expression biomarkers. Our model reclassifies the infected and non-infected samples with an AUC of 0.91 and an accuracy of 0.93. Overall, our mapping- and marker-based approaches can detect viral infections in any cell line simply based on readily accessible RNA-seq data, allowing researchers to avoid the use of unintentionally infected cell lines in their studies.