Upregulation of cholesterol synthesis by lysosomal defects requires a functional mitochondrial respiratory chain.

Mitochondria and lysosomes are two organelles that carry out both signaling and metabolic roles in cells. Recent evidence has shown that mitochondria and lysosomes are dependent on one another, as primary defects in one cause secondary defects in the other. Although there are functional impairments in both cases, the signaling consequences of primary mitochondrial dysfunction and lysosomal defects are dissimilar. Here, we used RNA sequencing to obtain transcriptomes from cells with primary mitochondrial or lysosomal defects to identify the global cellular consequences associated with mitochondrial or lysosomal dysfunction. We used these data to determine the pathways affected by defects in both organelles, which revealed a prominent role for the cholesterol synthesis pathway. We observed a transcriptional upregulation of this pathway in cellular and murine models of lysosomal defects, while it is transcriptionally downregulated in cellular and murine models of mitochondrial defects. We identified a role for the posttranscriptional regulation of transcription factor SREBF1, a master regulator of cholesterol and lipid biosynthesis, in models of mitochondrial respiratory chain deficiency. Furthermore, we found that retention of Ca2+ in lysosomes of cells with mitochondrial respiratory chain defects contributes to the differential regulation of the cholesterol synthesis pathway in the mitochondrial and lysosomal defects tested. Finally, we verified in vivo, using a model of mitochondria-associated disease in Caenorhabditis elegans that normalization of lysosomal Ca2+ levels results in partial rescue of the developmental delay induced by the respiratory chain deficiency.

Up-regulation of cholesterol synthesis by lysosomal defects requires a functional mitochondrial respiratory chain.

Mitochondria and lysosomes are two organelles that carry out both signaling and metabolic roles in the cells. Recent evidence has shown that mitochondria and lysosomes are dependent on one another, as primary defects in one cause secondary defects in the other. Nevertheless, the signaling consequences of primary mitochondrial malfunction and of primary lysosomal defects are not similar, despite in both cases there are impairments of mitochondria and of lysosomes. Here, we used RNA sequencing to obtain transcriptomes from cells with primary mitochondrial or lysosomal defects, to identify what are the global cellular consequences that are associated with malfunction of mitochondria or lysosomes. We used these data to determine what are the pathways that are affected by defects in both organelles, which revealed a prominent role for the cholesterol synthesis pathway. This pathway is transcriptionally up-regulated in cellular and mouse models of lysosomal defects and is transcriptionally down-regulated in cellular and mouse models of mitochondrial defects. We identified a role for post-transcriptional regulation of the transcription factor SREBF1, a master regulator of cholesterol and lipid biosynthesis, in models of mitochondrial respiratory chain deficiency. Furthermore, the retention of Ca 2+ in the lysosomes of cells with mitochondrial respiratory chain defects contributes to the differential regulation of the cholesterol synthesis pathway in the mitochondrial and lysosomal defects tested. Finally, we verified in vivo , using models of mitochondria-associated diseases in C. elegans , that normalization of lysosomal Ca 2+ levels results in partial rescue of the developmental arrest induced by the respiratory chain deficiency.

Predictive Models for Long Term Survival of AML Patients Treated with Venetoclax and Azacitidine or 7+3 Based on Post Treatment Events and Responses: Retrospective Cohort Study.

BACKGROUND: The treatment of acute myeloid leukemia (AML) in older or unfit patients typically involves a regimen of venetoclax plus azacitidine (ven/aza). Toxicity and treatment responses are highly variable following treatment initiation and clinical decision-making continually evolves in response to these as treatment progresses. To improve clinical decision support (CDS) following treatment initiation, predictive models based on evolving and dynamic toxicities, disease responses, and other features should be developed. OBJECTIVE: This study aims to generate machine learning (ML)-based predictive models that incorporate individual predictors of overall survival (OS) for patients with AML, based on clinical events occurring after the initiation of ven/aza or 7+3 regimen. METHODS: Data from 221 patients with AML, who received either the ven/aza (n=101 patients) or 7+3 regimen (n=120 patients) as their initial induction therapy, were retrospectively analyzed. We performed stratified univariate and multivariate analyses to quantify the association between toxicities, hospital events, and short-term disease responses and OS for the 7+3 and ven/aza subgroups separately. We compared the estimates of confounders to assess potential effect modifications by treatment. 17 ML-based predictive models were developed. The optimal predictive models were selected based on their predictability and discriminability using cross-validation. Uncertainty in the estimation was assessed through bootstrapping. RESULTS: The cumulative incidence of posttreatment toxicities varies between the ven/aza and 7+3 regimen. A variety of laboratory features and clinical events during the first 30 days were differentially associated with OS for the two treatments. An initial transfer to intensive care unit (ICU) worsened OS for 7+3 patients (aHR 1.18, 95% CI 1.10-1.28), while ICU readmission adversely affected OS for those on ven/aza (aHR 1.24, 95% CI 1.12-1.37). At the initial follow-up, achieving a morphologic leukemia free state (MLFS) did not affect OS for ven/aza (aHR 0.99, 95% CI 0.94-1.05), but worsened OS following 7+3 (aHR 1.16, 95% CI 1.01-1.31) compared to that of complete remission (CR). Having blasts over 5% at the initial follow-up negatively impacted OS for both 7+3 (P<.001) and ven/aza (P<.001) treated patients. A best response of CR and CR with incomplete recovery (CRi) was superior to MLFS and refractory disease after ven/aza (P<.001), whereas for 7+3, CR was superior to CRi, MLFS, and refractory disease (P<.001), indicating unequal outcomes. Treatment-specific predictive models, trained on 120 7+3 and 101 ven/aza patients using over 114 features, achieved survival AUCs over 0.70. CONCLUSIONS: Our findings indicate that toxicities, clinical events, and responses evolve differently in patients receiving ven/aza compared with that of 7+3 regimen. ML-based predictive models were shown to be a feasible strategy for CDS in both forms of AML treatment. If validated with larger and more diverse data sets, these findings could offer valuable insights for developing AML-CDS tools that leverage posttreatment clinical data.

Machine Learning-Based Exploratory Clinical Decision Support for Newly Diagnosed Patients With Acute Myeloid Leukemia Treated With 7 + 3 Type Chemotherapy or Venetoclax/Azacitidine.

PURPOSE: There are currently limited objective criteria to help assist physicians in determining whether an individual patient with acute myeloid leukemia (AML) is likely to do better with induction with either standard 7 + 3 chemotherapy or targeted therapy with venetoclax plus azacitidine. The study goal was to address this need by developing exploratory clinical decision support methods. PATIENTS AND METHODS: Univariable and multivariable analysis as well as comparison of a range of machine learning (ML) predictors were performed using cohorts of 120 newly diagnosed 7 + 3-treated AML patients compared with 101 venetoclax plus azacitidine-treated patients. RESULTS: A variety of features in the two patient cohorts were identified that may potentially correlate with short- and long-term outcomes, toxicities, and other considerations. A subset of these diagnostic features was then used to develop ML-based predictors with relatively high areas under the curve of short- and long-term outcomes, hospital stays, transfusion requirements, and toxicities for individual patients treated with either venetoclax/azacitidine or 7 + 3. CONCLUSION: Potential ML-based approaches to clinical decision support to help guide individual patients with newly diagnosed AML to either 7 + 3 or venetoclax plus azacitidine induction therapy were identified. Larger cohorts with separate test and validation studies are necessary to confirm these initial findings.

NLP-PIER: A Scalable Natural Language Processing, Indexing, and Searching Architecture for Clinical Notes.

Many design considerations must be addressed in order to provide researchers with full text and semantic search of unstructured healthcare data such as clinical notes and reports. Institutions looking at providing this functionality must also address the big data aspects of their unstructured corpora. Because these systems are complex and demand a non-trivial investment, there is an incentive to make the system capable of servicing future needs as well, further complicating the design. We present architectural best practices as lessons learned in the design and implementation NLP-PIER (Patient Information Extraction for Research), a scalable, extensible, and secure system for processing, indexing, and searching clinical notes at the University of Minnesota.

Application of HL7/LOINC Document Ontology to a University-Affiliated Integrated Health System Research Clinical Data Repository.

Fairview Health Services is an affiliated integrated health system partnering with the University of Minnesota to establish a secure research-oriented clinical data repository that includes large numbers of clinical documents. Standardization of clinical document names and associated attributes is essential for their exchange and secondary use. The HL7/LOINC Document Ontology (DO) was developed to provide a standard representation of clinical document attributes with a multi-axis structure. In this study, we evaluated the adequacy of DO to represent documents in the clinical data repository from legacy and current EHR systems across community and academic practice sites. The results indicate that a large portion of repository data items can be mapped to the current DO ontology but that document attributes do not always link consistently with DO axes and additional values for certain axes, particularly "Setting" and "Role" are needed for better coverage. To achieve a more comprehensive representation of clinical documents, more effort on algorithms, DO value sets, and data governance over clinical document attributes is needed.