Spectral connectivity: a Python package for computing multitaper spectral estimates and frequency-domain brain connectivity measures on the CPU and GPU

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[Pemetrexed + Sorafenib] lethality is increased by inhibition of ERBB1/2/3-PI3K-NFκB compensatory survival signaling

In the completed phase I trial NCT01450384 combining the anti-folate pemetrexed and the multi-kinase inhibitor sorafenib it was observed that 20 of 33 patients had prolonged stable disease or tumor regression, with one complete response and multiple partial responses. The pre-clinical studies in this manuscript were designed to determine whether [pemetrexed + sorafenib] –induced cell killing could be rationally enhanced by additional signaling modulators. Multiplex assays performed on tumor material that survived and re-grew after [pemetrexed + sorafenib] exposure showed increased phosphorylation of ERBB1 and of NFκB and IκB; with reduced IκB and elevated G-CSF and KC protein levels. Inhibition of JAK1/2 downstream of the G-CSF/KC receptors did not enhance [pemetrexed + sorafenib] lethality whereas inhibition of ERBB1/2/4 using kinase inhibitory agents or siRNA knock down of ERBB1/2/3 strongly promoted killing. Inhibition of ERBB1/2/4 blocked [pemetrexed + sorafenib] stimulated NFκB activation and SOD2 expression; and expression of IκB S32A S36A significantly enhanced [pemetrexed + sorafenib] lethality. Sorafenib inhibited HSP90 and HSP70 chaperone ATPase activities and reduced the interactions of chaperones with clients including c-MYC, CDC37 and MCL-1. In vivo, a 5 day transient exposure of established mammary tumors to lapatinib or vandetanib significantly enhanced the anti-tumor effect of [pemetrexed + sorafenib], without any apparent normal tissue toxicities. Identical data to that in breast cancer were obtained in NSCLC tumors using the ERBB1/2/4 inhibitor afatinib. Our data argue that the combination of pemetrexed, sorafenib and an ERBB1/2/4 inhibitor should be explored in a new phase I trial in solid tumor patients

Stable Iterative Variable Selection

Motivation: The emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature set also facilitates building robust predictive models with improved interpretability and convergence of the applied method due to the smaller feature space. Results: Here, we present a robust feature selection method named Stable Iterative Variable Selection (SIVS) and assess its performance over both omics and clinical data types. As a performance assessment metric, we compared the number and goodness of the selected feature using SIVS to those selected by Least Absolute Shrinkage and Selection Operator regression. The results suggested that the feature space selected by SIVS was, on average, 41% smaller, without having a negative effect on the model performance. A similar result was observed for comparison with Boruta and caret RFE. Availability and implementation: The method is implemented as an R package under GNU General Public License v3.0 and is accessible via Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/package¼sivs. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.</p

Easy-to-use tool for evaluating the elevated acute kidney injury risk against reduced cardiovascular disease risk during intensive blood pressure control

Objective: The Systolic Blood Pressure Intervention Trial (SPRINT) reported that lowering SBP to below 120 mmHg (intensive treatment) reduced cardiovascular morbidity and mortality among adults with hypertension but increased the incidence of adverse events, particularly acute kidney injury (AKI). The goal of this study was to develop an accurate risk estimation tool for comparing the risk of cardiovascular events and adverse kidney-related outcomes between standard and intensive antihypertensive treatment strategies.Methods: By applying Lasso regression on the baseline characteristics and health outcomes of 8760 participants with complete baseline information in the SPRINT trial, we developed predictive models for primary cardiovascular disease (CVD) outcome and incidence of AKI. Both models were validated against an independent test set of the SPRINT trial (one third of data not used for model building) and externally against the cardiovascular and renal outcomes available in Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial, consisting of 4733 participants with type 2 diabetes mellitus.Results: Lasso regression identified a subset of variables that accurately predicted the primary CVD outcome and the incidence of AKI (areas under receiver-operating characteristic curves 0.70 and 0.77, respectively). Based on the validated risk models, an easy-to-use risk assessment tool was developed and made available as an easy-to-use online tool.Conclusion: By predicting the risks of CVD and AKI at baseline, the developed tool can be used to weigh the benefits of intensive versus standard blood pressure control and to identify those who are likely to benefit most from intensive treatment.</p

A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer

Metastatic castration resistant prostate cancer (mCRPC) is one of the most common cancers with a poor prognosis. To improve prognostic models of mCRPC, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Consortium organized a crowdsourced competition known as the Prostate Cancer DREAM Challenge. In the competition, data from four phase III clinical trials were utilized. A total of 1600 patients’ clinical information across three of the trials was used to generate prognostic models, whereas one of the datasets (313 patients) was held out for blinded validation. As a performance baseline, a model presented in a recent study (so called Halabi model) was used to assess improvements of the new models. This paper presents the model developed by the team TYTDreamChallenge to predict survival risk scores for mCRPC patients at 12, 18, 24 and 30-months after trial enrollment based on clinical features of each patient, as well as an improvement of the model developed after the challenge. The TYTDreamChallenge model performed similarly as the gold-standard Halabi model, whereas the post-challenge model showed markedly improved performance. Accordingly, a main observation in this challenge was that the definition of the clinical features used plays a major role and replacing our original larger set of features with a small subset for training increased the performance in terms of integrated area under the ROC curve from 0.748 to 0.779.</p

Differential ATAC-seq and ChIP-seq peak detection using ROTS

Changes in cellular chromatin states fine-tune transcriptional output and ultimately lead to phenotypic changes. Here we propose a novel application of our reproducibility-optimized test statistics (ROTS) to detect differential chromatin states (ATAC-seq) or differential chromatin modification states (ChIP-seq) between conditions. We compare the performance of ROTS to existing and widely used methods for ATAC-seq and ChIP-seq data using both synthetic and real datasets. Our results show that ROTS outperformed other commonly used methods when analyzing ATAC-seq data. ROTS also displayed the most accurate detection of small differences when modeling with synthetic data. We observed that two-step methods that require the use of a separate peak caller often more accurately called enrichment borders, whereas one-step methods without a separate peak calling step were more versatile in calling sub-peaks. The top ranked differential regions detected by the methods had marked correlation with transcriptional differences of the closest genes. Overall, our study provides evidence that ROTS is a useful addition to the available differential peak detection methods to study chromatin and performs especially well when applied to study differential chromatin states in ATAC-seq data. </p

Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota—Implementation and Computational Analysis

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof-of-concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratory assembled microbial mixtures as well as human fecal samples. </p

Forum on immune digital twins: a meeting report

Medical digital twins are computational models of human biology relevant to a given medical condition, which can be tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. If medical digital twins are to faithfully capture the characteristics of a patient's immune system, we need to answer many questions, such as: What do we need to know about the immune system to build mathematical models that reflect features of an individual? What data do we need to collect across the different scales of immune system action? What are the right modeling paradigms to properly capture immune system complexity? In February 2023, an international group of experts convened in Lake Nona, FL for two days to discuss these and other questions related to digital twins of the immune system. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure

Functional diversity of chemokines and chemokine receptors in response to viral infection of the central nervous system.

Encounters with neurotropic viruses result in varied outcomes ranging from encephalitis, paralytic poliomyelitis or other serious consequences to relatively benign infection. One of the principal factors that control the outcome of infection is the localized tissue response and subsequent immune response directed against the invading toxic agent. It is the role of the immune system to contain and control the spread of virus infection in the central nervous system (CNS), and paradoxically, this response may also be pathologic. Chemokines are potent proinflammatory molecules whose expression within virally infected tissues is often associated with protection and/or pathology which correlates with migration and accumulation of immune cells. Indeed, studies with a neurotropic murine coronavirus, mouse hepatitis virus (MHV), have provided important insight into the functional roles of chemokines and chemokine receptors in participating in various aspects of host defense as well as disease development within the CNS. This chapter will highlight recent discoveries that have provided insight into the diverse biologic roles of chemokines and their receptors in coordinating immune responses following viral infection of the CNS

The role of CC chemokine receptor 5 (CCR5) and RANTES/CCL5 during chronic fungal asthma in mice1

In the present study, we explored the role of CC chemokine receptor 5 (CCR5) in a murine model of chronic fungal asthma induced by an intrapulmonary challenge with Aspergillus fumigatus conidia (or spores). Airway hyperresponsiveness was significantly lower in A. fumigatus‐sensitized mice lacking CCR5 (CCR5‐/‐) compared with similarly sensitized wild‐type (CCR5+/+) control mice at days 2, 21, 30, and 40 after the conidia challenge. CCR5‐/‐ mice exhibited significantly less peribronchial T‐cell and eosinophil accumulation and airway‐remodeling features, such as goblet cell hyperplasia and peribronchial fibrosis, compared with CCR5+/+ mice at these times after conidia. However, both groups of mice exhibited similar allergic airway disease at day 12 after the conidia challenge. In CCR5‐/‐ mice at day 12, the allergic airway disease was associated with airway hyperresponsiveness, peribronchial allergic inflammation, and goblet cell hyperplasia. Immunoneutralization of RANTES/CCL5 in sensitized CCR5+/+ and CCR5‐/‐ mice for 12 days after the conidia challenge significantly reduced the peribronchial inflammation and airway hyperresponsiveness in comparison with control wild‐type and knockout mice at this time. These data demonstrate that functional CCR5 and RANTES/CCL5 are required for the persistence of chronic fungal asthma in mice.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154329/1/fsb2fj010528fje.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154329/2/fsb2fj010528fje-sup-0001.pd