Alexander G. Ginsberg, Josefin Ahnström, James T. B. Crawley
et al.
Protein S (PS) is a notable anticoagulant implicated in both bleeding and thrombotic disorders, making it a promising drug target. Importantly, PS enhances the anticoagulant function of TFPI$α$, likely circulating in the bloodstream together with TFPI$α$ and a truncated form of factor V (fVshort) in the trimolecular complex, TFPI$α$-fVshort-PS, which we call protein S complex (PSC). PSC has been proposed to strongly inhibit thrombin production by enhancing the ability of TFPI$α$ to inhibit clotting factor Xa up to 100-fold and by localizing to platelet membranes, limiting fXa activity shortly after coagulation starts. Yet, exactly how PS functions with TFPI$α$ as an anticoagulant remains poorly understood. To investigate, we extend an experimentally validated mathematical model of blood coagulation to include PSC and free PS (not part of PSC) in the plasma, as well as free PS and TFPI$α$ in platelets. We find that shortly after coagulation initiation, PSC strongly inhibits thrombin production. We find that the (unknown) magnitude of the enhanced affinity of PSC binding to inhibit fXa critically regulates PSC's impact on thrombin production. We find that under flow, PSC can unexpectedly accumulate on platelets to concentrations ~50 times higher than in the plasma. We also find that PSC limits thrombin production by occupying fV-specific binding sites on platelets. Our results show that changes in PSC can dramatically impact severity of pathological bleeding disorders. For the east Texas bleeding disorder, elevated PSC concentrations eliminate thrombin bursts, leading to bleeding. With fV deficiency, reducing PSC rescues thrombin production in severe fV deficiency and returns thrombin production due to mild fV deficiency to normal. Finally, thrombin production in severe hemophilia A can be substantially improved by blocking PSC's anticoagulant function.
Bakhta Fedlaoui, Teresa Cosentino, Zeina Al Sayed
et al.
BACKGROUND: Primary aldosteronism is the most common form of secondary hypertension. The most frequent genetic cause of aldosterone-producing adenomas is somatic mutations in the potassium channel KCNJ5. They affect the ion selectivity of the channel, with sodium influx leading to cell membrane depolarization and activation of calcium signaling, the major trigger for aldosterone biosynthesis. METHODS: To investigate how KCNJ5 mutations lead to the development of aldosterone-producing adenomas, we established an adrenocortical cell model in which sodium entry into the cells can be modulated on demand using chemogenetic tools [H295R-S2 $α$7-5HT3-R ($α$7-5HT3 receptor) cells]. We investigated their functional and molecular characteristics with regard to aldosterone biosynthesis and cell proliferation. RESULTS: A clonal cell line with stable expression of the chimeric $α$7-5HT3-R in H295R-S2 (human adrenocortical carcinoma cell line, Strain 2) cells was obtained. Increased sodium entry through $α$7-5HT3-R upon stimulation with uPSEM-817 (uPharmacologically Selective Effector Molecule-817) led to cell membrane depolarization, opening of voltage-gated Ca 2+ channels, and increased intracellular Ca 2+ concentrations, resulting in the stimulation of CYP11B2 expression and increased aldosterone biosynthesis. Increased intracellular sodium influx did not increase proliferation but rather induced apoptosis. RNA sequencing and steroidome analyses revealed unique profiles associated with Na + entry, with only partial overlap with Ang II (angiotensin II) or potassium-induced changes. CONCLUSIONS: H295R-S2 $α$7-5HT3-R cells are a new model reproducing the major features of cells harboring KCNJ5 mutations. Increased expression of CYP11B2 and stimulation of the mineralocorticoid biosynthesis pathway are associated with a decrease of cell proliferation and an increase of apoptosis, indicating that additional events may be required for the development of aldosterone-producing adenomas.
James K Ruffle, Samia Mohinta, Kelly Pegoretti Baruteau
et al.
The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clinical practice. This is a problem that machine learning could plausibly automate. Using glioma data from 1172 patients, we developed VASARI-auto, an automated labelling software applied to both open-source lesion masks and our openly available tumour segmentation model. In parallel, two consultant neuroradiologists independently quantified VASARI features in a subsample of 100 glioblastoma cases. We quantified: 1) agreement across neuroradiologists and VASARI-auto; 2) calibration of performance equity; 3) an economic workforce analysis; and 4) fidelity in predicting patient survival. Tumour segmentation was compatible with the current state of the art and equally performant regardless of age or sex. A modest inter-rater variability between in-house neuroradiologists was comparable to between neuroradiologists and VASARI-auto, with far higher agreement between VASARI-auto methods. The time taken for neuroradiologists to derive VASARI was substantially higher than VASARI-auto (mean time per case 317 vs. 3 seconds). A UK hospital workforce analysis forecast that three years of VASARI featurisation would demand 29,777 consultant neuroradiologist workforce hours (£1,574,935), reducible to 332 hours of computing time (and £146 of power) with VASARI-auto. The best-performing survival model utilised VASARI-auto features as opposed to those derived by neuroradiologists. VASARI-auto is a highly efficient automated labelling system with equitable performance across patient age or sex, a favourable economic profile if used as a decision support tool, and with non-inferior fidelity in downstream patient survival prediction. Future work should iterate upon and integrate such tools to enhance patient care.
Regulation of cell proliferation is a crucial aspect of tissue development and homeostasis and plays a major role in morphogenesis, wound healing, and tumor invasion. A phenomenon of such regulation is contact inhibition, which describes the dramatic slowing of proliferation, cell migration and individual cell growth when multiple cells are in contact with each other. While many physiological, molecular and genetic factors are known, the mechanism of contact inhibition is still not fully understood. In particular, the relevance of cellular signaling due to interfacial contact for contact inhibition is still debated. Cellular automata (CA) have been employed in the past as numerically efficient mathematical models to study the dynamics of cell ensembles, but they are not suitable to explore the origins of contact inhibition as such agent-based models assume fixed cell sizes. We develop a minimal, data-driven model to simulate the dynamics of planar cell cultures by extending a probabilistic CA to incorporate size changes of individual cells during growth and cell division. We successfully apply this model to previous in-vitro experiments on contact inhibition in epithelial tissue: After a systematic calibration of the model parameters to measurements of single-cell dynamics, our CA model quantitatively reproduces independent measurements of emergent, culture-wide features, like colony size, cell density and collective cell migration. In particular, the dynamics of the CA model also exhibit the transition from a low-density confluent regime to a stationary postconfluent regime with a rapid decrease in cell size and motion. This implies that the volume exclusion principle, a mechanical constraint which is the only inter-cellular interaction incorporated in the model, paired with a size-dependent proliferation rate is sufficient to generate the observed contact inhibition.
Nik Pohl, Domenik Priebe, Tamadur AlBaraghtheh
et al.
In silico testing of implant materials is a research area of high interest, as cost- and labour-intensive experiments may be omitted. However, assessing the tissue-material interaction mathematically and computationally can be very complex, in particular when functional, such as biodegradable, implant materials are investigated. In this work, we expand and refine suitable existing mathematical models of bone growth and magnesium-based implant degradation based on ordinary differential equations. We show that we can simulate the implant degradation, as well as the osseointegration in terms of relative bone volume fraction and changes in bone ultrastructure when applying the model to experimental data from titanium and magnesium-gadolinium implants for healing times up to 32 weeks. An additional sensitivity analysis highlights important parameters and their interactions. Moreover, we show that the model is predictive in terms of relative bone volume fraction with mean absolute errors below 6%.
The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Developmental morphogenesis is dependent on the inherent material properties of developing tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks and program-like function-calling, via the developmental gene co-expression system unique to metazoans, has a quasi-computational basis. Multi-attractor dynamical models are argued to be misapplied to global properties of development, and it is suggested that along with computationalism, dynamicism is similarly unsuitable to accounting for cognitive phenomena. Proposals are made for treating brains and other nervous tissues as novel forms of excitable matter with inherent properties which enable the intensification of cell-based basal cognition capabilities present throughout the tree of life.
Ryuta Mizutani, Rino Saiga, Yoshiro Yamamoto
et al.
Human mentality develops with age and is altered in psychiatric disorders, though their underlying mechanism is unknown. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the anterior cingulate cortex from eight schizophrenia and eight control cases. The distribution profiles of neurite curvature of the control cases showed a trend depending on their age, resulting in an age-correlated decrease in the standard deviation of neurite curvature (Pearson's r = -0.80, p = 0.018). In contrast to the control cases, the schizophrenia cases deviate upward from this correlation, exhibiting a 60% higher neurite curvature compared with the controls (p = 7.8 x 10^(-4)). The neurite curvature also showed a correlation with a hallucination score (Pearson's r = 0.80, p = 1.8 x 10^(-4)), indicating that neurite structure is relevant to brain function. We suggest that neurite curvature plays a pivotal role in brain aging and can be used as a hallmark to exploit a novel treatment of schizophrenia. This nano-CT paper is the result of our decade-long analysis and is unprecedented in terms of number of cases.
Nathaniel Braman, Prateek Prasanna, Kaustav Bera
et al.
Purpose: Tumor-associated vasculature differs from healthy blood vessels by its chaotic architecture and twistedness, which promotes treatment resistance. Measurable differences in these attributes may help stratify patients by likely benefit of systemic therapy (e.g. chemotherapy). In this work, we present a new category of radiomic biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancers, imaging modalities, and treatment regimens. Experimental Design: We segmented tumor vessels and computed mathematical measurements of twistedness and organization on routine pre-treatment radiology (CT or contrast-enhanced MRI) from 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n=371) or non-small cell lung cancer (NSCLC, n=187). Results: Across 4 chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (p<.05) predicted response in held out testing cohorts alone (AUC=0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. QuanTAV risk scores were prognostic of recurrence free survival in treatment cohorts chemotherapy for breast cancer (p=0.002, HR=1.25, 95% CI 1.08-1.44, C-index=.66) and chemoradiation for NSCLC (p=0.039, HR=1.28, 95% CI 1.01-1.62, C-index=0.66). Categorical QuanTAV risk groups were independently prognostic among all treatment groups, including NSCLC patients receiving chemotherapy (p=0.034, HR=2.29, 95% CI 1.07-4.94, C-index=0.62). Conclusions: Across these domains, we observed an association of vascular morphology on radiology with treatment outcome. Our findings suggest the potential of tumor-associated vasculature shape and structure as a prognostic and predictive biomarker for multiple cancers and treatments.
Mild traumatic brain injury (mTBI) is a complex syndrome that affects up to 600 per 100,000 individuals, with a particular concentration among military personnel. About half of all mTBI patients experience a diverse array of chronic symptoms which persist long after the acute injury. Hence, there is an urgent need for better understanding of the white matter and gray matter pathologies associated with mTBI to map which specific brain systems are impacted and identify courses of intervention. Previous works have linked mTBI to disruptions in white matter pathways and cortical surface abnormalities. Herein, we examine these hypothesized links in an exploratory study of joint structural connectivity and cortical surface changes associated with mTBI and its chronic symptoms. Briefly, we consider a cohort of 12 mTBI and 26 control subjects. A set of 588 cortical surface metrics and 4,753 structural connectivity metrics were extracted from cortical surface regions and diffusion weighted magnetic resonance imaging in each subject. Principal component analysis (PCA) was used to reduce the dimensionality of each metric set. We then applied independent component analysis (ICA) both to each PCA space individually and together in a joint ICA approach. We identified a stable independent component across the connectivity-only and joint ICAs which presented significant group differences in subject loadings (p<0.05, corrected). Additionally, we found that two mTBI symptoms, slowed thinking and forgetfulness, were significantly correlated (p<0.05, corrected) with mTBI subject loadings in a surface-only ICA. These surface-only loadings captured an increase in bilateral cortical thickness.
Chronic fetal hypoxia and infection are examples of adverse conditions during complicated pregnancy, which impact cardiac myogenesis and increase the lifetime risk of heart disease. However, the effects that chronic hypoxic or inflammatory environments exert on cardiac pacemaker cells are poorly understood. Here, we review the current evidence and novel avenues of bench-to-bed research in this field of perinatal cardiogenesis as well as its translational significance for early detection of future risk for cardiovascular disease.
Juan F. Yee-de León, Brenda Soto-García, Diana Aráiz-Hernández
et al.
The detection and analysis of circulating tumor cells (CTCs) may enable a broad range of cancer-related applications, including the identification of acquired drug resistance during treatments. However, the non-scalable fabrication, prolonged sample processing times, and the lack of automation, associated with most of the technologies developed to isolate these rare cells, have impeded their transition into the clinical practice. This work describes a novel membrane-based microfiltration device comprised of a fully automated sample processing unit and a machine-vision-enabled imaging system that allows the efficient isolation and rapid analysis of CTCs from blood. The device performance was characterized using four prostate cancer cell lines, including PC-3, VCaP, DU-145, and LNCaP, obtaining high assay reproducibility and capture efficiencies greater than 93% after processing 7.5 mL blood samples from healthy donors, spiked with 100 cancer cells. Cancer cells remained viable after filtration due to the minimal shear stress exerted over cells during the procedure, while the identification of cancer cells by immunostaining was not affected by the number of non-specific events captured on the membrane. We were also able to identify the androgen receptor (AR) point mutation T878A from 7.5 mL blood samples spiked with 50 LNCaP cells using RT-PCR and Sanger sequencing. Finally, CTCs were detected in 8 of 8 samples from patients diagnosed with metastatic prostate cancer (mean $\pm$ SEM = 21 $\pm$ 2.957 CTCs/mL, median = 21 CTC/mL), thereby validating the potential clinical utility of the device.
Alex Bersellini Farinotti, Gustaf Wigerblad, Diana Nascimento
et al.
Rheumatoid arthritis-associated joint pain is frequently observed independent of disease activity, suggesting unidentified pain mechanisms. We demonstrate that antibodies binding to cartilage, specific for collagen type II (CII) or cartilage oligomeric matrix protein (COMP), elicit mechanical hypersensitivity in mice, uncoupled from visual, histological and molecular indications of inflammation. Cartilage antibody-induced pain-like behavior does not depend on complement activation or joint inflammation, but instead on tissue antigen recognition and local immune complex (IC) formation. smFISH and IHC suggest that neuronal Fcgr1 and Fcgr2b mRNA are transported to peripheral ends of primary afferents. CII-ICs directly activate cultured WT but not FcRγ chain-deficient DRG neurons. In line with this observation, CII-IC does not induce mechanical hypersensitivity in FcRγ chain-deficient mice. Furthermore, injection of CII antibodies does not generate pain-like behavior in FcRγ chain-deficient mice or mice lacking activating FcγRs in neurons. In summary, this study defines functional coupling between autoantibodies and pain transmission that may facilitate the development of new disease-relevant pain therapeutics.
Florent Letronne, Geoffroy Laumet, Anne-Marie Ayral
et al.
Although several ADAMs (A disintegrin-like and metalloproteases) have been shown to contribute to the amy-loid precursor protein (APP) metabolism, the full spectrum of metalloproteases involved in this metabolism remains to be established. Transcriptomic analyses centred on metalloprotease genes unraveled a 50% decrease in ADAM30 expression that inversely correlates with amyloid load in Alzheimer's disease brains. Accordingly, in vitro down-or up-regulation of ADAM30 expression triggered an increase/decrease in A$β$ peptides levels whereas expression of a biologically inactive ADAM30 (ADAM30 mut) did not affect A$β$ secretion. Proteomics/cell-based experiments showed that ADAM30-dependent regulation of APP metabolism required both cathepsin D (CTSD) activation and APP sorting to lysosomes. Accordingly, in Alzheimer-like transgenic mice, neuronal ADAM30 over-expression lowered A$β$42 secretion in neuron primary cultures, soluble A$β$42 and amyloid plaque load levels in the brain and concomitantly enhanced CTSD activity and finally rescued long term potentiation.
Diabetic-induced peripheral neuropathy (DPN) is a diabetic late complication. The molecular mechanisms underlying the pathophysiology of nerve damage & sensory loss remain largely unclear. Recently, alterations in metabolic flux have gained attention a basis for organ damage in diabetes; however, peripheral sensory neurons have not been adequately analyzed. In the present study, we attempted to delineate the role of alteration of metabolic pathways in relation to nerve damage & sensory loss. We employed STZ-injected mouse model of type1 diabetes. To investigate the progression of DPN by behavioral measurements of sensitivity to thermal & mechanical stimuli and quantitative assessment of intraepidermal nerve fiber density. We employed a MS-based screen to address alterations in levels of metabolites in peripheral sciatic nerve (SN) & amino acids (AA) in serum over several months post-STZ administration. Although hyperglycemia & body weight changes occurred early, sensory loss & reduced intraepithelial branching of nociceptive nerves was only evident at 22 wks post-STZ. The longitudinal metabolites screen in SN demonstrated that mice at 12 and 22 wks post-STZ showed an early impairment the tricarboxylic acid. We found that levels of citric acid, ketoglutaric acid, succinic acid, fumaric acid & malic acid were observed to be significantly reduced in SN at 22 wks post-STZ. In addition, we also found the increase in levels of sorbitol & L-Lactate in SN from 12 wks post-STZ injection. AA screen in serum showed that the amino acids Val, Ile and Leu, increased more than 2-fold from 12 wks post-STZ. Similarly, the levels of Tyr, Asn, Ser, His, Ala, & Pro showed progressive increase. Our results indicate that the impaired TCA cycle metabolites in peripheral nerve is the primary cause of shunting metabolic substrate to compensatory pathways which leads to mitochondrial dysfunction & nerve damage.
The construction of a novel surgical instrument is considered, which is also a probing device providing a signal to the measuring equipment, which after its interpretation allows to obtain useful information about the section quality and the biomaterial properties. We propose here some formalized considerations on the possibility of its implementation for different variables registration. The idea is also extrapolated into the field of micrurgy which refers to the microelectrode techniques and the local potential registration in situ.
Many pressing medical challenges - such as diagnosing disease, enhancing directed stem cell differentiation, and classifying cancers - have long been hindered by limitations in our ability to quantify proteins in single cells. Mass-spectrometry (MS) is poised to transcend these limitations by developing powerful methods to routinely quantify thousands of proteins and proteoforms across many thousands of single cells. We outline specific technological developments and ideas that can increase the sensitivity and throughput of single cell MS by orders of magnitude and usher in this new age. These advances will transform medicine and ultimately contribute to understanding biological systems on an entirely new level.