Nat Ecol Evol. 2021 Mar 4. doi: 10.1038/s41559-021-01397-0. Online ahead of print.

ABSTRACT

Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here, we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. The model maps metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of growth rates and resistance levels, which characterize Pareto resistance mutations emerging at different drug dosages. We also predict the prevalent resistance mechanism depending on drug and nutrient levels: low-dosage drug defence is mounted by regulation, evolution of distinct metabolic sectors sets in at successive threshold dosages. Evolutionary resistance mechanisms include membrane permeability changes and drug target mutations. These predictions are confirmed by empirical growth inhibition curves and genomic data of Escherichia coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.

PMID:33664488 | DOI:10.1038/s41559-021-01397-0

Sci Rep. 2021 Mar 4;11(1):5222. doi: 10.1038/s41598-021-84538-6.

ABSTRACT

The malignant energetic demands are satisfied through glycolysis, glutaminolysis and de novo synthesis of fatty acids, while the host curses with a state of catabolism and systemic inflammation. The concurrent inhibition of both, tumor anabolism and host catabolism, and their effect upon tumor growth and whole animal metabolism, have not been evaluated. We aimed to evaluate in colon cancer cells a combination of six agents directed to block the tumor anabolism (orlistat + lonidamine + DON) and the host catabolism (growth hormone + insulin + indomethacin). Treatment reduced cellular viability, clonogenic capacity and cell cycle progression. These effects were associated with decreased glycolysis and oxidative phosphorylation, leading to a quiescent energetic phenotype, and with an aberrant transcriptomic landscape showing dysregulation in multiple metabolic pathways. The in vivo evaluation revealed a significant tumor volume inhibition, without damage to normal tissues. The six-drug combination preserved lean tissue and decreased fat loss, while the energy expenditure got decreased. Finally, a reduction in gene expression associated with thermogenesis was observed. Our findings demonstrate that the simultaneous use of this six-drug combination has anticancer effects by inducing a quiescent energetic phenotype of cultured cancer cells. Besides, the treatment is well-tolerated in mice and reduces whole animal energetic expenditure and fat loss.

PMID:33664364 | DOI:10.1038/s41598-021-84538-6

Trends Cell Biol. 2021 Mar 1:S0962-8924(21)00027-1. doi: 10.1016/j.tcb.2021.02.002. Online ahead of print.

ABSTRACT

There is increasing evidence that long noncoding RNAs (lncRNAs) are among the main regulatory factors of stem cell maintenance and differentiation. They act through various mechanisms and interactions with proteins, DNA, and RNA. This heterogeneity in function increases the capabilities of the lncRNome toolkit but also makes it difficult to predict the function of novel lncRNAs or even rely on biological information produced in animal models. As lncRNAs are species- and tissue-specific, the recent technical advances in self-renewal and differentiation of human embryonic stem cells (ESCs) make these cells the ideal system to identify key regulatory lncRNAs and study their molecular functions. Here we provide an overview of the functional versatility of lncRNA mechanistic heterogeneity in regulating pluripotency maintenance and human differentiation.

PMID:33663944 | DOI:10.1016/j.tcb.2021.02.002

Biotechnol Biofuels. 2021 Mar 4;14(1):55. doi: 10.1186/s13068-021-01891-4.

ABSTRACT

BACKGROUND: Pretreatments are commonly used to facilitate the deconstruction of lignocellulosic biomass to its component sugars and aromatics. Previously, we showed that iron ions can be used as co-catalysts to reduce the severity of dilute acid pretreatment of biomass. Transgenic iron-accumulating Arabidopsis and rice plants exhibited higher iron content in grains, increased biomass yield, and importantly, enhanced sugar release from the biomass.

RESULTS: In this study, we used intracellular ferritin (FerIN) alone and in combination with an improved version of cell wall-bound carbohydrate-binding module fused iron-binding peptide (IBPex) specifically targeting switchgrass, a bioenergy crop species. The FerIN switchgrass improved by 15% in height and 65% in yield, whereas the FerIN/IBPex transgenics showed enhancement up to 30% in height and 115% in yield. The FerIN and FerIN/IBPex switchgrass had 27% and 51% higher in planta iron accumulation than the empty vector (EV) control, respectively, under normal growth conditions. Improved pretreatability was observed in FerIN switchgrass (~ 14% more glucose release than the EV), and the FerIN/IBPex plants showed further enhancement in glucose release up to 24%.

CONCLUSIONS: We conclude that this iron-accumulating strategy can be transferred from model plants and applied to bioenergy crops, such as switchgrass. The intra- and extra-cellular iron incorporation approach improves biomass pretreatability and digestibility, providing upgraded feedstocks for the production of biofuels and bioproducts.

PMID:33663584 | DOI:10.1186/s13068-021-01891-4

BMC Bioinformatics. 2021 Mar 4;22(1):108. doi: 10.1186/s12859-021-04046-2.

ABSTRACT

BACKGROUND: Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.

RESULTS: In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.

CONCLUSION: DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus .

PMID:33663384 | DOI:10.1186/s12859-021-04046-2

Biomed Phys Eng Express. 2021 Mar 4. doi: 10.1088/2057-1976/abec18. Online ahead of print.

ABSTRACT

Logan graphical analysis (LGA) is a method for in vivo quantification of tracer kinetics in positron emission tomography (PET). The shortcoming of LGA is the presence of a negative bias in the estimated parameters for noisy data. Various approaches have been proposed to address this issue. We recently applied an alternative regression method called least-squares cubic (LSC), which considers the errors in both the predictor and response variables to estimate the LGA slope. LSC reduced the bias in non-displaceable binding potential estimates while causing slight increases in the variance. In this study, we combined LSC with a principal component analysis (PCA) denoising technique to counteract the effects of variance on parametric image quality, which was assessed in terms of the contrast between gray and white matter. Tissue time-activity curves were denoised through PCA, prior to estimating the regression parameters using LSC. We refer to this approach as LSC-PCA. LSC-PCA was assessed against OLS-PCA (PCA with ordinary least-squares (OLS)), LSC, and conventional OLS-based LGA. Comparisons were made for simulated11C-carfentanil and11C Pittsburgh compound B (11C-PiB) data, and clinical11C-PiB PET images. PCA-based methods were compared over a range of principal components, varied by the percentage variance they account for in the data. The results showed reduced variances in distribution volume ratio estimates in the simulations for LSC-PCA compared to LSC, and lower bias compared to OLS-PCA and OLS. Contrasts were not significantly improved in clinical data, but they showed a significant improvement in simulation data |indicating a potential advantage of LSC-PCA over OLS-PCA. The effects of bias reintroduction when many principal components are used were also observed in OLS-PCA clinical images. We therefore encourage the use of LSC-PCA. LSC-PCA can allow the use of many principal components with minimal risk of bias, thereby strengthening the interpretation of PET parametric images.

PMID:33662939 | DOI:10.1088/2057-1976/abec18

Curr Opin Plant Biol. 2021 Mar 1;61:102012. doi: 10.1016/j.pbi.2021.102012. Online ahead of print.

ABSTRACT

How epigenetic memory states are established and maintained is a central question in gene regulation. A major epigenetic process important for developmental biology involves Polycomb-mediated chromatin silencing. Significant progress has recently been made on elucidating Polycomb silencing in plant systems through analysis of Arabidopsis FLOWERING LOCUS C (FLC). Quantitative silencing of FLC by prolonged cold exposure was shown to represent an ON to OFF switch in an increasing proportion of cells. Here, we review the underlying all-or-nothing, digital paradigm for Polycomb epigenetic silencing. We then examine other Arabidopsis Polycomb-regulated targets where digital regulation may also be relevant.

PMID:33662809 | DOI:10.1016/j.pbi.2021.102012

Chemosphere. 2021 Feb 9;275:129888. doi: 10.1016/j.chemosphere.2021.129888. Online ahead of print.

ABSTRACT

Carbon nanotubes (CNTs) have been widely studied because of their potential applications. The increasing applications of CNTs and less known of their environmental fates rise concerns about their safety. In this study, the biotransformation of multi-walled carbon nanotubes (MWCNTs) by Labrys sp. WJW was investigated. Within 16 days, qPCR analysis showed that cell numbers increased 4.92 ± 0.36 folds using 100 mg/L MWCNTs as the sole carbon source. The biotransformation of MWCNTs, which led to morphology and functional group change, was evidenced by transmission electron microscopy and X-ray photoelectron spectroscopy analyses. Raman spectra illustrated that more defects and disordered carbon appeared on MWCNTs during incubation. The underlying biotransformation mechanism of MWCNTs through an extracellular bacterial Fenton-like reaction was demonstrated. In this bacteria-mediated reaction, the OH production was induced by reduction of H2O2 involved a continuous cycle of Fe(II)/Fe(III). Bacterial biotransformation of MWCNTs will provide new insights into the understanding of CNTs bioremediation processes.

PMID:33662725 | DOI:10.1016/j.chemosphere.2021.129888

J Biol Chem. 2021 Mar 1:100485. doi: 10.1016/j.jbc.2021.100485. Online ahead of print.

ABSTRACT

Copper is essential for the activity and stability of cytochrome c oxidase (CcO), the terminal enzyme of the mitochondrial respiratory chain. Loss-of-function mutations in genes required for copper transport to CcO result in fatal human disorders. Despite the fundamental importance of copper in mitochondrial and organismal physiology, systematic identification of genes that regulate mitochondrial copper homeostasis is lacking. To discover these genes, we performed a genome-wide screen using a library of DNA-barcoded yeast deletion mutants grown in copper-supplemented media. Our screen recovered a number of genes known to be involved in cellular copper homeostasis as well as genes previously not linked to mitochondrial copper biology. These newly identified genes include the subunits of the adaptor protein 3 complex (AP-3) and components of the cellular pH-sensing pathway Rim20 and Rim21, both of which are known to affect vacuolar function. We find that AP-3 and Rim mutants exhibit decreased vacuolar acidity, which in turn perturbs mitochondrial copper homeostasis and CcO function. CcO activity of these mutants could be rescued by either restoring vacuolar pH or by supplementing growth media with additional copper. Consistent with these genetic data, pharmacological inhibition of the vacuolar proton pump leads to decreased mitochondrial copper content and a concomitant decrease in CcO abundance and activity. Taken together, our study uncovered novel genetic regulators of mitochondrial copper homeostasis and provided a mechanism by which vacuolar pH impacts mitochondrial respiration through copper homeostasis.

PMID:33662401 | DOI:10.1016/j.jbc.2021.100485

Cell Host Microbe. 2021 Feb 26:S1931-3128(21)00047-0. doi: 10.1016/j.chom.2021.02.001. Online ahead of print.

ABSTRACT

Isobiotic mice, with an identical stable microbiota composition, potentially allow models of host-microbial mutualism to be studied over time and between different laboratories. To understand microbiota evolution in these models, we carried out a 6-year experiment in mice colonized with 12 representative taxa. Increased non-synonymous to synonymous mutation rates indicate positive selection in multiple taxa, particularly for genes annotated for nutrient acquisition or replication. Microbial sub-strains that evolved within a single taxon can stably coexist, consistent with niche partitioning of ecotypes in the complex intestinal environment. Dietary shifts trigger rapid transcriptional adaptation to macronutrient and micronutrient changes in individual taxa and alterations in taxa biomass. The proportions of different sub-strains are also rapidly altered after dietary shift. This indicates that microbial taxa within a mouse colony adapt to changes in the intestinal environment by long-term genomic positive selection and short-term effects of transcriptional reprogramming and adjustments in sub-strain proportions.

PMID:33662276 | DOI:10.1016/j.chom.2021.02.001

PLoS One. 2021 Mar 4;16(3):e0247711. doi: 10.1371/journal.pone.0247711. eCollection 2021.

ABSTRACT

PCR methods are presently the standard for the diagnosis of Coronavirus disease 2019 (COVID-19), but additional methodologies are needed to complement PCR methods, which have some limitations. Here, we validated and investigated the usefulness of measuring serum antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using the iFlash3000 CLIA analyzer. We measured IgM and IgG titers against SARS-CoV-2 in sera collected from 26 PCR-positive COVID-19 patients, 53 COVID-19-suspected but PCR-negative patients, and 20 and 100 randomly selected non-COVID-19 patients who visited our hospital in 2020 and 2017, respectively. The repeatability and within-laboratory precision were obviously good in validations, following to the CLSI document EP15-A3. Linearity was also considered good between 0.6 AU/mL and 112.7 AU/mL for SARS-CoV-2 IgM and between 3.2 AU/mL and 55.3 AU/mL for SARS-CoV-2 IgG, while the linearity curves plateaued above the upper measurement range. We also confirmed that the seroconversion and no-antibody titers were over the cutoff values in all 100 serum samples collected in 2017. These results indicate that this measurement system successfully detects SARS-CoV-2 IgM/IgG. We observed four false-positive cases in the IgM assay and no false-positive cases in the IgG assay when 111 serum samples known to contain autoantibodies were evaluated. The concordance rates of the antibody test with the PCR test were 98.1% for SARS-CoV-2 IgM and 100% for IgG among PCR-negative cases and 30.8% for SARS-CoV-2 IgM and 73.1% for SARS-CoV-2 IgG among PCR-positive cases. In conclusion, the performance of this new automated method for detecting antibody against both N and S proteins of SARS-CoV-2 is sufficient for use in laboratory testing.

PMID:33661990 | DOI:10.1371/journal.pone.0247711

Blood Adv. 2021 Mar 9;5(5):1388-1402. doi: 10.1182/bloodadvances.2020003670.

ABSTRACT

Sickle cell disease (SCD) is associated with hemolysis, vascular inflammation, and organ damage. Affected patients experience chronic painful vaso-occlusive events requiring hospitalization. Hypoxia-induced polymerization of sickle hemoglobin S (HbS) contributes to sickling of red blood cells (RBCs) and disease pathophysiology. Dilution of HbS with nonsickling hemoglobin or hemoglobin with increased oxygen affinity, such as fetal hemoglobin or HbS bound to aromatic aldehydes, is clinically beneficial in decreasing polymerization. We investigated a novel alternate approach to modify HbS and decrease polymerization by inhibiting methionine aminopeptidase 2 (MetAP2), which cleaves the initiator methionine (iMet) from Val1 of α-globin and βS-globin. Kinetic studies with MetAP2 show that βS-globin is a fivefold better substrate than α-globin. Knockdown of MetAP2 in human umbilical cord blood-derived erythroid progenitor 2 cells shows more extensive modification of α-globin than β-globin, consistent with kinetic data. Treatment of human erythroid cells in vitro or Townes SCD mice in vivo with selective MetAP2 inhibitors extensively modifies both globins with N-terminal iMet and acetylated iMet. HbS modification by MetAP2 inhibition increases oxygen affinity, as measured by decreased oxygen tension at which hemoglobin is 50% saturated. Acetyl-iMet modification on βS-globin delays HbS polymerization under hypoxia. MetAP2 inhibitor-treated Townes mice reach 50% total HbS modification, significantly increasing the affinity of RBCs for oxygen, increasing whole blood single-cell RBC oxygen saturation, and decreasing fractional flow velocity losses in blood rheology under decreased oxygen pressures. Crystal structures of modified HbS variants show stabilization of the nonpolymerizing high O2-affinity R2 state, explaining modified HbS antisickling activity. Further study of MetAP2 inhibition as a potential therapeutic target for SCD is warranted.

PMID:33661300 | DOI:10.1182/bloodadvances.2020003670

Biol Chem. 2021 Mar 2. doi: 10.1515/hsz-2020-0392. Online ahead of print.

ABSTRACT

Genome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our analysis protocol on the analysis of a genome-wide CRISPR screen in hTERT-RPE1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our analysis protocol is general and can be applied on any cell type and with different CRISPR enzymes.

PMID:33660495 | DOI:10.1515/hsz-2020-0392

Neuromolecular Med. 2021 Mar 4. doi: 10.1007/s12017-021-08651-5. Online ahead of print.

ABSTRACT

The current SARS-CoV-2 outbreak, which causes COVID-19, is particularly devastating for individuals with chronic medical conditions, in particular those with Down Syndrome (DS) who often exhibit a higher prevalence of respiratory tract infections, immune dysregulation and potential complications. The incidence of Alzheimer's disease (AD) is much higher in DS than in the general population, possibly increasing further the risk of COVID-19 infection and its complications. Here we provide a biological overview with regard to specific susceptibility of individuals with DS to SARS-CoV-2 infection as well as data from a recent survey on the prevalence of COVID-19 among them. We see an urgent need to protect people with DS, especially those with AD, from COVID-19 and future pandemics and focus on developing protective measures, which also include interventions by health systems worldwide for reducing the negative social effects of long-term isolation and increased periods of hospitalization.

PMID:33660221 | DOI:10.1007/s12017-021-08651-5

J Cancer Res Clin Oncol. 2021 Mar 3. doi: 10.1007/s00432-021-03580-z. Online ahead of print.

ABSTRACT

PURPOSE: Plasmablastic lymphoma (PBL) is a rare and aggressive B-cell malignancy with a heterogenous clinical and prognostic spectrum, determined by multiple factors, including age, HIV- and MYC-status. While there exist several validated scoring systems for diffuse large B-cell lymphoma, which incorporate basic clinical features (age, lactate dehydrogenase, sites of (extranodal) involvement, stage and performance), none of these have been systematically assessed in PBL.

METHODS: We determined the (age-adjusted; aa)-International Prognostic Index (IPI), revised IPI (R-IPI), and National Comprehensive Cancer Network IPI (NCCN-IPI) in a comprehensive multi-center cohort (n = 78) of PBL patients. Further, all indices were comparatively investigated for model quality and concordance.

RESULTS: Univariate analysis revealed significant prognostic capabilities for all indices, all of which identified a subgroup with favorable outcome. Discriminatory power between patients with less benign prognosis and especially refractory disease exhibited significant variability. Subsequently, stratified models for each risk score were compared employing corrected Akaike's information criterion (cAIC) and Harrel's concordance index (c-index). Here, the NCCN-IPI outperformed both IPI and R-IPI regarding c-index with ambiguous cAIC results, underlining its clinical utility and suggesting it for preferential use in clinical practice.

CONCLUSION: Our current observations support the use of the IPI and its enhanced derivatives in PBL patients. There is, however, a distinct requirement for novel prognostic tools to better delineate subgroups at risk for early relapse or refractory disease as well as late relapse. A comprehensive molecular characterization of a clinically annotated cohort of PBL patients is therefore urgently warranted.

PMID:33660007 | DOI:10.1007/s00432-021-03580-z

Netw Syst Med. 2021 Feb 25;4(1):2-50. doi: 10.1089/nsm.2020.0003. eCollection 2021 Feb.

ABSTRACT

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

PMID:33659919 | PMC:PMC7919422 | DOI:10.1089/nsm.2020.0003

STAR Protoc. 2021 Feb 3;2(1):100327. doi: 10.1016/j.xpro.2021.100327. eCollection 2021 Mar 19.

ABSTRACT

Evaluating drug sensitivity is improved by directly quantifying death kinetics, rather than correlates of viability, such as metabolic activity. This is challenging, requiring time-lapse microscopy and genetically encoded labels to distinguish live and dead cells. Here, we describe fluorescence-based and lysis-dependent inference of cell death kinetics (FLICK). This method requires only a standard fluorescence plate reader, retaining the high-throughput nature and broad accessibility of common viability assays. However, FLICK specifically quantifies death, including an accurate inference of death kinetics. For complete details on the use and execution of this protocol, please refer to Richards et al. (2020).

PMID:33659903 | PMC:PMC7890003 | DOI:10.1016/j.xpro.2021.100327

iScience. 2021 Jan 29;24(2):102118. doi: 10.1016/j.isci.2021.102118. eCollection 2021 Feb 19.

ABSTRACT

Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells.

PMID:33659881 | PMC:PMC7892930 | DOI:10.1016/j.isci.2021.102118

iScience. 2021 Jan 29;24(2):102112. doi: 10.1016/j.isci.2021.102112. eCollection 2021 Feb 19.

ABSTRACT

Macrophages are highly plastic immune cells that dynamically integrate microenvironmental signals to shape their own functional phenotypes, a process known as polarization. Here we develop a large-scale mechanistic computational model that for the first time enables a systems-level characterization, from quantitative, temporal, dose-dependent, and single-cell perspectives, of macrophage polarization driven by a complex multi-pathway signaling network. The model was extensively calibrated and validated against literature and focused on in-house experimental data. Using the model, we generated dynamic phenotype maps in response to numerous combinations of polarizing signals; we also probed into an in silico population of model-based macrophages to examine the impact of polarization continuum at the single-cell level. Additionally, we analyzed the model under an in vitro condition of peripheral arterial disease to evaluate strategies that can potentially induce therapeutic macrophage repolarization. Our model is a key step toward the future development of a network-centric, comprehensive "virtual macrophage" simulation platform.

PMID:33659877 | PMC:PMC7895754 | DOI:10.1016/j.isci.2021.102112

Mitochondrial DNA B Resour. 2021 Feb 19;6(2):624-626. doi: 10.1080/23802359.2021.1875927.

ABSTRACT

The Egyptian mongoose, Herpestes ichneumon, is the only extant mongoose in Europe, with populations still distributed in Africa and the Middle East. In this study, we present the first mitochondrial genome sequence of Herpestes ichneumon and we investigate its phylogenetic position within Feliformia suborder. The resultant mitogenome sequence is 16,775 bps, composed of a conserved set of 37 genes containing 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and a control region. Our results represent a valuable resource for further phylogeographical studies.

PMID:33659708 | PMC:PMC7899645 | DOI:10.1080/23802359.2021.1875927