bluuebunny/crossref_metadata_2025 huggingface dataset:
Diversity of function and higher-order structure within HWE sensor histidine kinases
Authors: Dikiy Igor , Swingle Danielle , Toy Kaitlyn , Edupuganti Uthama R. , Rivera-Cancel Giomar , Gardner Kevin H.
Published: July 2020

Integral to the protein structure/function paradigm, oligomeric state is typically conserved along with function across evolution. However, notable exceptions such as the hemoglobins show how evolution can alter oligomerization to enable new regulatory mechanisms. Here we examine this linkage in histidine kinases (HKs), a large class of widely distributed prokaryotic environmental sensors. While the majority of HKs are transmembrane homodimers, members of the HWE/HisKA2 family can deviate from this architecture as exemplified by our finding of a monomeric soluble HWE/HisKA2 HK (EL346, a photosensing Light-Oxygen-Voltage (LOV)-HK). To further explore the diversity of oligomerization states and regulation within this family, we biophysically and biochemically characterized multiple EL346 homologs and found a range of HK oligomeric states and functions. Three LOV-HK homologs are primarily dimeric with differing structural and functional responses to light, while two Per-ARNT-Sim (PAS)-HKs interconvert between differentially active monomers and dimers, suggesting dimerization might control enzymatic activity for these proteins. Finally, we examined putative interfaces in a dimeric LOV-HK, finding that multiple regions contribute to dimerization. Our findings suggest the potential for novel regulatory modes and oligomeric states beyond those traditionally defined for this important family of environmental sensors.
Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance
Authors: Malec Scott A. , Bernstam Elmer V. , Wei Peng , Boyce Richard D. , Cohen Trevor
Published: July 2020

Introduction

Confounding bias threatens the reliability of observational studies and poses a significant scientific challenge. This paper introduces a framework for identifying confounding factors by exploiting literature-derived computable knowledge. In previous work, we have shown that semantic constraint search over computable knowledge extracted from the literature can be useful for reducing confounding bias in statistical models of EHR-derived observational clinical data. We hypothesize that adjustment sets of literature-derived confounders could also improve causal inference.

Methods

We introduce two methods (semantic vectors and string-based confounder search) that query the literature for potential confounders and use this information to build models from EHR-derived data to more accurately estimate causal effects. These methods search SemMedDB for indications TREATED BY the drug that is also known to CAUSE the adverse event. For evaluation, we attempt to rediscover associations in a publicly available reference dataset containing expected pairwise relationships between drugs and adverse events from empirical data derived from a corpus of 2.2M EHR-derived clinical notes. For our knowledge-base, we use SemMedDB, a database of computable knowledge mined from the biomedical literature. Using standard adjustment and causal inference procedures on dichotomous drug exposures, confounders, and adverse event outcomes, varying numbers of literature-derived confounders are combined with EHR data to predict and estimate causal effects in light of the literature-derived confounders. We then compare the performance of the new methods with naive (χ2, reporting odds ratio) measures of association.

Results and Conclusions

Logistic regression with ten vector space-derived confounders achieved the most improvement with AUROC of 0.628 (95% CI: [0.556,0.720]), compared with baselineχ20.507 (95% CI: [0.431,0.583]). Bias reduction was improved more often in modeling methods using more rather than less information, and using semantic vector rather than string-based search. We found computable knowledge useful for improving automated causal inference, and identified opportunities for further improvement, including a role for adjudicating literature-derived confounders by subject matter experts.

Graphical Abstract

Highlights

Access to causal background knowledge is required for causal learning to scale to large datasets.

We introduce a framework for identifying confounders to enhance causal inference from EHR.

We search computable knowledge for indications TREATED BY the drug that CAUSE the outcome.

Literature-derived confounders reduce confounding bias in EHR data.

Structured knowledge helps interpret and explain data captured in clinical narratives.
Acoustic and language analysis of speech for suicide ideation among US veterans
Authors: Belouali Anas , Gupta Samir , Sourirajan Vaibhav , Yu Jiawei , Allen Nathaniel , Alaoui Adil , Dutton Mary Ann , Reinhard Matthew J.
Published: July 2020

U.S. veterans are 1.5 times more likely to die by suicide than Americans who never served in the military. Considering such high rates, there is an urgent need to develop innovative approaches for objective and clinically applicable assessments to detect individuals at high risk. We hypothesize that speech in suicidal veterans has a range of distinctive acoustic and linguistic features. The purpose of this work is to build an automated machine learning and natural language processing tool to screen for suicidality. Veterans made 588 narrative audio recordings via a mobile app in a real-life setting. In addition, veterans completed self-report psychiatric scales and questionnaires. Recordings were analyzed to extract voice characteristics including prosodic, phonation, and glottal. The audios were also transcribed to extract textual features for linguistic analysis. We evaluated the acoustic and linguistic features using both statistical significance and ensemble feature selection. We also examined the performance of different machine learning algorithms on multiple combinations of features to classify suicidal and non-suicidal audios. Random Forest classifier correctly identified suicidal ideation in veterans based on the combined set of acoustic and linguistic features of speech with 86% sensitivity, 70% specificity, and an area under the receiver operating characteristic curve (AUC) of 80%. Speech analysis of audios collected from veterans in everyday life settings using smartphones is a promising approach for suicidal ideation detection. A machine learning classifier may eventually help clinicians identify and monitor high-risk veterans.
Predictors of Anxiety Regarding The COVID-19 Pandemic Among Health-care Workers in a Hospital Not Assigned to Manage COVID-19 Patients in Nepal
Authors: Thapa Lekhjung , Ghimire Aparna , Ghimire Sulochana , Sharma Nooma , Shrestha Shakti , Devkota Medha , Bhattarai Suman , Singh Maharjan Anzil Man , Lohani Subash , Phuyal Subash , Maharjan Pratibha
Published: July 2020

Introduction

We studied the levels and severity of anxiety caused by COVID-19 amongst frontline health-care workers (HCWs) in a tertiary care neurological hospital in Nepal, not assigned to manage COVID-19 cases.

Methods

A cross-sectional study was conducted on 100 frontline Health Care Workers (HCWs) using a 10-point subjective assessment tool, the Anxiety Level Index (ALI), and the Zung Self Rating Anxiety Score (SAS), to assess the level of anxiety regarding COVID-19.

Results

On ALI 55% of HCW were found to have marked severe anxiety (6-9), however, on SAS 44% of HCW’s reported anxiety. The majority HCW’s were female (n=78) with nurses forming 62% of the sample size. The mean age (±SD) was 26.8 years (± 8.17). Factors associated with significantly higher levels of anxiety regarding COVID-19 on ALI were age (p=0.002), sex (p=0.001), receiving regular COVID-19 updates via social media (p=0.011) and a high frequency of checking for COVID-19 information authenticity (p=0.039). Work experience (p=0.026) and frequency of checking for information authenticity (p=0.029) were factors found to increase SAS measured anxiety and were found to be associated with significantly higher levels of anxiety. Multivariate logistic regression analysis showed that respondents with work experience of ≤2 years were 0.380 (95% CI 0.158 to 0.910) times less likely to have anxiety than those with work experience of ≥2 years. Similarly, the odds of having anxiety among those who checked information authenticity less frequently was 0.377 (95% CI 0.153 to 0.931) times less than those who often did.

Conclusion

The COVID-19 outbreak has caused a substantial impact on the mental health of frontline HCWs in a hospital not assigned to manage COVID-19 patients. Length of time of worked in healthcare and the frequency of checking for COVID-19-related information were significant predictors of anxiety.
Clinical, Behavioral and Social Factors Associated with Racial Disparities in Hospitalized and Ambulatory COVID-19 Patients from an Integrated Health Care System in Georgia
Authors: Lobelo Felipe , Bienvenida Alan , Leung Serena , Mbanya Armand , Leslie Elizabeth J , Koplan Kate E , Shin S. Ryan
Published: July 2020

Introduction

Racial and ethnic minorities have shouldered a disproportioned burden of coronavirus disease 2019 (COVID-19) infection to date in the US, but data on the various drivers of these disparities is limited.

Objectives

To describe the characteristics and outcomes of COVID-19 patients and explore factors associated with hospitalization risk by race.

Methods

Case series of 448 consecutive patients with confirmed COVID-19 seen at Kaiser Permanente Georgia (KPGA), an integrated health care system serving the Atlanta metropolitan area, from March 3 to May 12, 2020. KPGA members with laboratory-confirmed COVID-19. Multivariable analyses for hospitalization risk also included an additional 3489 persons under investigation (PUI) with suspected infection. COVID-19 treatment and outcomes, underlying comorbidities and quality of care management metrics, socio-demographic and other individual and community-level social determinants of health (SDOH) indicators.

Results

Of 448 COVID-19 positive members, 68,3% was non-Hispanic Black (n=306), 18% non-Hispanic White (n=81) and 13,7% Other race (n=61). Median age was 54 [IQR 43-63) years. Overall, 224 patients were hospitalized, median age 60 (50-69) years. Black race was a significant factor in the Confirmed + PUI, female and male models (ORs from 1.98 to 2.19). Obesity was associated with higher hospitalization odds in the confirmed, confirmed + PUI, Black and male models (ORs from 1.78 to 2.77). Chronic disease control metrics (diabetes, hypertension, hyperlipidemia) were associated with lower odds of hospitalization ranging from 48% to 35% in the confirmed + PUI and Black models. Self-reported physical inactivity was associated with 50% higher hospitalization odds in the Black and Female models. Residence in the Northeast region of Atlanta was associated with lower hospitalization odds in the Confirmed + PUI, White and female models (ORs from 0.22 to 0.64)

Conclusions

We found that non-Hispanic Black KPGA members had a disproportionately higher risk of infection and, after adjusting for covariates, twice the risk of hospitalization compared to other race groups. We found no significant differences in clinical outcomes or mortality across race/ethnicity groups. In addition to age, sex and comorbidity burden, pre-pandemic self-reported exercise, metrics on quality of care and control of underlying cardio-metabolic diseases, and location of residence in Atlanta were significantly associated with hospitalization risk by race groups. Beyond well-known physiologic and clinical factors, individual and community-level social indicators and health behaviors must be considered as interventions designed to reduce COVID-19 disparities and the systemic effects of racism are implemented.
A model of COVID-19 propagation based on a gamma subordinated negative binomial branching process
Authors: Levesque Jérôme , Maybury David W. , Shaw R. H. A. David
Published: July 2020

We build a parsimonious Crump-Mode-Jagers continuous time branching process of COVID-19 propagation based on a negative binomial process subordinated by a gamma subordinator. By focusing on the stochastic nature of the process in small populations, our model provides decision making insight into mitigation strategies as an outbreak begins. Our model accommodates contact tracing and isolation, allowing for comparisons between different types of intervention. We emphasize a physical interpretation of the disease propagation throughout which affords analytical results for comparison to simulations. Our model provides a basis for decision makers to understand the likely trade-offs and consequences between alternative outbreak mitigation strategies particularly in office environments and confined work-spaces. Combining the asymptotic limit of our model with Bayesian hierarchical techniques, we provide US county level inferences for the reproduction number from cumulative case count data over July and August of this year.
Zika Virus Congenital Microcephaly Severity Classification and the Association of Severity with Neuropsychomotor Development
Authors: Esper Nathalia Bianchini , Franco Alexandre Rosa , Soder Ricardo Bernardi , Bomfim Rodrigo Cerqueira , Nunes Magda Lahorgue , Radaelli Graciane , Esper Katherine Bianchini , Kotoski Aline , Pripp Willian , Neto Felipe Kalil , Azambuja Luciana Schermann , Mathias Nathália Alves , da Costa Danielle Irigoyen , Portuguez Mirna Wetters , da Costa Jaderson Costa , Buchweitz Augusto
Published: July 2020

Background

Zika virus infection during pregnancy is linked to birth defects, most notably, microcephaly, which in its turn, is associated with neurodevelopmental delays.

Objective

The goal of the study is to propose a method for severity classification of congenital microcephaly based on neuroradiological findings of MRI scans, and to investigate the association of severity with neuropsychomotor developmental scores. We also propose a semi-automated method for MRI-based severity classification of microcephaly.

Methods

Cross-sectional investigation of 42 infants born with congenital Zika infection. Bayley-III developmental evaluations and MRI scans were carried out at ages 13-39 months (mean: 24.8, SD: 5.8). The severity score was generated based on neuroradiologist evaluations of brain malformations. Next, we established a distribution of Zika virus-microcephaly severity score into mild, moderate, and severe and investigated the association of severity with neuropsychomotor developmental scores. Finally, we propose a simplified semi-automated procedure for estimating the severity score, based only on volumetric measures.

Results

Results showed a correlation of r = 0.89 (p < 0.001) between the Zika virus-microcephaly severity score and the semi-automated method. The trimester of infection did not correlate with the semi-automated method. Neuropsychomotor development correlated with the severity classification based on radiological readings and with the semi-automated method; the more severe the imaging scores, the lower neuropsychomotor developmental scores.

Conclusion

The severity classification methods may be used to evaluate severity of microcephaly and possible association with developmental consequences. The semi-automated methods thus may be an alternative for prediction of severity of microcephaly using only one MRI sequence.
The heterogeneous nature of the Coronavirus receptor, angiotensin-converting enzyme 2 (ACE2) in differentiating airway epithelia
Authors: Manna Vincent J. , Caradonna Salvatore J.
Published: July 2020

Coronavirus Disease 2019 (COVID-19) is transmitted through respiratory droplets containing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) particles. Once inhaled, SARS-CoV-2 particles gain entry into respiratory ciliated cells by interacting with angiotensin converting enzyme 2 (ACE2). It is known that ACE2 functions within the renin-angiotensin system to regulate blood pressure, fluid homeostasis and inflammation. However, it is largely unknown what roles ACE2 has in ciliated cells of the airway. Therefore, understanding the function and nature of ACE2 within airway tissue has become an essential element in combatting the COVID-19 pandemic. Airway mucociliary tissue was generatedin-vitrousing primary human nasal epithelial cells isolated from nasal turbinates of donors and the air-liquid interface (ALI) model of differentiation. Using ALI tissue we cloned transcripts for three distinct variants of ACE2, one of which encodes the full-length ACE2 protein, the other two transcripts are truncated isoforms that had only been predicted to exist via sequence analysis software. We demonstrate that all three isoforms have the capacity to be glycosylated, a known modification of full-length ACE2. Immunofluorescence microscopy of individual ACE2 isoform transfected cells reveals distinct localization of variant 1 relative to X1 and X2. Double staining immunohistochemistry of ALI tissue using antibodies to either the N-term or C-term region of ACE2 revealed distinct and overlapping signals in the apical cytosol of ciliated cells. Most notably only the ACE2 C-term antibody displayed plasma-membrane localization in ciliated cells. We also observed a decrease in the total amount of ACE2 in ALI tissue derived from a 33 year-old male donor when compared to a 34 year-old female donor, thus there may be variation in the abundance of ACE2 protein in the airway among the population. Together, our data begins to highlight the dynamic status of the ACE2 protein in airway mucociliary tissue and we propose multiple ACE2 parameters that may impact an individual’s susceptibility to SARS-CoV-2. These parameters include the balance of cytosolic versus membrane bound ACE2, isoform expression levels, maintenance of post-translational modifications and the impact of genetic, environmental and lifestyle factors on these processes.
Developing a method for real-time visualization of cellulase activity
Authors: Kumari Pallavi , Sayas Tali , Bucki Patricia , Miyara Sigal Brown , Kleiman Maya
Published: July 2020

Studying the interactions between microorganisms and plant roots is crucial for understanding a variety of phenomena concerning crop yield and health. The role of root surface properties in these interactions, is rarely addressed. To this end, we previously built a synthetic system, from the inert polymer polydimethyl siloxane (PDMS), mimicking the root surface microstructure, using a replication technique. This replica enables the study of isolated effects of surface structure on microorganism-plant interactions. Since the root surface is composed mostly of cellulose, using cellulose-like materials as our replica, instead of PDMS, is the next logical step. This will enable following the hydrolysis of such surfaces as a result of microorganisms secreting Plant Cell Wall Degrading Enzymes (PCWDE), and in particular, cellulase. Visualization of such hydrolysis in a synthetic system can assist in studying the localization and activity of microorganisms and how they correlate with surface microtopography, separately from chemical plant signals.

In this work, we modified the known carboxymethyl cellulase (CMC) hydrolysis visualization method to enable real-time tracking of cellulase activity of microorganisms on the surface. Surface was formed from pure CMC, rather than CMC incorporated in agar as is often done, and by that, eliminating diffusion issues. Acridine orange dye, which is compatible, at low concentrations, with microorganisms, as opposed to other routinely used dyes, was incorporated into the film. The dye disassociated from the film when hydrolysis occurred, forming a halo surrounding the point of hydrolysis. This enabled real-time visualization since the common need for post hydrolysis dyeing was negated. Using Root Knot Nematode (RKN) as a model organism that penetrates the plant root, we showed it was possible to follow microorganism cellulase secretion on the surface in the form of CMC film hydrolysis. Furthermore, the addition of natural additives, in the form of root extract was also shown to be an option and resulted in an increased RKN response. We tested our newly developed method by changing temperature and pH conditions and by characterization of the hydrolyzed surface using both Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM).

This method will be implemented in the future on a root surface microstructure replica. We believe the combination of this new method with our previously developed root surface microstructure replication technique can open a new avenue of research in the field of plant root-microorganism interactions.
Phylogenomics of 8,839Clostridioides difficilegenomes reveals recombination-driven evolution and diversification of toxin A and B
Authors: Mansfield Michael J. , Tremblay Benjamin J-M , Zeng Ji , Wei Xin , Hodgins Harold , Worley Jay , Bry Lynn , Dong Min , Doxey Andrew C.
Published: July 2020

Clostridioides difficileis the major worldwide cause of antibiotic-associated gastrointestinal infection. A pathogenicity locus (PaLoc) encoding one or two homologous toxins, toxin A (TcdA) and toxin B (TcdB) is essential forC. difficilepathogenicity. However, toxin sequence variation poses major challenges for the development of diagnostic assays, therapeutics, and vaccines. Here, we present a comprehensive phylogenomic analysis 8,839C. difficilestrains and their toxins including 6,492 genomes that we assembled from the NCBI short read archive. A total of 5,175tcdAand 8,022tcdBgenes clustered into 7 (A1-A7) and 12 (B1-B12) distinct subtypes, which form the basis of a new method for toxin-based subtyping ofC. difficile. We developed a haplotype coloring algorithm to visualize amino acid variation across all toxin sequences, which revealed that TcdB has diversified through extensive homologous recombination throughout its entire sequence, and formed new subtypes through distinct recombination events. In contrast, TcdA varies mainly in the number of repeats in its C-terminal repetitive region, suggesting that recombination-mediated diversification of TcdB provides a selective advantage inC. difficileevolution. The application of toxin subtyping is then validated by classifying 351C. difficileclinical isolates from Brigham and Women’s Hospital in Boston, demonstrating its clinical utility. Subtyping partitions TcdB into binary functional and antigenic groups generated by intragenic recombinations, including two distinct cell-rounding phenotypes, whether recognizing frizzled proteins as receptors, and whether it can be efficiently neutralized by monoclonal antibody bezlotoxumab, the only FDA-approved therapeutic antibody. Our analysis also identifies eight universally conserved surface patches across the TcdB structure, representing ideal targets for developing broad-spectrum therapeutics. Finally, we established an open online database (DiffBase) as a central hub for collection and classification ofC. difficiletoxins, which will help clinicians decide on therapeutic strategies targeting specific toxin variants, and allow researchers to monitor the ongoing evolution and diversification ofC. difficile.
Nano-particles carried by multiple dynein motors: A Self-Regulating Nano-Machine
Authors: Fayer I. , Halbi G. , Aranovich D. , Gat S. , Bar S. , Erukhimovitch V. , Granek Rony , Bernheim-Groswasser Anne
Published: July 2020

Native cargos demonstrate efficient intra-cellular active transport. Here we investigate the motion of spherical nano-particles (NPs) grafted with flexible polymers, each ending with a nuclear localization signal peptide, thereby allowing recruitment of mammalian cytoplasmic dynein. Bead-motility assays show several unique motility features, depending on the number of NP-bound motors. NPs perform angular motion, in which the plus-end directed and right-handed motions are correlated. To simulate the system, we formulate a theoretical model that builds on single mammalian dynein properties, generalized to include motor-motor elastic and excluded-volume interactions. We find that long time trajectories exhibit both left- and right-handed helical motion, consistent with the measured angular velocity. The number of participating motors is self-regulated, thus allowing the NP to benefit from alternations between single and multiple transporting motors. Native cargos could use a similar approach to achieve both obstacle bypassing and persistent motion in the crowded cellular environment.

Significance Statement

The mechanism of active transport of native cargos, such as some viruses, is a long-standing conundrum. Their need for persistence motion towards the nucleus, while bypassing obstacles in the super-crowded intracellular milieu, requires sophisticated natural design. To fathom this machinery, we study a smartly designed nano-particle that recruitsseveraldynein motor-proteins from the cytoplasm. Motility assays and model simulations reveal long run-times, long run-lengths, and helical motion around the microtubule symmetry axis. Moreover, the nano-particles self-regulate the number of dyneins participating in the motion, which optimizes its motility properties. We suggest that alternating between single motor motility, which we believe is beneficial for obstacle bypassing, and multiple motor states, which engender persistent motion towards the nucleus, the NP achieves optimal transport efficiency.
Unravelling the developmental roadmap towards human brown adipose tissue
Authors: Carobbio Stefania , Guenantin Anne-Claire , Bahri Myriam , Samuelson Isabella , Honig Floris , Rodriguez-Fdez Sonia , Long Kathleen , Kamzolas Ioannis , Awad Sherine , Lukovic Dunja , Erceg Slaven , Bassett Andrew , Mendjan Sasha , Vallier Ludovic , Rosen Barry S. , Chiarugi Davide , Vidal-Puig Antonio
Published: July 2020

Increasing brown adipose tissue (BAT) mass and activation has been proposed as a potential therapeutic strategy to treat obesity and associated cardiometabolic complications. Given that obese and diabetic patients possess low amounts of BAT, an efficient way to expand their BAT mass would be necessary if BAT is to be useful. Currently, there is limited knowledge about how human BAT develops, differentiates, and is optimally activated. Moreover, to have access to human BAT is challenging, given its low volume and being anatomically dispersed. These constrain makes detailed mechanistic studies related to BAT development and function in humans virtually impossible. To overcome these limitations, we have developed a human-relevant new protocol for the differentiation of human pluripotent stem cells (hPSCs) into brown adipocytes (BAs). Unique to this protocol is that it is chemically-defined to recapitulate a physiological step-by-step developmental path of BAT that captures transient paraxial mesoderm and BAT progenitor states, on its way to reaching the adipocyte stage finally. These hPSC-derived BAs express brown adipocyte and thermogenic markers, are insulin sensitive, and respond to β-adrenergic stimuli. This new protocol is a scalable tool to study human BAs during development.
Clinical characteristics and primary management of patients diagnosed with prostate cancer between 2015 and 2019 at the Uganda Cancer Institute
Authors: Katongole Paul , Sande Obondo J. , Yusuf Mulumba , Joloba Moses , Reynolds Steven J , Niyonzima Nixon
Published: July 2020

Background

Prostate cancer is the second most common cancer among men in Uganda, with over 2086 incident cases in 2018. This study’s objective was to report the clinical characteristics and primary management of men diagnosed with prostate cancer at the Uganda Cancer Institute from 1stJanuary 2015 to 31stDecember 2019.

Methods

Records from all men diagnosed with Prostate cancer at the Uganda Cancer Institute from 1stJanuary 2015 to 31stDecember 2019 were reviewed. Clinical characteristics and primary treatment were recorded. Risk categorization was done using the European Society for Medical Oncology prostate cancer risk group classification.

Results

total of 874 medical records for men diagnosed with prostate cancer was retrieved. The median age was 70 years (interquartile range 64–77). In this study, 501 (57.32%) patients had localized disease. Among patients with localized disease, 2 (0.23%) were classified as low-risk, 5 (0.53%) as intermediate-risk, and 494 (56.52%) as high-risk. Three hundred seventy-three (373) patients had metastatic disease at diagnosis. Among patients with distant metastases, the most common site of metastases was bone 143 (16.36%), followed by spinal cord 54 (6.18%), abdomen 22 (2.52%), and lungs 14 (1.60%). Regarding the primary treatment options majority of the patients were on chemotherapy 384(43.94%) followed by hormonal therapy 336 (38.44%) and radiotherapy 127 (14.53%).

Conclusion

The majority of the patients diagnosed with prostate cancer at the Uganda Cancer Institute presented with advanced disease. The primary treatments were mostly chemotherapy, hormonal therapy, and radiotherapy. There is a need to improve prostate cancer screening in regional health care facilities and the communities to enhance early detection and management of prostate cancer.
Overlaid positive and negative feedback loops shape dynamical properties of PhoPQ two-component system
Authors: Rao Satyajit D , Igoshin Oleg A
Published: July 2020

Bacteria use two-component systems (TCSs) to sense environmental conditions and change gene expression to adapt to those conditions. To amplify cellular responses, many bacterial TCSs are under positive feedback control, i.e. increase their own expression when activated. InE. coli, Mg2+-sensing TCS, PhoPQ, in addition to the positive feedback includes a negative feedback via upregulation of MgrB protein that inhibits PhoQ. How interplay of these feedback loops shapes steady state and dynamical responses of PhoPQ TCS to change in Mg2+remains poorly understood. In particular, how the presence of MgrB feedback affects the robustness of PhoPQ response to overexpression of TCS is unclear. It is also unclear why the steady state response to decreasing Mg2+is biphasic, i.e. plateaus over a range of Mg2+concentrations and then increases again at growth-limiting Mg2+. In this study, we use mathematical modeling to identify potential mechanisms behind these experimentally observed dynamical properties. The results make experimentally testable predictions for the regime with response robustness and propose novel explanation of biphasic response constraining the mechanisms for modulation of PhoQ activity by Mg2+and MgrB. Finally, we show how interplay of positive and negative feedback loops affect networks steady-state sensitivity and response dynamics. In the absence of MgrB feedback, the model predicts oscillations thereby suggesting a general mechanism of oscillatory or pulsatile dynamics in autoregulated TCSs. These results help better understanding of TCS signaling and other networks with overlaid positive and negative feedback.

Author summary

Feedback loops are commonly observed in bacterial gene-regulatory networks to enable proper dynamical responses to stimuli. Positive feedback loops often amplify the response to stimulus, whereas negative feedback loops are known to speed-up the response and increase robustness. Here we demonstrate how combination of positive and negative feedback in network sensing extracellular ion concentrations affects its steady state and dynamic responses. We utilize published experimental data to calibrate mathematical models of the gene regulatory network. The resulting model quantitatively matches experimentally observed behavior and can make predictions on the mechanism of negative feedback control. Our results show the advantages of such a combination feedback loops and predict the effect of their perturbation on the steady state and dynamic responses. This study improves our understanding of how feedback loops shape dynamical properties of signaling networks.
Transmission delays and frequency detuning can regulate information flow between brain regions
Authors: Pariz Aref , Fischer Ingo , Valizadeh Alireza , Mirasso Claudio
Published: July 2020

Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure (connectome). Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role that the connection delay and the frequency detuning between different neural populations play in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations can be determined solely by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.

Author summary

Collective dynamics in brain networks is characterized by a coordinated activity of their constituent neurons that lead to brain oscillations. Many evidences highlight the role that brain oscillations play in signal transmission, the control of the effective communication between brain areas and the integration of information processed by different specialized regions. Oscillations periodically modulate the excitability of neurons and determine the response those areas receiving the signals. Based on the communication trough coherence (CTC) theory, the adjustment of the phase difference between local oscillations of connected areas can specify the timing of exchanged signals and therefore, the efficacy of the communication channels. An important factor is the delay in the transmission of signals from one region to another that affects the phase difference and timing, and consequently the impact of the signals. Despite this delay plays an essential role in CTC theory, its role has been mostly overlooked in previous studies. In this manuscript, we concentrate on the role that the connection delay and the oscillation frequency of the populations play in the signal transmission, and consequently in the effective connectivity, between two brain areas. Through extensive numerical simulations, as well as analytical results with reduced models, we show that these parameters have two essential impacts on the effective connectivity of the neural networks: First, that the populations advancing in phase to others do not necessarily play the role of the information source; and second, that the amount and direction of information transfer dependents on the oscillation frequency of the populations.
Comparison of target enrichment strategies for ancient pathogen DNA
Authors: Furtwängler Anja , Neukamm Judith , Böhme Lisa , Reiter Ella , Vollstedt Melanie , Arora Natasha , Singh Pushpendra , Cole Stewart T. , Knauf Sascha , Calvignac-Spencer Sébastien , Krause-Kyora Ben , Krause Johannes , Schuenemann Verena J. , Herbig Alexander
Published: July 2020

In ancient DNA research, the degraded nature of the samples generally results in poor yields of highly fragmented DNA, and targeted DNA enrichment is thus required to maximize research outcomes. The three commonly used methods – (1) array-based hybridization capture and in-solution capture using either (2) RNA or (3) DNA baits – have different characteristics that may influence the capture efficiency, specificity, and reproducibility. Here, we compared their performance in enriching pathogen DNA ofMycobacterium lepraeandTreponema pallidumof 11 ancient and 19 modern samples. We find that in-solution approaches are the most effective method in ancient and modern samples of both pathogens, and RNA baits usually perform better than DNA baits.

Method summary

We compared three targeted DNA enrichment strategies used in ancient DNA research for the specific enrichment of pathogen DNA regarding their efficiency, specificity, and reproducibility for ancient and modernMycobacterium lepraeandTreponema pallidumsamples. Array-based capture and in-solution capture with RNA and DNA baits were all tested in three independent replicates.
Structures of potent and convergent neutralizing antibodies bound to the SARS-CoV-2 spike unveil a unique epitope responsible for exceptional potency
Authors: Du Shuo , Cao Yunlong , Zhu Qinyu , Wang Guopeng , Du Xiaoxia , He Runsheng , Xu Hua , Zheng Yinghui , Wang Bo , Bai Yali , Ji Chenggong , Yisimayi Ayijiang , Wang Qisheng , Gao Ning , Xie X. Sunney , Su Xiao-dong , Xiao Junyu
Published: July 2020

Understanding the mechanism of neutralizing antibodies (NAbs) against SARS-CoV-2 is critical for effective vaccines and therapeutics development. We recently reported an exceptionally potent NAb, BD-368-2, and revealed the existence ofVH3-53/VH3-66convergent NAbs in COVID-19. Here we report the 3.5-Å cryo-EM structure of BD-368-2’s Fabs in complex with a mutation-induced prefusion-state-stabilized spike trimer. UnlikeVH3-53/VH3-66NAbs, BD-368-2 fully blocks ACE2 binding by occupying all three receptor-binding domains (RBDs) simultaneously, regardless of their “up” and “down” positions. BD-368-2 also triggers fusogenic-like structural rearrangements of the spike trimer, which could impede viral entry. Moreover, BD-368-2 completely avoids the common epitope ofVH3-53/VH3-66NAbs, evidenced by multiple crystal structures of their Fabs in tripartite complexes with RBD, suggesting a new way of pairing potent NAbs to prevent neutralization escape. Together, these results rationalize a unique epitope that leads to exceptional neutralization potency, and provide guidance for NAb therapeutics and vaccine designs against SARS-CoV-2.
The effects of environmental complexity on microbial community yield and structure
Authors: Pacheco Alan R. , Segrè Daniel
Published: July 2020

Environmental composition is a major, though poorly understood determinant of microbiome dynamics. Here we ask whether general principles govern how key microbial community properties, i.e. yield and diversity, scale with the number of environmental molecules. By assembling hundreds of synthetic consortia, we found that community yield remains constant as a function of environmental complexity, in agreement with additive expectations of an idealized model. However, taxonomic diversity is much lower than expected. By quantifying this deviation with a metric for epistatic interactions between environments, we uncovered simple ecological rules that govern how communities respond nonlinearly to the coupling of different nutrient sets. Our results demonstrate that environmental complexity alone is not sufficient for maintaining microbiome diversity, and provide practical guidance for designing and controlling microbial communities.
Limited genetic diversity ofblaCMY-2-containing IncI1-pST12 plasmids from Enterobacteriaceae of human and broiler chicken origin in the Netherlands
Authors: den Drijver Evert , Stohr Joep J.J.M. , Verweij Jaco J. , Verhulst Carlo , Velkers Francisca C. , Stegeman Arjan , Kluytmans-van den Bergh Marjolein F.Q. , Kluytmans Jan A.J.W. ,   i-4-1-Health Study Group
Published: July 2020

Distinguishing epidemiologically related and unrelated plasmids is essential to confirm plasmid transmission. We compared IncI1-pST12 plasmids from both human and livestock origin and explored the degree of sequence similarity between plasmids from Enterobacteriaceae with different epidemiological links. Short-read sequence data of Enterobacteriaceae cultured from humans and broilers were screened for the presence of both ablaCMY-2gene and an IncI1-pST12 replicon. Isolates were long-read sequenced on a MinION sequencer (OxfordNanopore Technologies). After plasmid reconstruction using hybrid assembly, pairwise single nucleotide polymorphisms (SNP) were determined. The plasmids were annotated, and a pan-genome was constructed to compare genes variably present between the different plasmids. NineEscherichia colisequences of broiler origin, fourEscherichia colisequences and oneSalmonella entericasequence of human origin were selected for the current analysis. A circular contig with the IncI1-pST12 replicon andblaCMY-2gene was extracted from the assembly graph of all fourteen isolates. Analysis of the IncI1-pST12 plasmids revealed a low number of SNP differences (range of 0-9 SNPs). The range of SNP differences overlapped in isolates with different epidemiological links. One-hundred and twelve from a total of 113 genes of the pan-genome were present in all plasmid constructs. NGS-analysis ofblaCMY--2-containing IncI1-pST12 plasmids isolated from Enterobacteriaceae with different epidemiological links show a high degree of sequence similarity in terms of SNP differences and the number of shared genes. Therefore, statements on the horizontal transfer of these plasmids based on genetic identity should be made with caution.
The genetic paradigms of dietary restriction fail to extend life span incep-1(gk138)mutant ofC. elegansp53 due to possible background mutations
Authors: Goyala Anita , Baruah Aiswarya , Mukhopadhyay Arnab
Published: July 2020

Dietary restriction (DR) increases life span and improves health in most model systems tested, including non-human primates. InC. elegans, as in other models, DR leads to reprogramming of metabolism, improvements in mitochondrial health, large changes in gene expression, including increase in expression of cytoprotective genes, better proteostasis etc. Understandably, multiple global transcriptional regulators like transcription factors FOXO/DAF-16, FOXA/PHA-4, HSF1/HSF-1 and NRF2/SKN-1 are important for DR longevity. Considering the wide-ranging effects of p53 on organismal biology, we asked whether theC. elegansortholog, CEP-1 is required for DR-mediated longevity assurance. We employed the widely-used TJ1 strain ofcep-1(gk138). We show thatcep-1(gk138)suppresses the life span extension of two genetic paradigms of DR, but two non-genetic modes of DR remain unaffected in this strain. We find that incep-1(gk138), two aspects of DR, increased autophagy and the up-regulation of expression of cytoprotective xenobiotic detoxification program (cXDP) genes are dampened. Importantly, we find that background mutation(s) in the strain may be the actual cause for the phenotypic differences that we observed andcep-1may not be directly involved in genetic DR-mediated longevity assurance in worms. Identifying these mutation(s) may reveal a novel regulator of longevity required specifically by genetic modes of DR.
Error propagation in constraint-based modeling of Chinese hamster ovary cells
Authors: Széliová Diana , Iurashev Dmytro , Ruckerbauer David E , Koellensperger Gunda , Borth Nicole , Melcher Michael , Zanghellini Jürgen
Published: July 2020

Background

Chinese hamster ovary (CHO) cells are the most popular mammalian cell factories for the production of glycosylated biopharmaceuticals. To further increase titer and productivity and ensure product quality, rational systems-level engineering strategies based on constraint-based metabolic modeling, such as flux balance analysis (FBA), have gained strong interest. However, the quality of FBA predictions depends on the accuracy of the experimental input data, especially on the exchange rates of extracellular metabolites. Yet it is not standard practice to devote sufficient attention to the accurate determination of these rates.

Results

In this work we investigated to what degree the sampling frequency during a batch culture and the measurement errors of metabolite concentrations influence the accuracy of the calculated exchange rates and further, how this error then propagates into FBA predictions of growth rates. We determined that accurate measurements of essential amino acids with low uptake rates are crucial for the accuracy of FBA predictions, followed by a sufficient number of analysed time points.

Conclusions

We observed that the measured difference in growth rates of two cell lines can only be reliably predicted when both high measurement accuracy and sampling frequency are ensured.
Multiplexed End-point Microfluidic Chemotaxis Assay using Centrifugal Alignment
Authors: Satti Sampath , Deng Pan , Matthews Kerryn , Duffy Simon P. , Ma Hongshen
Published: July 2020

A fundamental challenge to multiplexing microfluidic chemotaxis assays at scale is the requirement for time-lapse imaging to continuously track migrating cells. Drug testing and drug screening applications require the ability to perform hundreds of experiments in parallel, which is not feasible for assays that require continuous imaging. To address this limitation, end-point chemotaxis assays have been developed using fluid flow to align cells in traps or sieves prior to cell migration. However, these methods require precisely controlled fluid flow to transport cells to the correct location without undesirable mechanical stress, which introduce significant set up time and design complexity. Here, we describe a microfluidic device that eliminates the need for precise flow control by using centrifugation to align cells at a common starting point. A chemoattractant gradient is then formed using passive diffusion prior to chemotaxis in an incubated environment. This approach provides a simple and scalable approach to multiplexed chemotaxis assays. Centrifugal alignment is also insensitive to cell geometry, enabling this approach to be compatible with primary cell samples that are often heterogeneous. We demonstrate the capability of this approach by assessing chemotaxis of primary neutrophils in response to an fMLP (N-formyl-met-leu-phe) gradient. Our results show that cell alignment by centrifugation offers a potential avenue to develop scalable end-point multiplexed microfluidic chemotaxis assays.
Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data
Authors: Fang Xinying , Liu Yu , Ren Zhijie , Du Yuheng , Huang Qianhui , Garmire Lana X.
Published: July 2020

Previously we developedLilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, here we report the next version ofLilikoias a significant upgrade. The newLilikoiv2.0 R package has implemented a deep-learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-PH model and the deep-learning based Cox-nnet model. Additionally,Lilikoiv2.0 supports data preprocessing, exploratory analysis, pathway visualization and metabolite-pathway regression. In summary,Lilikoiv2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.