Biology subjects

Sarkar, D.

Publications and source records attributed to Sarkar, D..

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bioRxiv2021

Design, Fabrication, and Theoretical Investigation of a Cost-Effective Laser Printing Based Colorimetric Paper Sensor for Non-Invasive Glucose and Ketone Detection

Diabetes, a chronic condition, is one of the prevalent afflictions of the 21st century, and if left unchecked, this ailment could lead to severe life-threatening complications. A widely accepted methodology for monitoring diabetes is the estimation of the glucose and ketone contents in the body-fluids, viz. blood, urine, etc. Additionally, certain conditions such as starvation, and following a protein rich diet (e.g., keto-diet) could also lead to significant changes in the ketone content, thereby resulting in false-positive diagnosis. Hence, a precise, portable, and on-demand procedure for the rapid and combined estimation of glucose and ketone in the bodily-fluids is of utmost importance. To that end, paper-based analytical devices (PADs) are promising tools, owing to their multitudinous advantages, and compatibility with biofluids. Although, numerous researchers have contributed substantially in the fundamental investigation, design, and fabrication of PADs for various applications, a combined platform capable of rapid, accurate and on-demand glucose and ketone detection, that is easy to fabricate, is still relatively unexplored. Moreover, the flow dynamics of an analyte, in combination with enzyme-catalysed (for glucose) and uncatalyzed reactions (for ketone), within a porous paper matrix is also vaguely understood. Herein, we present a facile laser-printing based fabrication of colorimetric sensors on a filter paper, for rapid, and non-invasive estimation of glucose and ketone contents in urine. The urine sample, upon being deposited in a particular expanse, is wicked through the paper matrix, and reacts with specific reagents in the designated zone(s), giving rise to a final color, concomitant with the glucose or ketone content in the sample. The device design enables the liquid to be wicked into the porous matrix in a way that would concentrate the colored product in a dedicated detection zone, thereby augmenting the feasibility for accurate colorimetric detection. Furthermore, we present for the first time, a detailed dynamic model of the flow-field in a variable cross-section paper device using the Richards equation, while also considering the species transport and reaction kinetics within the porous media. The results of the numerical simulation agree well with those observed experimentally, thereby validating the present model. Finally, we also developed a web and desktop-based application that would enable the user to upload the images of the colored zones to provide an accurate estimate of the glucose and ketone content in the sample. We believe that our model, in combination with the proposed fabrication methodology, and the in-house developed app., would enable rapid and reliable fabrication of PADs for various fundamental investigations, and applications pertaining to affordable health-care monitoring. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=59 SRC="FIGDIR/small/461386v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@49e824org.highwire.dtl.DTLVardef@27a512org.highwire.dtl.DTLVardef@c65242org.highwire.dtl.DTLVardef@429896_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2021

Quantifying the propagation of parametric uncertainty on flux balance analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g/mmol, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.

systems biology↗

bioRxiv2021

The Structure of ChAdOx1/AZD-1222 Reveals Interactions with CAR and PF4 with Implications for Vaccine-induced Immune Thrombotic Thrombocytopenia

Adenovirus derived vectors, based on chimpanzee adenovirus Y25 (ChAdOx1) and human adenovirus type 26 are proving critical in combatting the 2019 SARS-CoV-2 pandemic. Following emergency use authorisation, scale up in vaccine administration has inevitably revealed vaccine related adverse effects; too rare to observe even in large Phase-III clinical trials. These include vaccine-induced thrombotic thrombocytopenia (VITT), an ultra-rare adverse event in which patients develop life-threatening blood clots 5-24 days following vaccination. To investigate vector-host interactions of ChAdOx1 underpinning VITT we solved the structure of the ChAdOx1 capsid by CryoEM, and the structure of the primary receptor tropism determining fiber-knob protein by crystallography. These structural insights have enabled us to unravel key protein interactions involved in ChAdOx1 cell entry and a possible means by which it may generate misplaced immunity to platelet factor 4 (PF4), a protein involved in coagulation. We use in vitro cell binding assays to show that the fiber-knob protein uses coxsackie and adenovirus receptor (CAR) as a high affinity binding partner, while it does not form a stable interface with CD46. Computational simulations identified a putative mechanism by which the ChAdOx1 capsid interacts with PF4 by binding in the spaces between hexon proteins, with downstream implications for the causes of VITT. SummaryWe present the structure of the ChAdOx1 viral vector, derived from chimpanzee adenovirus Y25 at 4.2[A] resolution1. ChAdOx1 is in global use in the AstraZeneca vaccine, ChAdOx1 nCoV-19/AZD-1222, to combat the SARS-CoV-2 coronavirus pandemic. Recently observed, rare, adverse events make detailed mechanistic understanding of this vector key to informing proper treatment of affected patients and the development of safer viral vectors. Here, we determine a primary mechanism ChAdOx1 uses to attach to cells is coxsackie and adenovirus receptor (CAR), a protein which is identical in humans and chimpanzees. We demonstrate the vector does not form a stable CD46 interaction, a common species B adenovirus receptor, via its primary attachment protein. Further, we reveal the surface of the ChAdOx1 viral capsid has a strong electronegative potential. Molecular simulations suggest this charge, together with shape complementarity, are a mechanism by which an oppositely charged protein, platelet factor 4 (PF4) may bind the vector surface. PF4 is a key protein involved in the formation of blood clots2, and the target of auto-antibodies in heparin-induced immune thrombotic thrombocytopenia (HITT)3, an adverse reaction to heparin therapy which presents similarly to vaccine-induced immune thrombotic thrombocytopenia (VITT), a rare complication of ChAdOx1 nCoV-19 vaccination4-6. We propose a mechanism in which the ChAdOx1-PF4 complex may stimulate the production of antibodies against PF4, leading to delayed blood clot formation, as observed in VITT.

molecular biology↗

bioRxiv2021

Dissecting the Metabolic Reprogramming of Maize Root under Nitrogen Limiting Stress Condition

The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen-stress condition. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants was used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N-) and nitrogen-deficient (N-) conditions. Model-predicted result achieved 70% accuracy comparing to the experimental direction change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (e.g., L-methionine, L-asparagine, L-lysine, cholesterol, and L-pipecolate) playing critical regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidyl-choline and phosphatidyl-glycerol metabolites which even though not coupled with biomass production played a key role in the increased biomass production under N-. Overall, the omics-integrated-GSM provides a promising tool to facilitate stress-condition analysis for maize root and ultimately engineer better stress-tolerant maize genotypes. SummaryO_LIThe growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. C_LIO_LIA genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen-stress condition. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. C_LIO_LITranscriptomics data derived from the roots of hydroponically grown maize plants was used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) conditions. Model-predicted result achieved 70% accuracy comparing to the experimental direction change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (e.g., L-methionine, L-asparagine, L-lysine, cholesterol, and L-pipecolate) playing critical regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidyl-choline and phosphatidyl-glycerol metabolites which even though not coupled with biomass production played a key role in the increased biomass production under N-. C_LIO_LIOverall, the omics-integrated-GSM provides a promising tool to facilitate stress-condition analysis for maize root and ultimately engineer better stress-tolerant maize genotypes. C_LI

systems biology↗

bioRxiv2021

Single Cell Transcriptomic Landscape of Diabetic Foot Ulcers

To understand the diabetic wound healing microenvironment, we profiled 174,962 single cells from foot, forearm, and PBMCs using single-cell RNA sequencing (scRNASeq) approach. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 genes and M1 macrophage polarization in the DFU patients with healing wounds. Further, scRNASeq of spatially separated samples from same patient and spatial transcriptomics (ST) revealed preferential localization of these healing associated fibroblasts toward deep wound/ulcer bed as compared to wound edge or non-wounded skin. ST also validated our findings of higher enrichment of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

genomics↗

bioRxiv2020

"Tracking the Rain Bird": Modeling the monthly distribution of Pied cuckoo in India

Pied cuckoo Clamator jacobinus (Boddart, 1783) is a migratory, brood-parasitic bird found in the African and Indian Subcontinent. Although the southern Indian population is presumably resident, the North Indian Population migrates from Africa to India during the summer. The arrival of the bird is linked to the onset of monsoon in India from scientific literature to folklore. It is known to make its appearance in central and northern India in the last week of May or early June, indicating the imminent arrival of the monsoon with its unmistakably loud metallic calls. There have been few attempts to compile relevant information on the species migration in the early 1900s and citizen science approach by Bird-count India; little information is available on how environmental factors might be affecting its migration. Here, we have used Maximum Entropy modeling to identify the monthly and seasonal distribution patterns and major bioclimatic factors that might be influencing the distribution of the species in India. We have used E-Bird citizen science platform data, seven bioclimatic variables, and monthly NDVI of respective months for building the models. The predicted output shows the species presence throughout the year in southern India. In contrast, in northern India, the distribution is dynamic, peaking in summers in the Month of May-June and no presence in winter. The influence of bioclimatic variables used in SDM varied monthly; Water vapor pressure was the primary contributing variable in the months prior to species arrival. In July, it was NDVI (Higher NDVI suggests abundance of food resources for the species). In August-September, Windspeed and water vapor pressure (Factors might be responsible for the departure of the species) have contributed highest. Our approach provides a more concise understanding of Pied cuckoos monthly distributions throughout India, which helps understand the complex seasonal shifts in the distribution of such migratory birds.

ecology↗

bioRxiv2020

Assessing tiger corridor functionality with landscape genetics and modelling across Terai-Arc Landscape, India

India led the global tiger conservation initiatives since last decade and has doubled its wild tiger population to 2967 (2603-3346). The survival of these growing populations residing inside the continuously shrinking habitats is a major concern, which can only be tackled through focused landscape-scale conservation planning across five major extant Indian tiger landscapes. The Terai-Arc landscape (TAL) is one of the global priority tiger conservation landscapes holding 22% of the countrys wild tigers. We used intensive field-sampling, genetic analyses and GIS modelling to investigate tiger population structure, source-sink dynamics and functionality of the existing corridors across TAL. Genetic analyses with 219 tigers revealed three low, but sigficantly differentiated tiger subpopulations. Overall, we identified Seven source and 10 sink areas in TAL through genetic migrant and gene flow analyses. GIS modelling identified total 19 (10 high, three medium and six low conductance) corridors in this landscape, with 10 being critical to maintain landscape connectivity. We suggest urgent management attention towards 2707 sq. km. non-protected habitat, mitigation measures associated with ongoing linear infrastructure developments and transboundary coordination with Nepal to ensure habitat and genetic connectivity and long-term sustainability of tigers in this globally important landscape.

ecology↗

bioRxiv2020

Expansion Revealing: Decrowding Proteins to Unmask Invisible Brain Nanostructures

Cells and tissues are made out of nanoscale building blocks, such as proteins, organized in crowded nanostructures. We show that many biomolecules, and the nanostructures in which they are embedded, may be invisible to prior imaging techniques, due to the inaccessibility of labels (e.g., antibodies) to biomolecules embedded within such crowded structures. We developed a technology, expansion revealing (ExR), which isotropically decrowds proteins from each other, to enable their labeling. We use ExR to discover the alignment of presynaptic calcium channels with postsynaptic machinery in intact brain circuits, as well as the existence of periodic amyloid nanoclusters containing ion channel proteins in Alzheimers model mice. Thus, ExR reveals nanostructures within complex biological specimens that were not previously visualizable, and may find broad use in biology. One Sentence SummaryDe-crowding biomolecules with Expansion Revealing unmasks fundamentally new nanostructures in intact brain tissue, which remained invisible otherwise.

bioengineering↗

bioRxiv2020

Altered Mediator dynamics during heat shock in budding yeast

The Mediator complex is central to transcription by RNA polymerase II (Pol II) in eukaryotes. In yeast, Mediator is recruited by activators via its tail module and then facilitates assembly of the pre-initiation complex (PIC), including Pol II, setting the stage for productive transcription. Mediator occupies proximal promoter regions only transiently prior to Pol II escape; interruption of the transcription cycle by inactivation or depletion of Kin28 inhibits Pol II escape and stabilizes Mediator occupancy at promoters. However, whether Mediator occupancy and dynamics differ for gene cohorts induced by stress or alternative growth conditions has not been examined on a genome-wide scale. Here we investigate Mediator occupancy following heat shock or CdCl2 induction, with or without depletion of Kin28. We find that Pol II occupancy exhibits similar dependence on Mediator under normal and heat shock conditions; however, Mediator occupancy does not increase upon Kin28 depletion at most genes active during heat shock, indicating altered dynamics. Furthermore, Mediator occupancy persists at genes repressed by heat shock or CdCl2 induction and exhibits peaks upstream of the proximal promoter whether or not Kin28 is depleted, suggesting that Mediator is recruited by activators but is unable to engage PIC components at these repressed targets. Finally, we show a reduced dependence on PIC components for Mediator occupancy at promoters after heat shock, further supporting an altered dynamics or stronger engagement with activators under these conditions.

molecular biology↗

bioRxiv2020

Specific OCRL1 patient mutations differentially impact Lowe Syndrome cellular phenotypes

Lowe Syndrome (LS) is a lethal genetic disorder caused by mutations in the OCRL1 gene which encodes the lipid 5 phosphatase Ocrl1. Patients exhibit a characteristic triad of symptoms including eyes, brain and kidneys abnormalities with renal failure as the most common cause of premature death. Over 200 OCRL1 mutations have been identified in LS, but their specific impact on cellular processes is unknown. Despite observations of heterogeneity in patient symptom severity, there is little understanding of the correlation between genotype and its impact on phenotype. Here, we show that different mutations had diverse effects on protein localization and on triggering LS cellular phenotypes. In addition, some mutations affecting specific domains imparted unique characteristics to the resulting mutated protein. We also propose that certain mutations conformationally affect the 5-phosphatase domain of the protein, resulting in loss of enzymatic activity and causing common and specific phenotypes. This study is the first to show the differential effect of patient 5-phosphatase mutations on cellular phenotypes and introduces a conformational disease component in LS. This work provides a framework that can help stratify patients as well as to produce a more accurate prognosis depending on the nature and location of the mutation within the OCRL1 gene.

cell biology↗

bioRxiv2020

Mind reading of the proteins: Deep-learning to forecast molecular dynamics

Molecular dynamics (MD) simulations have emerged to become the back-bone of todays computational biophysics. Simulation tools such as, NAMD, AMBER and GROMACS have accumulated more than 100,000 users. Despite this remarkable success, now also bolstered by compatibility with graphics processor units (GPUs) and exascale computers, even the most scalable simulations cannot access biologically relevant timescales - the number of numerical integration steps necessary for solving differential equations in a million-to-billion-dimensional space is computationally in-tractable. Recent advancements in Deep Learning has made it such that patterns can be found in high dimensional data. In addition, Deep Learning have also been used for simulating physical dynamics. Here, we utilize LSTMs in order to predict future molecular dynamics from current and previous timesteps, and examine how this physics-guided learning can benefit researchers in computational biophysics. In particular, we test fully connected Feed-forward Neural Networks, Recurrent Neural Networks with LSTM / GRU memory cells with TensorFlow and PyTorch frame-works trained on data from NAMD simulations to predict conformational transitions on two different biological systems. We find that non-equilibrium MD is easier to train and performance improves under the assumption that each atom is independent of all other atoms in the system. Our study represents a case study for high-dimensional data that switches stochastically between fast and slow regimes. Applications of resolving these sets will allow real-world applications in the interpretation of data from Atomic Force Microscopy experiments.

biophysics↗

bioRxiv2020

Data-guided Multi-Map variables for ensemble refinement of molecular movies

Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. Here, the 3-dimensional electron density of a protein, as it would be determined by cryo-EM or X-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work uses the recently developed Multi-Map methodology to monitor concerted movements within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along reaction coordinates derived from electron densities can serve to induce conformational transitions between known intermediates. The simulated pathways appear reversible, with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein DataBank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution (~5 [A]) density information coupled to comparatively large structural transitions. Practical considerations for generating transitions with multiple intermediate atomic density distributions are also discussed.

biophysics↗

bioRxiv2020

Understanding photothermal interactions will help expand production range and increase genetic diversity of lentil (Lens culinaris Medik.)

O_LILentil (Lens culinaris Medik.) is cultivated under a wide range of environmental conditions, which led to diverse phenological adaptations and resulted in a decrease in genetic variability within breeding programs due to reluctance in using genotypes from other environments. C_LIO_LIWe phenotyped 324 genotypes across nine locations over three years to assess their phenological response to the environment of major lentil production regions and to predict days from sowing to flowering (DTF) using a photothermal model. C_LIO_LIDTF was highly influenced by the environment and is sufficient to explain adaptation. We were able to predict DTF reliably in most environments using a simple photothermal model, however, in certain site-years, results suggest there may be additional environmental factors at play. Hierarchical clustering of principal components revealed the presence of eight groups based on the responses of DTF to contrasting environments. These groups are associated with the coefficients of the photothermal model and revealed differences in temperature and photoperiod sensitivity. C_LIO_LIExpanding genetic diversity is critical to the success of a breeding program; understanding adaptation will facilitate the use of exotic germplasm. Future climate change scenarios will result in increase temperature and/or shifts in production areas, we can use the photothermal model to identify genotypes most likely to succeed in these new environments. C_LI

plant biology↗

bioRxiv2020

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

This paper describes outcomes of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 [A]) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several novel metrics were introduced. The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual laboratory experiments and holdings of structure data archives such as the Protein Data Bank. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived from these benchmark maps by 13 participating teams, representing both widely used and novel modeling approaches. We also evaluate the pros and cons of the commonly used metrics to assess model quality and recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed density in the cryo-EM map.

biochemistry↗

bioRxiv2019

Metabolic switching of Mycobacterium tuberculosis during hypoxia is controlled by the virulence regulator PhoP

Mycobacterium tuberculosis (Mtb) retains the unique ability to establish an asymptomatic latent infection. A fundamental question in mycobacterial physiology is to understand the mechanisms involved in hypoxic stress, a critical player in persistence. Here, we show that the virulence regulator PhoP responds to hypoxia, the dormancy signal and effectively integrates hypoxia with nitrogen metabolism. We also provide evidence to demonstrate that both under nitrogen limiting conditions and during hypoxia, phoP locus controls key genes involved in nitrogen metabolism. Consistently, under hypoxia{Delta} phoP shows growth attenuation even with surplus nitrogen, the alternate electron acceptor, and complementation of the mutant restores bacterial growth. Together, our observations provide new biological insights into the role of PhoP in integrating nitrogen metabolism with hypoxia by the assistance of the hypoxia regulator DosR. The results have significant implications on the mechanism of intracellular survival and growth of the tubercle bacilli under a hypoxic environment within the phagosome. ImportanceMtb retains the unique ability to establish an asymptomatic latent infection. To understand the mechanisms involved in hypoxic stress which plays a critical role in persistence, we show that the virulence regulator PhoP responds to hypoxia, the dormancy signal. In keeping with this, phoP was shown to play a major role in Mtb growth under hypoxia even in presence of surplus nitrogen, the alternate electron acceptor. Our results showing regulation of hypoxia-responsive genes provide new biological insights into role of the virulence regulator in metabolic switching by sensing hypoxia and integrating nitrogen metabolism with hypoxia by the assistance of the hypoxia regulator DosR.

microbiology↗

bioRxiv2019

CryoFold: Ab-initio structure determination from electron density maps using molecular dynamics

Cryo-EM is a powerful method for determining protein structures. But it requires computational assistance. Physics-based computations have the power to give low-free-energy structures and ensembles of populations, but have been computationally limited to only small soluble proteins. Here, we introduce CryoFold. By integrating data of varying sparsity from electron density maps of 3-5 [A] resolution with coarse-grained physical knowledge of secondary and tertiary interactions, CryoFold determines ensembles of protein structures directly from sequence. We give six examples showing its broad capabilities, over proteins ranging from 72 to 2000 residues, including membrane and multi-domain proteins, and including results from two EMDB competitions. The ensembles CryoFold predicts starting from the density data of a single known protein conformation encompass multiple low-energy conformations, all of which are experimentally validated and biologically relevant.

biophysics↗

bioRxiv2019

Kin28 depletion increases association of TFIID subunits Taf1 and Taf4 with promoters in Saccharomyces cerevisiae

In eukaryotes, transcription of mRNA-encoding genes by RNA polymerase II (Pol II) begins with assembly of the pre-initiation complex (PIC), comprising Pol II and the general transcription factors. Although the pathway of PIC assembly is well established, the mechanism of assembly and the dynamics of PIC components are not fully understood. For example, only recently has it been shown in yeast that the Mediator complex, which assists in pre-initiation complex formation at promoters of essentially all genes transcribed by Pol II, normally occupies promoters only transiently. This was inferred from studies showing that inhibiting Pol II promoter escape by depleting or inactivating Kin28 resulted in increased promoter occupancy by Mediator, as measured by chromatin immunoprecipitation (ChIP). Here we show that two subunits of TFIID, Taf1 and Taf4, similarly show increased occupancy as measured by ChIP upon depletion or inactivation of Kin28. In contrast, TBP occupancy is unaffected by depletion of Kin28, thus revealing an uncoupling of Taf and TBP occupancy during the transcription cycle. Increased Taf1 occupancy upon Kin28 depletion is suppressed by depletion of TBP, while depletion of TBP in the presence of Kin28 has little effect on Taf1 occupancy. Taf1 occupancy relative to TBP is higher at TFIID-dominated promoters and promoters having consensus TATA elements than at SAGA-dominated promoters and promoters lacking consensus TATA elements, consistent with prior work, and the increase in Taf occupancy upon depletion of Kin28 is more pronounced at TFIID-dominated promoters. Our results support the suggestion, based on recent structural studies, that TFIID may not remain bound to gene promoters through the transcription initiation cycle.\n\nAuthor SummaryTranscription of mRNA-encoding genes by RNA polymerase II (Pol II) begins when the pre-initiation complex, a large complex comprising Pol II and several general transcription factors, including the TATA-binding protein (TBP)-containing TFIID complex, assembles at gene promoters. Although the major steps in the pathway of PIC assembly have been identified, the mechanism of assembly in vivo and the dynamics of PIC components are not fully understood. In this work we have used a yeast strain that is engineered to allow inhibition of promoter escape by Pol II by administration of a chemical, in order to \"freeze\" the assembled PIC and thus determine whether this condition increases the promoter occupancy of TBP and two TBP-associated factors (Tafs) that are components of TFIID. This approach was used recently to demonstrate that the Mediator complex, which facilitates PIC assembly, normally binds only transiently to gene promoters. We find that Tafs, like Mediator, show increased occupancy when Pol II promoter escape is inhibited, whereas TBP binding is constant. These results imply that binding of TBP and Tafs is uncoupled during the transcription cycle, and that Taf occupancy is at least partially interrupted upon Pol II promoter escape.

genetics↗

bioRxiv2018

Discovery of volatile biomarkers of Parkinsons disease from sebum

Parkinsons disease (PD) is a progressive, neurodegenerative disease that presents with significant motor symptoms, for which there is no diagnostic test (1-3). We have serendipitously identified a hyperosmic individual, a Super Smeller that can detect PD by odor alone, and our early pilot studies have indicated that the odor was present in the sebum from the skin of PD subjects(4). Here, we have employed an unbiased approach to investigate the volatile metabolites of sebum samples obtained non-invasively from the upper back of 64 participants in total (21 controls and 43 PD subjects). Our results, validated by an independent cohort, identified a distinct volatiles-associated signature of PD, including altered levels of perillic aldehyde and eicosane, the smell of which was then described as being highly similar to the scent of PD by our Super Smeller.\n\n1 sentence summaryMetabolomics identifies volatile odorous compounds from patient sebum that associate with the smell of Parkinsons.

neuroscience↗