bluuebunny/crossref_metadata_2025 huggingface dataset:
An Inter-Species Translation Model Implicates Integrin Signaling in Infliximab-Resistant Colonic Crohn’s Disease
Authors: Brubaker Douglas. K. , Kumar Manu. P. , Vega Paige. N. , Southard-Smith Austin. N. , Simmons Alan. J. , Scoville Elizabeth. A. , Coburn Lori. A. , Wilson Keith. T. , Lau Ken. S. , Lauffenburger Douglas. A.
Published: September 2019

Anti-TNF therapy resistance is a major clinical challenge in Crohn’s Disease (CD), partly due to insufficient understanding of disease-site, protein-level mechanisms of CD and anti-TNF treatment resistance. Although some proteomics data from CD mouse models exists, data type and phenotype discrepancies contribute to confounding attempts to translate between preclinical animal models of disease and human clinical cohorts. To meet this important challenge, we develop and demonstrate here an approach called Translatable Components Regression (TransComp-R) to overcome inter-species and trans-omic discrepancies between CD mouse models and human subjects. TransComp-R combines CD mouse model proteomic data with patient pre-treatment transcriptomic data to identify molecular features discernable in the mouse data predictive of patient response to anti-TNF therapy. Interrogating the TransComp-R models predominantly revealed upregulated integrin pathway signaling via collagen-binding integrin ITGA1 in anti-TNF resistant colonic CD (cCD) patients. Toward validation, we performed single-cell RNA sequencing on biopsies from a cCD patient and analyzed publicly available immune cell proteomics data to characterize the immune and intestinal cell types contributing to anti-TNF resistance. We found that ITGA1 is indeed expressed in colonic T-cell populations and that interactions between collagen-binding integrins on T-cells and colonic cell types expressing secreted collagens are associated with anti-TNF therapy resistance. Biologically, TransComp-R linked previously disparate observations about collagen and ITGA1 signaling to a potential therapeutic avenue for overcoming anti-TNF therapy resistance in cCD. Methodologically, TransComp-R provides a flexible, generalizable framework for addressing inter-species, inter-omic, and inter-phenotypic discrepancies between animal models and patients to deliver translationally relevant biological insights.

One Sentence Summary

Brubaker et al. implicate dysregulated collagen-binding integrin signaling in resistance to anti-TNF therapy in Crohn’s Disease by developing a mouse-proteomic to human-transcriptomic translation model and confirm the associated inter-cellular signaling network using single-cell RNA sequencing.
Field application of the geometric framework reveals a multistep strategy of nutrient regulation in a leaf-miner
Authors: Body Mélanie J.A. , Behmer Spencer T. , Pelisson Pierre-François , Casas Jérôme , Giron David
Published: September 2019

Animals have evolved a vast array of behavioral and physiological strategies that allow them to achieve a nutritionally balanced diet. Plants as food for herbivores are often considered suboptimal, but phytophagous insects can employ pre- and post-ingestive mechanisms and/or symbiotic associations to help overcome food nutritional imbalances. This is particularly crucial for permanent multivoltine leaf-miner insects such as the caterpillarPhyllonorycter blancardellawhich completes development within a restricted area of a single leaf and use deciduous leaves to fuel growth and reproduction even under senescing autumnal conditions. Using the geometric framework for nutrition under natural field conditions, we show that this insect has multiple strategies to deal with inadequate food supply from the plant. First, larvae manipulate the protein-sugar content of both normal, photosynthetically active, and senescing, photosynthetically inactive, leaf tissues. Control of nutritional homeostasis of mined tissues is however higher for late instars, which differ from younger larval instars in their feeding mode (fluid-vs.tissue-feeder). Second, slight differences in the protein-sugar environment remain between mined tissues on green and yellow leaves despite this manipulation of the leaf physiology. This insect uses post-ingestive mechanisms to achieve similar body protein, sugar and lipid composition. This study demonstrates, for the first time under natural conditions, the ability of an insect herbivore to practice a combination of pre- and post-ingestive compensatory mechanisms to attain similar growth and metabolic outcomes in fundamentally different nutritional environments. Additionally, a comparison of larval nutritional requirements of 117 species from various insect groups further reinforces the hypothesis of a close association betweenP. blancardellaand endosymbiotic bacteria for nutritional purposes.
Environmentally sensitive hotspots in the methylome of the early human embryo
Authors: Silver Matt J. , Saffari Ayden , Kessler Noah J. , Chandak Giriraj R. , Fall Caroline H.D. , Issarapu Prachand , Dedaniya Akshay , Betts Modupeh , Moore Sophie E. , Routledge Michael N. , Herceg Zdenko , Cuenin Cyrille , Derakhshan Maria , James Philip T. , Monk David , Prentice Andrew M.
Published: September 2019

In humans, DNA methylation marks inherited from gametes are largely erased following fertilisation, prior to construction of the embryonic methylome. Exploiting a natural experiment of seasonal variation including changes in diet and nutritional status in rural Gambia, we analysed two independent child cohorts and identified 259 CpGs showing consistent associations between season of conception (SoC) and DNA methylation. SoC effects were most apparent in early infancy, with evidence of attenuation by mid-childhood. SoC-associated CpGs were enriched for metastable epialleles, parent-of-origin specific methylation and germline DMRs, supporting a periconceptional environmental influence. Many SoC-sensitive CpGs overlapped enhancers or sites of active transcription in H1 ESCs and fetal tissues. Half were influenced but not determined by measured genetic variants that were independent of SoC. Environmental ‘hotspots’ providing a record of environmental influence at periconception constitute a valuable resource for investigating epigenetic mechanisms linking early exposures to lifelong health and disease.
Reconstructing an epigenetic landscape using a genetic ‘pulling’ approach
Authors: Assaf Michael , Be’er Shay , Roberts Elijah
Published: September 2019

Cells use genetic switches to shift between alternate stable gene expression states, e.g., to adapt to new environments or to follow a developmental pathway. Conceptually, these stable phenotypes can be considered as attractive states on an epigenetic landscape with phenotypic changes being transitions between states. Measuring these transitions is challenging because they are both very rare in the absence of appropriate signals and very fast. As such, it has proven difficult to experimentally map the epigenetic landscapes that are widely believed to underly developmental networks. Here, we introduce a new nonequilibrium perturbation method to help reconstruct a regulatory network’s epigenetic landscape. We derive the mathematical theory needed and then use the method on simulated data to reconstruct the landscapes. Our results show that with a relatively small number of perturbation experiments it is possible to recover an accurate representation of the true epigenetic landscape. We propose that our theory provides a general method by which epigenetic landscapes can be studied. Finally, our theory suggests that the total perturbation impulse required to induce a switch between metastable states is a fundamental quantity in developmental dynamics.
Comparative evolutionary analysis and prediction of deleterious mutation patterns between sorghum and maize
Authors: Lozano Roberto , Gazave Elodie , dos Santos Jhonathan P.R. , Stetter Markus , Valluru Ravi , Bandillo Nonoy , Fernandes Samuel B. , Brown Patrick J. , Shakoor Nadia , Mockler Todd C. , Ross-Ibarra Jeffrey , Buckler Edward S. , Gore Michael A.
Published: September 2019

Sorghum and maize share a close evolutionary history that can be explored through comparative genomics. To perform a large-scale comparison of the genomic variation between these two species, we analyzed 13 million variants identified from whole genome resequencing of 468 sorghum lines together with 25 million variants previously identified in 1,218 maize lines. Deleterious mutations in both species were prevalent in pericentromeric regions, enriched in non-syntenic genes, and present at low allele frequencies. A comparison of deleterious burden between sorghum and maize revealed that sorghum, in contrast to maize, departed from the “domestication cost” hypothesis that predicts a higher deleterious burden among domesticates compared to wild lines. Additionally, sorghum and maize population genetic summary statistics were used to predict a gene deleterious index with an accuracy higher than 0.5. This research represents a key step towards understanding the evolutionary dynamics of deleterious variants in sorghum and provides a comparative genomics framework to start prioritizing them for removal through genome editing and breeding.
Strain-level identification of bacterial tomato pathogens directly from metagenomic sequences
Authors: Mechan Llontop Marco E. , Sharma Parul , Flores Marcela Aguilera , Yang Shu , Pollock Jill , Tian Long , Huang Chenjie , Rideout Steve , Heath Lenwood S. , Li Song , Vinatzer Boris A.
Published: September 2019

Routine strain-level identification of plant pathogens directly from symptomatic tissue could significantly improve plant disease control and prevention. Here we tested the Oxford Nanopore Technologies (ONT) MinION™ sequencer for metagenomic sequencing of tomato plants either artificially inoculated with a known strain of the bacterial speck pathogenPseudomonas syringaepv.tomato(Pto), or collected in the field and showing bacterial spot symptoms caused by either one of fourXanthomonasspecies. After species-level identification using ONT’s WIMP software and the third party tools Sourmash and MetaMaps, we used Sourmash and MetaMaps with a custom database of representative genomes of bacterial tomato pathogens to attempt strain-level identification. In parallel, each metagenome was assembled and the longest contigs were used as query with the genome-based microbial identification Web service LINbase. Both the read-based and assembly-based approaches correctly identifiedPtostrain T1 in the artificially inoculated samples. The pathogen strain in most field samples was identified as a member ofXanthomonas perforansgroup 2. This result was confirmed by whole genome sequencing of colonies isolated from one of the samples. Although in our case, metagenome-based pathogen identification at the strain-level was achieved, caution still needs to be exerted when interpreting strain-level results because of the challenges inherent to assigning reads to specific strains and the error rate of nanopore sequencing.
Metagenomics and stable isotope probing offer insights into metabolism of polycyclic aromatic hydrocarbons degraders in chronically polluted seawater
Authors: Sieradzki Ella T. , Morando Michael , Fuhrman Jed A.
Published: September 2019

Bacterial biodegradation is a significant contributor to remineralization of polycyclic aromatic hydrocarbons (PAHs): toxic and recalcitrant components of crude oil as well as byproducts of partial combustion chronically introduced into seawater via atmospheric deposition. The Deepwater Horizon oil spill demonstrated the speed at which a seed PAH-degrading community maintained by low chronic inputs can respond to an acute pollution. We investigated the diversity and functional potential of a similar seed community in the Port of Los Angeles, a chronically polluted site, using stable isotope probing with naphthalene, deep-sequenced metagenomes and carbon incorporation rate measurements at the port and in two sites further into the San Pedro Channel. We show a switch in the composition of the PAH degrading community from diverse early-responding generalists to late-blooming specialized degraders. This switch demonstrates the ability of the local seed community of degraders at the Port of LA to incorporate carbon from PAHs independently of a labile-hydrocarbon degrading succession. We were able to directly show that assembled genomes belonged to naphthalene degraders by matching their 16S-rRNA gene with experimental stable isotope probing data. Surprisingly, we did not find a full PAH degradation pathway in any of those genomes and even when combining genes from the entire microbial community. We use metabolic pathways identified in those genomes to generate metagenomic-based recommendations for future optimization of PAHs bioremediation.
Combinatorial analysis of Saccharomyces cerevisiae regulatory elements
Authors: Dhillon N. , Shelansky R. , Townshend B. , Jain M. , Boeger H. , Endy D. , Kamakaka R.T.
Published: September 2019

Gene expression in Saccharomyces cerevisiae is regulated at multiple levels. Genomic and epigenomic mapping of transcription factors and chromatin components has led to the definition and delineation of various regulatory elements. Enhancers, promoters, 5’ untranslated regions (5’UTR) and transcription terminators/3’ untranslated regions (3’UTR) have all been defined. However, the specific contributions of each of these features as part of a regulatory unit and the functional communications between these regulatory elements remains under explored.

We built a combinatorial library of 26 different enhancers, core promoters, 5’UTRs and transcription terminators/3’UTRs. This library was analyzed with respect to gene expression in order to better understand the interactions between different regulatory elements. In the process we developed new methods to estimate the contribution of individual regulatory parts from just a few simple measurements. Our data show that different pairs of regulatory parts follow specific interaction rules affecting overall activity either positively or negatively. We find that while enhancers are the initiators of gene activity, core promoters modulate the levels of enhancer mediated expression. Cluster analysis based on expression show that TATA-box containing core promoters appear to increase enhancer-driven transcription to a greater extent than TATA-less promoters. Principal component analysis highlight outliers and suggest differences in mechanisms of regulation. These results provide a system to characterize regulatory elements and use these elements in the design of synthetic regulatory circuits.
Inferring the Genetic Influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition
Authors: Hatoum Alexander S. , Reineberg Andrew E. , Kragel Philip A. , Wager Tor D. , Friedman Naomi P.
Published: September 2019

Genetic correlations between brain and behavioral phenotypes in analyses from major genetic consortia have been weak and mostly non-significant. fMRI models of systems-level brain patterns may help improve our ability to link genes, brains, and behavior by identifying reliable and reproducible endophenotypes. Work using connectivity-based predictive modeling (CBPM) has generated brain-based proxies of behavioral and neuropsychological variables. If such models capture activity in inherited brain systems, they may offer a more powerful link between genes and behavior. As a proof of concept, we develop models predicting intelligence (IQ) based on fMRI connectivity and test their effectiveness as endophenotypes. We link brain and IQ in a model development dataset of N=3,000 individuals; and test the genetic correlations between brain models and measured IQ in a genetic validation sample of N=13,092 individuals from the UKBiobank. We compare an additive connectivity-based model to multivariate LASSO and ridge models phenotypically and genetically. We also compare these approaches to single “candidate” brain areas. We find that predictive brain models were significantly phenotypically correlated with IQ and showed much stronger correlations than individual edges. Further, brain models were more heritable than single brain regions (h2=.155-.181) and capture about half of the genetic variance in IQ (rG=.422-.576), while rGs with single brain measures were smaller and non-significant. For the different approaches, LASSO and Ridge were similarly predictive, with slightly weaker performance of the additive model. LASSO model weights were highly theoretically interpretable and replicated known brain IQ associations. Finally, functional connectivity models trained in midlife showed genetic correlations with early life correlates of IQ, suggesting some stability in the prediction of fMRI models. We conclude that multi-system predictive models hold promise as imaging endophenotypes that offer complex and theoretically relevant conclusions for future imaging genetics research.
Comparison of BOLD and CBV using 3D EPI and 3D GRASE for cortical layer fMRI at 7T
Authors: Beckett Alexander JS , Dadakova Tetiana , Townsend Jennifer , Huber Laurentius , Park Suhyung , Feinberg David A
Published: September 2019

Purpose

Functional MRI (fMRI) at the mesoscale of cortical layers and columns requires both sensitivity and specificity, which can be compromised if the imaging method is affected by vascular artifacts, particularly cortical draining veins at the pial surface. Recent studies have shown that cerebral blood volume (CBV) imaging is more specific to the actual laminar locus of neural activity than BOLD imaging when using standard gradient-echo (GE) EPI sequences. Gradient and Spin Echo (GRASE) BOLD imaging has also shown greater specificity when compared with GE-BOLD.

Methods

Here we directly compare CBV and BOLD contrasts in high-resolution imaging of the primary motor cortex for laminar fMRI in four combinations of signal labeling, VASO (CBV) and BOLD with 3D GE-EPI and zoomed 3D GRASE image readouts.

Results

We find that both CBV imaging using EPI-VASO and BOLD imaging using GRASE-BOLD, show similar specificity and sensitivity and are thus useful tools for mesoscopic fMRI in the human cortex.

Conclusion

These techniques demonstrate sufficient sensitivity and specificity to allow layer-fMRI to be used by neuroscientists in a wide range of investigations of depth-dependent neural circuitry in the human brain.
Active transcription regulates ORC/MCM distribution whereas replication timing correlates with ORC density in human cells
Authors: Kirstein Nina , Buschle Alexander , Wu Xia , Krebs Stefan , Blum Helmut , Hammerschmidt Wolfgang , Hyrien Olivier , Audit Benjamin , Schepers Aloys
Published: September 2019

Eukaryotic replication initiates during S phase from origins that have been licensed in the preceding G1 phase. Here, we compare ChIP-seq profiles of the licensing factors Orc2, Orc3, Mcm3, and Mcm7 with replication initiation events obtained by Okazaki fragment sequencing. We demonstrate that MCM is displaced from early replicating, actively transcribed gene bodies, while ORC is mainly enriched at active TSS. Late replicating, H4K20me3 containing initiation zones display enhanced ORC and MCM levels. Furthermore, we find early RTDs being primarily enriched in ORC, compared to MCM, indicating that ORC levels are involved in organizing the temporal order of DNA replication. The organizational connection between active transcription and replication competence directly links changes in the transcriptional program to flexible replication patterns, which ensures the cell’s flexibility to respond to environmental cues.
High-dimensional Bayesian phenotype classification and model selection using genomic predictors
Authors: Linder Daniel F. , Panchal Viral
Published: September 2019

Motivation

In this paper we describe a Bayesian hierarchical model termed ‘PMMLogit’ for classification and model selection in high-dimensional settings with binary phenotypes as outcomes. Posterior computation in the logistic model is known to be computationally demanding due to its non-conjugacy with common priors. We combine a Polya-Gamma based data augmentation strategy and use recent results on Markov chain Monte-Carlo (MCMC) techniques to develop an efficient and exact sampling strategy for the posterior computation. We use the resulting MCMC chain for model selection and choose the best combination(s) of genomic variables via posterior model probabilities. Further, a Bayesian model averaging (BMA) approach using the posterior mean, which averages across visited models, is shown to give superior prediction of phenotypes given genomic measurements.

Results

Using simulation studies, we compared the performance of the proposed method with other popular methods. Simulation results show that the proposed method is quite effective in selecting the true model and has better estimation and prediction accuracy than other methods. These observations are consistent with theoretical results that have been developed in the statistics literature on optimality for this class of priors. Application to two well-known datasets on colon cancer and leukemia identified genes that have been previously reported in the clinical literature to be related to the disease outcomes.

Availability

Source code is publicly available on GitHub athttps://github.com/v-panchal/PMML.

Contact

dlinder@augusta.edu

Supplementary information

Supplementary data are available online.
High throughput small molecule screening reveals NRF2-dependent and - independent pathways of cellular stress resistance
Authors: Lombard David B. , Kohler William , Guo Angela H. , Gendron Christi , Han Melissa , Ding Weiqiao , Lyu Yang , Li Xinna , Shi Xiaofang , Nikolovska-Coleska Zaneta , Duan Yuzhu , Girke Thomas , Hsu Ao-Lin , Pletcher Scott D. , Miller Richard A.
Published: September 2019

Biological aging is the dominant risk factor for most chronic diseases. Development of anti-aging interventions offers the promise of preventing many such illnesses simultaneously. Cellular stress resistance is an evolutionarily conserved feature of longevity. Here, we identify compounds that induced resistance to the superoxide generator paraquat (PQ), the heavy metal cadmium (Cd), and the DNA alkylator methyl methanesulfonate (MMS). Some rescue compounds conferred resistance to a single stressor, while others provoked multiplex resistance. Induction of stress resistance in fibroblasts was predictive of longevity extension in a published large-scale longevity screen inC. elegans. Transcriptomic analysis implicated Nrf2 signaling in stress resistance provided by two protective compounds, cardamonin and AEG 3482. Molecules that conferred stress resistance also induced cellular inflammatory pathways, and other core pathways such as AMPK signaling. Small molecules identified in this work may represent attractive candidates to evaluate for their potential pro-health and pro-longevity effects in mammals.
Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference
Authors: Zhang Chi , Huelsenbeck John P. , Ronquist Fredrik
Published: September 2019

Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as ‘convergence’) and in estimating the correct proportions of the different types of them (known as ‘mixing’). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical datasets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these datasets, ranging in size from 357 to 934 taxa and from 1,740 to 5,681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account.
DAVI:Deep Learning Based Tool for Alignment and Single Nucleotide Variant identification
Authors: Gupta Gaurav , Saini Shubhi
Published: September 2019

The Next Generation Sequencing (NGS) technologies have provided affordable ways to generate errorful raw genetical data. To extract Variant Information from billions of NGS reads is still a daunting task which involves various hand-crafted and parameterized statistical tools. Here we propose a Deep Neural Networks (DNN) based alignment and SNV tool known as DAVI. DAVI consists of models for both global and local alignment and for Variant Calling. We have evaluated the performance of DAVI against existing state of the art tool-set and found that its accuracy and performance is comparable to existing tools used for benchmarking. We further demonstrate that while existing tools are based on data generated from a specific sequencing technology, the models proposed in DAVI are generic and can be used across different NGS technologies. Moreover, this approach is a migration from expert driven statistical models to generic, automated, self-learning models.
Ultrasound-based optimal parameter estimation improves assessment of calf muscle-tendon interaction during walking
Authors: Delabastita T. , Afschrift M. , Vanwanseele B. , De Groote F.
Published: September 2019

We present and evaluate a new approach to estimate calf muscle-tendon parameters and calculate calf muscle-tendon function during walking. We used motion analysis, ultrasound, and EMG data of the calf muscles collected in six young and six older adults during treadmill walking as inputs to a new optimal estimation algorithm. We used estimated parameters or scaled generic parameters in an existing approach to calculate muscle fiber lengths and activations. We calculated the fit with experimental data in terms of root mean squared differences (RMSD) and coefficients of determination (R2). We also calculated the calf muscle metabolic energy cost. RMSD between measured and calculated fiber lengths and activations decreased and R2increased when estimating parameters compared to using scaled generic parameters. Moreover, R2between measured and calculated gastrocnemius medialis fiber length and soleus activations increased by 19 % and 70 %, and calf muscle metabolic energy decreased by 25% when using estimated parameters compared to using scaled generic parameters at speeds not used for estimation. This new approach estimates calf muscle-tendon parameters in good accordance with values reported in literature. The approach improves calculations of calf muscle-tendon interaction during walking and highlights the importance of individualizing calf muscle-tendon parameters.
Oscillatory Multi-Timescale Mechanisms Underlying Multisensory Sequence Prediction
Authors: Wang Peng , Maÿe Alexander , Daume Jonathan , Xue Gui , Engel Andreas K.
Published: September 2019

Neural oscillations have been proposed to be involved in predictive processing, however less addressed in the multisensory context. In the present study, we recorded cortical activity using magnetoencephalography during an audiovisual serial prediction task in which participants had to acquire stimulus sequences and to monitor whether subsequent probe items complied with the sequence. In a given trial, either visual or auditory features of the bimodal stimuli were task-relevant. Task-related changes of power and coupling of neural oscillations were analyzed using a data-driven clustering approach. We observed prediction-related theta-band coupling in a network involving the cingulate, premotor, prefrontal, and superior temporal regions. The behavioral performance of participants varied as a function of the phase delay of the coupling between different regions in this network. Two additional clusters were detected in which beta power was stronger if the previous or the following stimuli were the same as the current one in the sequence. These clusters interacted with the theta-band prediction network through cross-frequency coupling. Our results offer novel insights into the multi-timescale neural dynamics underlying multisensory predictions, suggesting that oscillations in multiple frequency ranges, as well as coupling within and across frequencies, may be critical for multisensory sequence processing.
A streamlined mass spectrometry-based proteomics workflow for large scale FFPE tissue analysis
Authors: Coscia Fabian , Doll Sophia , Bech Jacob Mathias , Mund Andreas , Lengyel Ernst , Lindebjerg Jan , Madsen Gunvor Iben , Moreira José M. A. , Mann Matthias
Published: September 2019

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Here, we describe an MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from pathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including less than 10,000 cancer cells isolated by laser-capture microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total work up took less than a day. We observed a moderate trend towards lower protein identifications in long-term stored samples (>15 years) but clustering into distinct proteomic subtypes was independent of archival time. Our results underline the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and prove the feasibility of analyzing large tissue cohorts in a robust, timely and streamlined manner.
Sleeve Gastrectomy enhances glucose utilization and remodels adipose tissue independent of weight loss
Authors: Harris David A. , Mina Amir , Cabarkapa Dimitrije , Heshmati Keyvan , Subramaniam Renuka , Banks Alexander S. , Tavakkoli Ali , Sheu Eric G.
Published: September 2019

Objective

Sleeve gastrectomy (SG) induces weight-loss independent improvements in glucose homeostasis by unknown mechanisms. We sought to identify the metabolic adaptations responsible for these improvements.

Methods

Non-obese C57Bl6/J mice on standard chow underwent SG or sham surgery. Functional testing and indirect calorimetry were used to capture metabolic phenotypes. Tissue-specific glucose uptake was assessed by 18-FDG PET/CT and RNA sequencing was used for gene expression analysis.

Results

In this model, SG induced durable improvements in glucose tolerance despite not causing lasting changes in weight, fat/lean mass, or food intake. Indirect calorimetry revealed post-SG animals had respiratory exchange ratios (RER) nearing 1.0 on average and had daily RER excursions above 1.0, indicating preferential glucose utilization and increased energy demand, respectively. Sham operated mice demonstrate normal RER feeding/fasting excursions. PET/CT showed increased avidity within white adipose depots. Finally, SG led to an upregulation in the transcriptional pathways involved in energy metabolism, adipocyte maturation, and adaptive and innate immune cell chemotaxis and differentiation within the visceral adipose tissue.

Conclusions

SG induces a rapid, weight-loss independent shift towards glucose utilization and transcriptional remodeling of metabolic and immune pathways in visceral adipose tissue.
Microbiota Assembly, Structure, and Dynamics Among Tsimane Horticulturalists of the Bolivian Amazon
Authors: Sprockett Daniel D. , Martin Melanie , Costello Elizabeth K. , Burns Adam , Holmes Susan P. , Gurven Michael , Relman David A.
Published: September 2019

Little is known about the relative contributions of selective and neutral forces on human-associated microbiota assembly. Here, we characterize microbial community assembly in 52 Tsimane infant-mother pairs, using longitudinally collected stool and tongue swab samples profiled with 16S rRNA gene amplicon sequencing. The Tsimane are an indigenous Bolivian population who practice infant care associated behaviors expected to increase mother-infant dispersal. Infant consumption of dairy products, vegetables, and chicha (a fermented drink inoculated with oral microbes) was significantly associated with gut microbiota composition. At both body sites, maternal microbes at higher relative abundance were more likely to be shared. Shared microbes were also higher in abundance in infants at both body sites, but decreased in average relative abundance with age and were not significantly higher by 12 months of age. Infant microbiotas were modeled using a neutral community model of assembly, which showed that the prevalence of more than two thirds of infant-colonizing microbes could be explained using neutral processes alone. The same method was applied to datasets from Finnish and Bangladeshi infants, confirming that the majority of microbes colonizing infants from different countries were neutrally distributed. Among the Tsimane infant and adult gut microbiota samples, neutral processes were less prominent in villages with more market access. These results underscore the importance of neutral processes during infant microbiota assembly, and suggest that cultural changes associated with market integration may be affecting traditional modes of microbiota assembly by decreasing the role of these neutral processes, perhaps through changes in diet, sanitation, or access to medical care.
Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons
Authors: Sederberg Audrey , Nemenman Ilya
Published: September 2019

Advances in neural recording methods enable sampling from populations of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to the theoretical models of computations underlying performance. In the context of decision making in rodents, patterns of functional connectivity between choice-selective cortical neurons, as well as broadly distributed choice information in both excitatory and inhibitory populations, were recently reported [1]. The straightforward interpretation of these data suggests a mechanism relying on specific patterns of anatomical connectivity to achieve selective pools of inhibitory as well as excitatory neurons. We investigate an alternative mechanism for the emergence of these experimental observations using a computational approach. We find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and indistinguishable excitatory and inhibitory readout weight distributions, as observed in recorded neural populations. Further, we make the readily verifiable experimental predictions that, for this type of evidence accumulation task, there are no anatomically defined sub-populations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. This work suggests that distributed stimulus selectivity and patterns of functional organization in population codes could be emergent properties of randomly connected networks.

Author summary

What can we learn about neural circuit organization and function from recordings of large populations of neurons? For example, in population recordings in the posterior parietal cortex of mice performing an evidence integration task, particular patterns of selectivity and correlations between cells were observed. One hypothesis for an underlying mechanism generating these patterns is that they follow from intricate rules of connectivity between specific neurons, but this raises the question of how such intricate patterns arise during learning or development. An alternative hypothesis, which we explore here, is that such patterns emerge from networks with broad spectra of eigenvalues, which is a generic property of certain random networks. We find that a random network model matches many features of experimental recordings, from single cells to populations. We suggest that such emergent selectivity could be an important principle in brain areas, in which a broad distribution of selectivity is observed.
Accumulated degeneration of transcriptional regulation contributes to disease development and detrimental clinical outcomes of Alzheimer’s disease
Authors: Meng Guofeng , Lu Dong , Yu Feng , Sun Jijia , Ding Chong , Sun Yan , Liu Xuan , Dai Jiapei , Jin Wenfei , Zhang Weidong
Published: September 2019

Alzheimer’s disease (AD) is extremely complex for both causal mechanism and clinical manifestation, requiring efforts to uncover its diversity and the corresponding mechanisms. Here, we applied a modelling analysis to investigate the regulation divergence among a large-scale cohort of AD patients. We found that transcription regulation tended to get degenerated in AD patients, which contributed to disease development and the detrimental clinical outcomes, mainly by disrupting protein degradation, neuroinflammation, mitochondrial and synaptic functions. To measure the accumulated effects, we came up with a new concept, regulation loss burden, which better correlated with AD related clinical manifestations and the ageing process. The epigenetic studies to multiple active regulation marks also supported a tendency of regulation loss in AD patients. Our finding can lead to a unified model as AD causal mechanism, where AD and its diversity are contributed by accumulated degeneration of transcriptional regulation.

The significance of this study is that: (1) it is the first system biology investigation to transcription regulation divergence among AD patients; (2) we observed an accumulated degeneration of transcription regulation, which well correlates with detrimental clinical outcomes; (3) transcriptional degeneration also contributes to the ageing process, where its correlation with ages is up to 0.78.
Defining data-driven primary transcript annotations with primaryTranscriptAnnotation in R
Authors: Anderson Warren D. , Duarte Fabiana M. , Civelek Mete , Guertin Michael J.
Published: September 2019

Nascent transcript measurements derived from run-on sequencing experiments are critical for the investigation of transcriptional mechanisms and regulatory networks. However, conventional gene annotations specify the boundaries of mRNAs, which significantly differ from the boundaries of primary transcripts. Moreover, transcript isoforms with distinct transcription start and end coordinates can vary between cell types. Therefore, new primary transcript annotations are needed to accurately interpret run-on data. We developed the primaryTranscriptAnnotation R package to infer the transcriptional start and termination sites of annotated genes from genomic run-on data. We then used these inferred co-ordinates to annotate transcriptional units identified de novo. Hence, this package provides the novel utility to integrate data-driven primary transcript annotations with transcriptional unit coordinates identified in an unbiased manner. Our analyses demonstrated that this new methodology increases the sensitivity for detecting differentially expressed transcripts and provides more accurate quantification of RNA polymerase pause indices, consistent with the importance of using accurate primary transcript coordinates for interpreting genomic nascent transcription data.

Availability

https://github.com/WarrenDavidAnderson/genomicsRpackage/tree/master/primaryTranscriptAnnotation
Quantitative mitochondrial DNA copy number determination using droplet digital PCR with single-cell resolution
Authors: O'Hara Ryan , Tedone Enzo , Ludlow Andrew , Huang Ejun , Arosio Beatrice , Mari Daniela , Shay Jerry W.
Published: September 2019

Mitochondria are involved in a number of diverse cellular functions, including energy production, metabolic regulation, apoptosis, calcium homeostasis, cell proliferation, and motility, as well as free radical generation. Mitochondrial DNA (mtDNA) is present at hundreds to thousands of copies per cell in a tissue-specific manner. mtDNA copy number also varies during aging and disease progression and therefore might be considered as a biomarker that mirrors alterations within the human body. Here, we present a new quantitative, highly sensitive droplet digital PCR (ddPCR) method, droplet digital mitochondrial DNA measurement (ddMDM), to measure mtDNA copy number not only from cell populations but also from single cells. Our developed assay can generate data in as little as 3 h, is optimized for 96-well plates, and also allows the direct use of cell lysates without the need for DNA purification or nuclear reference genes. We show that ddMDM is able to detect differences between samples whose mtDNA copy number was close enough as to be indistinguishable by other commonly used mtDNA quantitation methods. By utilizing ddMDM, we show quantitative changes in mtDNA content per cell across a wide variety of physiological contexts including cancer progression, cell cycle progression, human T cell activation, and human aging.
RNA-seq as a tool for evaluating human embryo competence
Authors: Groff Abigail F. , Resetkova Nina , DiDomenico Francesca , Sakkas Denny , Penzias Alan , Rinn John L. , Eggan Kevin
Published: September 2019

The majority of embryos created through in vitro fertilization (IVF) do not implant. It seems plausible that rates of implantation would improve if we had a better understanding of molecular factors affecting embryo competence. Currently, the process of selecting an embryo for uterine transfer uses an ad hoc combination of morphological criteria, the kinetics of development, and genetic testing for aneuploidy. However, no single criterion can ensure selection of a viable embryo. In contrast, RNA-sequencing (RNA-seq) of embryos could yield high-dimensional data, which may provide additional insight and illuminate the discrepancies among current selection criteria. Recent advances enabling the production of RNA-seq libraries from single cells have facilitated the application of this technique to the study of transcriptional events in early human development. However, these studies have not assessed the quality of their constituent embryos relative to commonly used embryological criteria. Here, we perform proof-of-principle advancement to embryo selection procedures by generating RNA-seq libraries from a trophectoderm biopsy as well as the remaining whole embryo. We combine state-of-the-art embryological methods with low-input RNA-seq to develop the first transcriptome-wide approach for assessing embryo competence. Specifically, we show the capacity of RNA-seq as a promising tool in preimplantation screening by showing that biopsies of an embryo can capture valuable information available in the whole embryo from which they are derived. Furthermore, we show that this technique can be used to generate a RNA-based digital karyotype and to identify candidate competence-associated genes. Together, these data establish the foundation for a future RNA-based diagnostic in IVF.
Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features
Authors: Zagorac D. , Müller H. , Ruehl S. , Zagorac J. , Rehme S.
Published: September 2019

The Inorganic Crystal Structure Database (ICSD) is the world's largest database of fully evaluated and published crystal structure data, mostly obtained from experimental results. However, the purely experimental approach is no longer the only route to discover new compounds and structures. In the past few decades, numerous computational methods for simulating and predicting structures of inorganic solids have emerged, creating large numbers of theoretical crystal data. In order to take account of these new developments the scope of the ICSD was extended in 2017 to include theoretical structures which are published in peer-reviewed journals. Each theoretical structure has been carefully evaluated, and the resulting CIF has been extended and standardized. Furthermore, a first classification of theoretical data in the ICSD is presented, including additional categories used for comparison of experimental and theoretical information.
Prediction of potential pseudo-symmetry issues in the indexing of electron backscatter diffraction patterns
Authors: Lenthe William , Singh Saransh , De Graef Marc
Published: September 2019

A new methodology to predict potential pseudo-symmetric or systematically mis-indexed orientations for an arbitrary crystal structure is presented. The method leverages the recently proposed spherical indexing algorithm to index electron backscatter diffraction patterns. Potential pseudo-symmetric orientations are interpreted as secondary peaks in the autocorrelation of the Kikuchi sphere. The generality of the method is illustrated using a number of crystal systems, ranging from nickel, where no significant pseudo-symmetric issues are expected, to SrTiO3, with mild occurrence of such issues, to the olivine series, γ-TiAl and U-6%Nb systems, where the traditional Hough method systematically mis-indexes the pseudo-symmetric variants. Furthermore, the method predicts the severity of potential pseudo-symmetric matches and ranks all variants using a normalized autocorrelation coefficient.
An effective hydrogen scattering cross section for time-of-flight neutron experiments with simple organic molecules
Authors: Capelli Silvia Chiara , Romanelli Giovanni
Published: September 2019

The wavelength dependence of the effective neutron cross section for hydrogen has been investigated by measuring the transmitted total scattering cross section in urea, β-alanine, tartaric acid and polyethylene over the energy range 3 meV to 10 eV. Under the assumption that carbon, nitrogen and oxygen atoms contribute a small and invariant amount to the measured total cross section, these data represent a direct measure of the wavelength dependence of the overall scattering contribution of the hydrogen atoms to the total cross section. These experimental data can be used to apply effective wavelength-dependent corrections to neutron scattering data of hydrogen-rich simple organic compounds.
Determination of X-ray pulse duration via intensity correlation measurements of X-ray fluorescence
Authors: Inoue Ichiro , Tamasaku Kenji , Osaka Taito , Inubushi Yuichi , Yabashi Makina
Published: September 2019

A simple method using X-ray fluorescence is proposed to diagnose the duration of an X-ray free-electron laser (XFEL) pulse. This work shows that the degree of intensity correlation of the X-ray fluorescence generated by irradiating an XFEL pulse on metal foil reflects the magnitude relation between the XFEL duration and the coherence time of the fluorescence. Through intensity correlation measurements of copper Kα fluorescence, the duration of 12 keV XFEL pulses from SACLA was evaluated to be ∼10 fs.