bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantifying climate change impacts on plant functional composition and soil nitrogen fixation in Mediterranean grasslands
Authors: Bomfim Barbara , Dawson Hilary R. , Reed Paul B. , Shek Katherine L. , Bohannan Brendan J. M. , Bridgham Scott D. , Silva Lucas C. R.
Published: September 2022

The projected increase in warming and drought severity (i.e., hotter and drier summers) in the U.S. Pacific Northwest (PNW) may negatively impact grassland plant composition and ecosystem function, with further implications for sustainable land management in the region. To test the vulnerability of Mediterranean grassland function to climate change, we quantified the response of grassland communities to multiannual warming (+2.5°C) and drought (−40% precipitation) by quantifying plant species diversity, legume cover, and biogeochemical controls on and patterns of soil asymbiotic nitrogen fixation (ANF). We hypothesized that the effects of warming on plant functional diversity would increase soil ANF inputs by decreasing legume cover and soil nitrogen availability. Given that asymbiotic N fixers can increase soil organic carbon (C) and nitrogen (N) availability under drought, we hypothesized that the effect of drought on grassland plant cover correlated with increased soil ANF. We surveyed the vegetation and collected composite soil samples from five co-located plots under control (ambient), drought and warming conditions during the fall and spring seasons. In control and drought plots, we quantified the moderator effect of plant composition by comparing low-diversity (unmanipulated plant composition) and high-diversity (manipulated composition) grassland plots. We used a point intercept technique to survey plot-level plant community composition and calculate Shannon’s diversity index and percent cover of legumes (members of Fabaceae according to the Integrated Taxonomic Information System). We measured ANF by incubating collected soils with N-labeled dinitrogen (15N2), and quantified total soil C, total and available N, available phosphorus (P) and iron (Fe) pools, pH, and soil water holding capacity. Plant species diversity decreased significantly with warming and along the drought severity gradient. ANF response to warming varied by season and site, with rates increasing along the drought severity gradient in the fall but decreasing in the spring. Total soil inorganic N was the strongest predictor of ANF response to warming in the spring but not in the fall. Soil ANF response to drought increased with drought intensity; while soil ANF increased nearly twofold in the southernmost (warm and dry) site, ANF decreased in the northernmost (cool and wet) site. ANF response to drought also varied depending on plant diversity, where low-diversity grasslands had more predictable response to drought than high-diversity grasslands. Soil P availability and pH were the most important variables explaining ANF variability across vegetation types and sites. Our study highlights the importance of using soil-plant-atmosphere interactions to assess grassland ecosystem resilience to drought and warming in the PNW.
Influence of Sodium Salicylate on Adventitious Organogenesis of a Commercial Cucumber Cultivar
Authors: Miguel Jorge Fonseca
Published: September 2022

The effect of different concentrations of sodium salicylate (NaSA), a phenolic compound, onin vitrocallus and shoot regeneration of cucumber (Cucumis sativusL.) was investigated. Four-day-old cotyledon explants from the Spanish cultivar ‘Marketer’ were employed. MS-derived shoot induction medium containing 0.5 mg L-1 IAA and 2.5 mg L-1 BAP was supplemented with NaSA. Frequency and shoot number were enhanced by 1.5-fold with NaSA at 0.1 μM. Higher salicylate levels led to increased callus formation and decreased shoot regeneration. The application of sodium salicylate at a specific concentration showed a positive trend inin vitroadventitious organogenesis of a commercial cucumber cultivar. Some probable mechanisms that may underlie the beneficial effects of salicylic acid/salicylates onin vitroregeneration were also discussed.
Distributed coding of evidence accumulation across the mouse brain using microcircuits with a diversity of timescales
Authors: Imani Elaheh , Radkani Setayesh , Hashemi Alireza , Harati Ahad , Pourreza Hamidreza , Goudarzi Morteza Moazami
Published: September 2022

The gradual accumulation of noisy evidence for or against options is the main step in the perceptual decision-making process. Using brain-wide electrophysiological recording in mice (Steinmetz et al., 2019), we examined neural correlates of evidence accumulation across brain areas. We demonstrated that the neurons with Drift-Diffusion-Model-like firing rate activity (i.e., evidence-sensitive ramping firing rate) were distributed across the brain. Exploring the underlying neural mechanism of evidence accumulation for the DDM-like neurons revealed different accumulation mechanisms (i.e. single and race) both within and across the brain areas. Our findings support the hypothesis that evidence accumulation is happening through multiple integration mechanisms in the brain. We further explored the timescale of the integration process in the single and race accumulator models. The results demonstrated that the accumulator microcircuits within each brain area had distinct properties in terms of their integration timescale, which were organized hierarchically across the brain. These findings support the existence of evidence accumulation over multiple timescales. Besides the variability of integration timescale across the brain, a heterogeneity of timescales was observed within each brain area as well. We demonstrated that this variability reflected the diversity of microcircuit parameters, such that accumulators with longer integration timescales had higher recurrent excitation strength.
PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions
Authors: Trudeau Stephen J. , Hwang Howook , Mathur Deepika , Begum Kamrun , Petrey Donald , Murray Diana , Honig Barry
Published: September 2022

We describe the Predicting Protein Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between nearly 7 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence and structural similarity-based metrics are established between template proteins in the Protein Data Bank, T, that bind small molecules, C, and proteins in the models database, Q. When these metrics pass a sequence threshold value, it is assumed that C also binds to Q with a probability derived from machine learning. If the relationship is based on structure, this probability is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto Coefficient identifies other small molecules that may bind to Q. A likelihood ratio for the binding of C to Q is obtained from naïve Bayesian statistics. The PrePCI algorithm performs well under different validations. It can be queried by entering a UniProt ID for a protein and obtaining a list of compounds predicted to bind to it along with associated probabilities. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database are described and a strategy is introduced to use PrePCI as a first step in a docking screen.
Combinatorial assembly and design of enzymes
Authors: Lipsh-Sokolik Rosalie , Khersonsky Olga , Schröder Sybrin P. , de Boer Casper , Hoch Shlomo-Yakir , Davies Gideon J. , Overkleeft Hermen S. , Fleishman Sarel J.
Published: September 2022

Design of structurally diverse enzymes is constrained by long-range interactions that are needed for accurate folding. We introduce an atomistic and machine-learning strategy for Combinatorial Assembly and Design of ENZymes, CADENZ, to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of active and structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a tenfold improved hit rate and >10,000 active enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.
Reconstructor: A COBRApy compatible tool for automated genome-scale metabolic network reconstruction with parsimonious flux-based gap-filling
Authors: Jenior Matthew L , Glass Emma M , Papin Jason A
Published: September 2022

Genome-scale metabolic network reconstructions (GENREs) are valuable for understanding cellular metabolism in silico. Several tools exist for automatic GENRE generation. However, these tools frequently (1) do not readily integrate with some of the widely-used suites of packaged methods available for network analysis, (2) lack effective network curation tools, and (3) are not sufficiently user-friendly. Here, we present Reconstructor, a user-friendly COBRApy compatible tool with ModelSEED namespace compatibility and a pFBA-based gap-filling technique. We demonstrate how Reconstructor readily generates high-quality GENRES that are useful for further biological discovery.

Availability and Implementation

The Reconstructor package is freely available for download via pip in the command line (pip install reconstructor). Usage instructions and benchmarking data are available at http://github.com/emmamglass/reconstructor.

Contact

Jason Papin: papin@virginia.edu
The Groucho co-repressor inhibits progression through the early transcription elongation checkpoint to repress gene expression
Authors: Martínez Quiles María Lorena , Jennings Barbara H
Published: September 2022

The Groucho/Transducin-Like Enhancer of split (Gro/TLE) family proteins act as co-repressors for many transcription factors that act in several key pathways during development and cancer. Recent studies have shown that Gro is enriched at a subset of genes that exhibit RNA polymerase II (RNAP II) pausing in Drosophila melanogaster cells and that depletion of Gro can lead to pause release. Here we compare Gro requirement across three cell lines and find that although Gro target sites are largely cell-type specific, Gro is recruited across a similar profile of genomic features. Gro recruitment frequently overlaps that of general factors which promote RNAP II pausing but it does not preclude recruitment of factors that mediate release of paused RNAP II, indicating that Gro restricts the activity of pause release factors rather than their recruitment. Genetic interaction analyses reveal that the association of Gro with factors known to promote RNAP II pausing in Drosophila cell lines reflects a functional relationship in vivo. Knock-down of factors that promote RNAP II pausing enhance gro knock-down phenotypes during wing vein patterning. However, knocking down these factors in the eye concurrently with gro had different effects on patterning (enhancement) and growth (rescue), indicating that the molecular mechanisms underlying Gro function differ in these two processes. Our results identify a mechanism through which developmentally regulated transcription factors can establish RNAP II pausing to modulate transcription.
CysPresso: A classification model utilizing deep learning protein representations to predict recombinant expression of cysteine-dense peptides
Authors: Ouellet Sébastien , Ferguson Larissa , Lau Angus Z. , Lim Tony K.Y.
Published: September 2022

Background:Cysteine-dense peptides (CDPs) are an attractive pharmaceutical scaffold that display extreme biochemical properties, low immunogenicity, and the ability to bind targets with high affinity and selectivity. While many CDPs have potential and confirmed therapeutic uses, synthesis of CDPs is a challenge. Recent advances have made the recombinant expression of CDPs a viable alternative to chemical synthesis. Moreover, identifying CDPs that can be expressed in mammalian cells is crucial in predicting their compatibility with gene therapy and mRNA therapy. Currently, we lack the ability to identify CDPs that will express recombinantly in mammalian cells without labour intensive experimentation. To address this, we developed CysPresso, a novel machine learning model that predicts recombinant expression of CDPs based on primary sequence.

Results:We tested various protein representations generated by deep learning algorithms (SeqVec, proteInfer, AlphaFold2) for their suitability in predicting CDP expression and found that AlphaFold2 representations possessed the best predictive features. We then optimized the model by concatenation of AlphaFold2 representations, time series transformation with random convolutional kernels, and dataset partitioning.

Conclusion:Our novel model, CysPresso, is the first to successfully predict recombinant CDP expression in mammalian cells and is particularly well suited for predicting recombinant expression of knottin peptides. When preprocessing the deep learning protein representation for supervised machine learning, we found that random convolutional kernel transformation preserves more pertinent information relevant for predicting expressibility than embedding averaging. Our study showcases the applicability of deep learning-based protein representations, such as those provided by AlphaFold2, in tasks beyond structure prediction.
Environment-dependent epistasis increases phenotypic diversity in gene regulatory networks
Authors: Baier Florian , Gauye Florence , Perez-Carrasco Ruben , Payne Joshua L. , Schaerli Yolanda
Published: September 2022

Mutations to gene regulatory networks can be maladaptive or a source of evolutionary novelty. Epistasis confounds our understanding of how mutations impact the expression patterns of gene regulatory networks, because such nonlinearities make it difficult to predict the combined phenotypic effects of mutations based on knowledge of the mutations’ individual effects. This challenge is exacerbated by the dependence of epistasis on the environment, which is particularly germane to gene regulatory networks that interpret signals in space or time. To help fill this knowledge gap, we used the toolkit of synthetic biology to systematically assay the effects of pairwise and triplet combinations of mutant genotypes on the expression pattern of a gene regulatory network expressed in Escherichia coli that interprets an inducer gradient across a spatial domain. We uncovered a preponderance of epistasis in both pairwise and triplet combinations that can switch in magnitude and sign across the inducer gradient to produce a greater diversity of expression pattern phenotypes than would be possible in the absence of such environment-dependent epistasis. We discuss our findings in the context of the evolution of hybrid incompatibilities and evolutionary novelties, arguing that environment-dependent epistasis is likely an important cause of both phenomena in gene regulatory networks.
Phospholipase B is critical for Cryptococcus neoformans survival in the central nervous system
Authors: Hamed Mohamed F. , de Sousa Araújo Glauber Ribeiro , Munzen Melissa E. , Reguera-Gomez Marta , Epstein Carly , Lee Hiu Ham , Frases Susana , Martinez Luis R.
Published: September 2022

Cryptococcus neoformans (Cn) is an opportunistic, encapsulated, yeast-like fungus that causes severe meningoencephalitis, especially in countries with high HIV prevalence. In addition to its well-known polysaccharide capsule, Cn has other virulence factors such as phospholipases, a heterogeneous group of enzymes that hydrolyze ester linkages in glycerophospholipids. Phospholipase B (PLB1) has been demonstrated to play a key role in Cn pathogenicity. In this study, we used a PLB1 mutant (plb1) and its reconstituted strain (Rec1) to assess the importance of this enzyme on Cn brain infection in vivo and in vitro. Mice infected with plb1 strain survive significantly longer, have lower central nervous system (CNS) fungal load, and fewer and smaller cryptococcomas or biofilm-like brain lesions compared to H99- and Rec1-infected animals. plb1 cryptococci are significantly more phagocytosed and killed by NR-9460 microglia-like cells. plb1 cells have altered capsular polysaccharide biophysical properties that impair their ability to stimulate glia cell responses or morphological changes. We provide significant evidence demonstrating that Cn phospholipase is an important virulence factor for fungal colonization of and survival in the CNS as well as in the progression of cryptococcal meningitis. These findings may potentially help fill in a gap of knowledge in our understanding of cerebral cryptococcosis and may provide novel research avenues in Cn pathogenesis.

IMPORTANCE

Cryptococcal meningoencephalitis is a serious disease caused by infection of the neurotropic fungal pathogen Cryptococcus neoformans (Cn). Due to the increasing number of cases in HIV-infected individuals, as well as the limited therapies available, investigation into potential targets for new therapeutics has become critical. Phospholipase B (PLB1) is an enzyme synthesized by Cn that confers virulence to the fungus through capsular enlargement, immunomodulation, and intracellular replication. In this study, we examined the properties of PLB1 by comparing infection of Cn PLB1 mutant strain with both the wild-type and a PLB1 reconstituted strain. We show that PLB1 augments the survival and proliferation of the fungus in the CNS and strengthens virulence through modulation of the immune response and enhancement of specific biophysical properties of the fungus. The implications of PLB1 inhibition reveal its involvement in Cn infection and suggest that it may be a possible molecular target in the development of antifungal therapies. The results of this study support additional investigation into the mechanism of PLB1 to further understand the intricacies of Cn infection.
A deep learning workflow for quantification of Micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation
Authors: Panchbhai Anand , Ishanzadeh Munuse Ceyda , Pankanti Smarana , Sidali Ahmed , Solaiman Nadeeen , Kanagaraj Radhakrishnan , Murphy John J , Surendranath Kalpana
Published: September 2022

The cytokinesis block micronucleus assay is widely used for measuring/scoring/counting micronuclei, a marker of genome instability in cultured and primary cells. Though a gold standard method, this is a laborious and time-consuming process with person-to-person variation observed in quantification of micronuclei. We report in this study the utilisation of a new deep learning workflow for detection of micronuclei in DAPI stained nuclear images. The proposed deep learning framework achieved an average precision of >90% in detection of micronuclei. This proof of principle investigation in a DNA damage studies laboratory supports the idea of deploying AI powered tools in a cost-effective manner for repetitive and laborious tasks with relevant computational expertise. These systems will also help improving the quality of data and wellbeing of researchers.

Simple Summary

This study aims to test a suitable deep learning method for micronucleus detection in images acquired for cytokinesis block micronucleus assay. This study has reached a mean average precision of >90%.
Cholinergic boutons are distributed along the dendrites and somata of VIP neurons in the inferior colliculus
Authors: Kwapiszewski Julia T. , Rivera-Perez Luis M. , Roberts Michael T.
Published: September 2022

Cholinergic signaling shapes sound processing and plasticity in the inferior colliculus (IC), the midbrain hub of the central auditory system, but how cholinergic terminals contact and influence individual neuron types in the IC remains largely unknown. Using pharmacology and electrophysiology, we recently found that acetylcholine strongly excites VIP neurons, a class of glutamatergic principal neurons in the IC, by activating α3β4* nicotinic acetylcholine receptors (nAChRs). Here, we confirm and extend these results using tissue from mice of both sexes. First, we show that mRNA encoding α3and β4nAChR subunits is expressed in many neurons throughout the IC, including most VIP neurons, suggesting that these subunits, which are rare in the brain, are important mediators of cholinergic signaling in the IC.

Next, by combining fluorescent labeling of VIP neurons and immunofluorescence against the vesicular acetylcholine transporter (VAChT), we show that individual VIP neurons in the central nucleus of the IC (ICc) are contacted by a large number of cholinergic boutons. Cholinergic boutons were distributed adjacent to the somata and along the full length of the dendritic arbors of VIP neurons, positioning cholinergic signaling to affect synaptic computations arising throughout the somatodendritic compartments of VIP neurons. In addition, cholinergic boutons were observed in close apposition to dendritic spines on VIP neurons, raising the possibility that cholinergic signaling also modulates presynaptic release onto VIP neurons. Together, these results strengthen evidence that cholinergic signaling exerts widespread influence on auditory computations performed by VIP neurons and other neurons in the IC.
Diverse long-range projections convey position information to the retrosplenial cortex
Authors: Gianatti Michele , Garvert Ann Christin , Vervaeke Koen
Published: September 2022

Neuronal signals encoding the animal’s position, originally discovered in the hippocampus, widely modulate neocortical processing. While it is assumed that these signals depend on hippocampal output, their origin has not been investigated directly. Here, we asked which brain region sends position information to the retrosplenial cortex (RSC), a key circuit for navigation and memory. Using two-photon axonal imaging in head-fixed mice performing a spatial task, we performed a comprehensive functional characterization of long-range inputs to agranular RSC. Surprisingly, most long-range pathways convey position information, but with key differences. We found that axons from the secondary motor cortex transmit the most position information. By contrast, axons from the posterior parietal-anterior cingulate- and orbitofrontal cortex and thalamus convey substantially less position information. Axons from the primary- and secondary visual cortex make a negligible contribution. These data show that RSC is a node in a widely distributed ensemble of networks that share position information in a projection-specific manner.
Transient Polycomb activity represses developmental genes in growing oocytes
Authors: Jarred Ellen G. , Qu Zhipeng , Tsai Tesha , Oberin Ruby , Petautschnig Sigrid , Bildsoe Heidi , Pederson Stephen , Zhang Qing-hua , Stringer Jessica M. , Carroll John , Gardner David K. , van den Buuse Maarten , Sims Natalie A. , Gibson William T. , Adelson David L. , Western Patrick S.
Published: September 2022

Background

Non-genetic disease inheritance and offspring phenotype is substantially influenced by germline epigenetic programming, including genomic imprinting. Loss of Polycomb Repressive Complex 2 (PRC2) function in oocytes causes non-genetically inherited effects on offspring, including embryonic growth restriction followed by post-natal offspring overgrowth. While PRC2 dependent non-canonical imprinting is likely to contribute, less is known about germline epigenetic programming of non-imprinted genes during oocyte growth. In addition, de novo germline mutations in genes encoding PRC2 lead to overgrowth syndromes in human patients, but the extent to which PRC2 activity is conserved in human oocytes is poorly understood.

Results

In this study we identify a discrete period of early oocyte growth during which PRC2 is expressed in mouse growing oocytes. Deletion of Eed during this window led to the de-repression of 343 genes. A high proportion of these were developmental regulators, and the vast majority were not imprinted genes. Many of the de-repressed genes were also marked by the PRC2-dependent epigenetic modification histone 3 lysine 27 trimethylation (H3K27me3) in primary-secondary mouse oocytes, at a time concurrent with PRC2 expression. In addition, we found H3K27me3 was also enriched on many of these genes by the germinal vesicle (GV) stage in human oocytes, strongly indicating that this PRC2 function is conserved in the human germline. However, while the 343 genes were de-repressed in mouse oocytes lacking EED, they were not de-repressed in pre-implantation embryos and lost H3K27me3 during pre-implantation development. This implies that H3K27me3 is a transient feature that represses a wide range of genes in oocytes.

Conclusions

Together, these data indicate that EED has spatially and temporally distinct functions in the female germline to repress a wide range of developmentally important genes, and that this activity is conserved in the mouse and human germlines.
Comprehensive Interactome Mapping of the DNA Repair Scaffold SLX4 using Proximity Labeling and Affinity Purification
Authors: Aprosoff Camila M. , Dyakov Boris J.A. , Cheung Vivian H.W. , Wong Cassandra J. , Palandra Mikaela , Gingras Anne-Claude , Wyatt Haley D.M.
Published: September 2022

The DNA repair scaffold SLX4 has pivotal roles in cellular processes that maintain genome stability, most notably homologous recombination. Germline mutations in SLX4 are associated with Fanconi anemia, a disease characterized by chromosome instability and cancer susceptibility. The role of mammalian SLX4 in homologous recombination depends critically on binding and activating structure-selective endonucleases, namely SLX1, MUS81-EME1, and XPF-ERCC1. Increasing evidence indicates that cells rely on distinct SLX4-dependent complexes to remove DNA lesions in specific regions of the genome. Despite our understanding of SLX4 as a scaffold for DNA repair proteins, a detailed repertoire of SLX4 interactors has never been reported. Here, we provide the first comprehensive map of the human SLX4 interactome using proximity-dependent biotin identification (BioID) and affinity purification coupled to mass spectrometry (AP-MS). We identified 237 high-confidence interactors, of which the vast majority represent novel SLX4 binding proteins. Network analysis of these hits revealed pathways with known involvement of SLX4, such as DNA repair, and novel or emerging pathways of interest, including RNA metabolism and chromatin remodeling. In summary, the comprehensive SLX4 interactome we report here provides a deeper understanding of how SLX4 functions in DNA repair while revealing new cellular processes that may involve SLX4.
Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials
Authors: Gong Tiansu , Ju Fusong , Bu Dongbo
Published: September 2022

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of Knot-Fold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using a self-attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially-designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely-used dynamic programming algorithms, thus facilitating the identification of pseudoknots. Using a total of 1605 RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy significantly higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.
Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression
Authors: Sagy Naor , Meyrom Noa , Beckerman Pazit , Pleniceanu Oren , Bar Daniel Z
Published: September 2022

Chronological and biological age correlate with DNA methylation levels at specific sites in the genome. Linear combinations of multiple methylation sites, termed epigenetic clocks, can inform us of the chronological age and predict multiple health-related outcomes. However, why some sites correlate with lifespan, healthspan, or specific medical conditions remains poorly understood. Kidney fibrosis is the common pathway for Chronic Kidney Disease, which affects 10% of Europe and USA population. Here we identify epigenetic clocks and methylation sites that correlate with kidney function. Moreover, we identify methylation sites that have a unique methylation signature in the kidney. Methylation levels in the majority of these sites correlates with kidney state and function. When kidney function deteriorates, all of these sites regress towards the common methylation pattern observed in other tissues. Interestingly, while the majority of sites are less methylated in the kidney and become more methylated with loss of function, a fraction of the sites are highly methylated in the kidney and become less methylated when kidney function declines. These methylation sites are enriched for specific transcription-factor binding sites. In a large subset of sites, changes in methylation pattern are accompanied by changes in gene expression in kidneys of chronic kidney disease patients. These results support the information theory of aging, and the hypothesis that the unique tissue identity, as captured by methylation patterns, is lost as tissue function declines. However, this information loss is not random, but guided towards a baseline that is dependent on the genomic loci.
A benchmarking of human Y-chromosomal haplogroup classifiers from whole-genome and whole-exome sequence data
Authors: García-Olivares Víctor , Muñoz-Barrera Adrián , Rubio-Rodríguez Luis A. , Jáspez David , Díaz-de Usera Ana , Iñigo Campos Antonio , Veeramah Krishna R. , Alonso Santos , Thomas Mark G. , Lorenzo-Salazar José M. , González-Montelongo Rafaela , Flores Carlos
Published: September 2022

The non-recombinant region of the Y chromosome (NRY) contains a great number of polymorphic markers that allows to accurately reconstruct pedigree relationships and retrieve ancestral information from study samples. The analysis of NRY is typically implemented in anthropological, medical, and forensic studies. High-throughput sequencing (HTS) has profoundly increased the identification of genetic markers in the NRY genealogy and has prompted the development of automated NRY haplogroup classification tools. Here, we present a benchmarking study of five command-line tools for NRY haplogroup classification. The evaluation was done using empirical short-read HTS data from 50 unrelated donors using paired data from whole-genome sequencing (WGS) and whole-exome sequencing (WES) experiments. Besides, we evaluate the performance of the top-ranked tool in the classification of data of third generation HTS obtained from a subset of donors. Our findings demonstrate that WES can be an efficient approach to infer the NRY haplogroup, albeit generally providing a lower level of genealogical resolution than that recovered by WGS. Among the tools evaluated, YLeaf offers the best performance for both WGS and WES applications. Finally, we demonstrate that YLeaf is able to correctly classify all samples sequenced with nanopore technology from long noisy reads.
Can neural correlates of encoding explain the context dependence of reward-enhanced memory?
Authors: Hellerstedt Robin , Bekinschtein Tristan , Talmi Deborah
Published: September 2022

Selective encoding can be studied by manipulating how valuable it is for participants to remember specific stimuli, for instance by varying the monetary reward participants receive for recalling a particular stimulus in a subsequent memory test. It would be reasonable for participants to strategically attend more to high-reward items compared to low-reward items in mixed list contexts, but to attend both types of items equally in pure list contexts, where all items are of equal value. Reward-enhanced memory may be driven by automatic dopaminergic interactions between reward circuitry and the hippocampus and thus be insensitive to list context; or it may be driven by meta-cognitive strategies, and thus context-dependent. We contrasted these alternatives by manipulating list composition, and tracked selective encoding through multiple EEG measures of attention and rehearsal. Behavioural results were context-dependent, such that recall of high-reward items was increased only in mixed lists. This result and aspects of the recall dynamics confirm predictions of the eCMR (emotional Context Maintenance and Retrieval) model. The power of ssVEPs was lower for high-reward items regardless of list composition, suggesting decreased visual processing of high-reward stimuli and that ssVEPs may index the modulation of context-to-item associations predicted by eCMR. By contrast, reward modulated the amplitude of Late Positive Potential and Frontal Slow Wave only in mixed lists. Taken together the results provide evidence that reward-enhanced memory is caused by an interplay between strategic processes applied when high- and low-reward items compete for cognitive resources during encoding and context-dependent mechanisms operating during recall.
Human immunodeficiency virus integration complexes are active following ordered addition of wild type integrase, viral DNA, and LEDGF/p75
Authors: Rabe Anthony J. , Russo Jacob A. , Larue Ross C. , Yoder Kristine E.
Published: September 2022

Human immunodeficiency virus (HIV-1) requires integration of the viral genome into the host DNA for replication. Efficient HIV-1 integration employs a host co-factor LEDGF/p75 to stabilize the HIV-1 integration complex and tether that complex to host chromatin. Integration may be studied with purified components HIV-1 integrase (IN), LEDGF/p75, and DNA mimicking the ends of the viral DNA genome (vDNA) assembled as an intasome. There is a likely order of addition during infection with HIV-1 IN binding to vDNA before encountering LEDGF/p75. However, the ordered assembly of wild type HIV-1 IN, LEDGF/p75, and oligomer vDNA has not been tested. Variable assemblies occurred on ice before the addition of target DNA. Incubation on ice and addition of LEDGF/p75 were required to assemble complexes capable of efficient concerted integration. Integration efficiency following variable order of addition of intasome components was greatest when LEDGF/p75 was added last to preassembled HIV-1 IN and vDNA.
Nuclear morphology predicts cell survival to cisplatin chemotherapy
Authors: Kim Chi-Ju , Gonye Anna LK , Truskowski Kevin , Lee Cheng-Fan , Cho Yoon-Kyoung , Austin Robert H , Pienta Kenneth J , Amend Sarah R
Published: September 2022

In this study, we characterized nuclear morphology and function as cancer cells underwent recovery following chemotherapeutic treatment to identify the unique characteristics associated with treatment resistance and successful survival. Cells that survived following treatment and resisted therapy-induced cell death were predominantly mononucleated with increased nuclear/cellular size, enabled by continuous endocycling. We found that cells that survive after therapy release likely employ more efficient DNA damage repair and exhibit a distinct nucleolar phenotype - fewer but larger nucleoli – and increased rRNA levels. These data support a paradigm where soon after therapy release, the treated population mostly contains cells with a high level of widespread and catastrophic DNA damage that leads to apoptosis, while the minority of cells that have successful DDR are more likely to access a pro-survival state. These findings suggest that one way cancer cells can survive systemic therapy is to enter the polyaneuploid cancer cell (PACC) state, a recently-described mechanism of therapy resistance. Cancer cells in this state are physically enlarged, undergo whole-genome doubling resulting in polyaneuploid genomes, and are associated with worse prognosis in cancer patients. The PACC state is accessed when a cancer cell experiences external stress, such as genotoxic chemotherapy; after a period of recovery, cells exit the PACC state and resume proliferation to repopulate the tumor cell pool. Our findings demonstrate the fate of cancer cells following chemotherapy treatment and define key characteristics of the resistant PACC state. This work is essential for understanding and, ultimately, targeting, cancer resistance and recurrence.
Inducible CRISPR epigenome systems mimic cocaine induced bidirectional regulation of Nab2 and Egr3
Authors: Choi Eric Y. , Franco Daniela , Stapf Catherine A. , Gordin Madeleine , Chow Amanda , Cover Kara K. , Chandra Ramesh , Lobo Mary Kay
Published: September 2022

Substance use disorder is a debilitating chronic disease and a leading cause of disability around the world. The nucleus accumbens (NAc) is a major brain hub that mediates reward behavior. Studies demonstrate exposure to cocaine is associated with molecular and functional imbalance in two NAc medium spiny neuron subtypes (MSNs), dopamine receptor 1 and 2 enriched D1-MSNs and D2-MSNs. Our previous reports showed that repeated cocaine exposure induced transcription factor early growth response 3 (Egr3) mRNA in NAc D1-MSNs, while reducing it in D2-MSNs. Here, we report our findings of repeated cocaine exposure inducing cell subtype specific bidirectional expression of the Egr3 corepressor NGFI-A-binding protein 2 (Nab2). Using CRISPR activation and interference (CRISPRa and CRISPRi) tools combined with Nab2 or Egr3 targeted sgRNAs, we mimicked these bidirectional changes in Neuro2a cells. Furthermore, we investigated D1-MSN and D2-MSN subtype specific expressional changes of histone lysine demethylases Kdm1a, Kdm6a and Kdm5c in NAc after repeated cocaine exposure. Since Kdm1a showed bidirectional expression patterns in D1-MSNs and D2-MSNs, like Egr3, we developed a light inducible Opto-CRISPR-KDM1a system. We were able to downregulate Egr3 and Nab2 transcripts and cause bidirectional expression changes in D1-MSNs and D2-MSNs similar to cocaine exposure in Neuro2A cells. In contrast, our Opto-CRISPR-p300 activation system induced the Egr3 and Nab2 transcripts and caused bidirectional transcription regulations in D1-MSNs and D2-MSNs. Our study sheds light on the expression patterns of Nab2 and Egr3 in specific NAc MSN subtypes in cocaine action and uses CRISPR tools to further mimic these expression patterns.
Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification
Authors: Bifarin Olatomiwa O.
Published: September 2022

Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability is germane. In metabolomics, partial least square discriminant analysis (PLS-DA) and its variants are widely used, partly due to the model’s interpretability with the Variable Influence in Projection (VIP) scores, a global interpretable method. Herein, Tree-based Shapley Additive explanations (SHAP), an interpretable ML method grounded in game theory, was used to explain ML models with local explanation properties. In this study, ML experiments (binary classification) were conducted for three published metabolomics datasets using PLS-DA, random forests, gradient boosting, and extreme gradient boosting (XGBoost). Using one of the datasets, PLS-DA model was explained using VIP scores, while a tree-based model was interpreted using Tree SHAP. The results show that SHAP has a more explanation depth than PLS-DA’s VIP, making it a powerful method for rationalizing machine learning predictions from metabolomics studies.
DNA processing by the Kaposi’s sarcoma-associated herpesvirus alkaline exonuclease SOX contributes to viral gene expression and infectious virion production
Authors: Hartenian Ella , Mendez Aaron S. , Didychuk Allison L. , Khosla Shivani , Glaunsinger Britt A.
Published: September 2022

Alkaline exonucleases (AE) are present in several large DNA viruses including bacteriophage λ and herpesviruses, where they play roles in viral DNA processing during genome replication. Given the genetic conservation of AEs across viruses infecting different kingdoms of life, these enzymes likely assume central roles in the lifecycles of viruses where they have yet to be well characterized. Here, we applied a structure-guided functional analysis of the bifunctional AE in the oncogenic human gammaherpesvirus Kaposi’s sarcoma-associated herpesvirus (KSHV), called SOX. In addition to identifying a preferred DNA substrate preference for SOX, we define key residues important for DNA binding and DNA processing, and how SOX activity on DNA partially overlaps with its functionally separable cleavage of mRNA. By engineering these SOX mutants into KSHV, we reveal roles for its DNase activity in viral gene expression and infectious virion production. Our results provide mechanistic insight into gammaherpesviral AE activity as well as areas of functional conservation between this mammalian virus AE and its distant relative in phage λ.
Minimal cell JCVI-syn3B as a chassis to reveal the mechanisms behind Mycoplasma host–pathogen interactions
Authors: de C. Bittencourt Daniela M. , Brown David M. , Assad-Garcia Nacyra , Lynott Michaela R. , Sun Lijie , Palhares de Melo Luis Alberto M. , Freire Marcelo , Glass John I.
Published: September 2022

Mycoplasmas are atypical bacteria that are obligate parasites of animals and plants. They are known for their capacity to contaminate and parasitize mammalian cell cultures, thereby offering fortuitous models to examine the host-microbe interaction. We used a near wildtype strain of the goat pathogen Mycoplasma mycoides (Mmc) and several minimized Mmc strains lacking genes not essential for growth in laboratory media to investigate host-mycoplasma interactions. Stains with near minimal genomes were incapable of surviving in co-culture with mammalian HEK-293T and HeLa cells. We identified a cluster of eight non-essential genes that when added back to the minimized strains enabled mycoplasma attachment to mammalian cells. Those genes did not restore the capacity of the minimal cell to grow when co-cultured with mammalian cells indicating processes of attaching to mammalian cells and parasitism involve different genes. Furthermore, minimized Mmc strains were not phagocytized by human myeloid cells unlike the near wildtype Mmc.
Learning probabilistic protein–DNA recognition codes from DNA-binding specificities using structural mappings
Authors: Wetzel Joshua L. , Zhang Kaiqian , Singh Mona
Published: September 2022

Knowledge of how proteins interact with DNA is essential for understanding gene regulation. Although DNA-binding specificities for thousands of transcription factors (TFs) have been determined, the specific amino acid–base interactions comprising their structural interfaces are largely unknown. This lack of resolution hampers attempts to leverage these data in order to predict specificities for uncharacterized TFs or TFs mutated in disease. Here we introduce recognition code learning via automated mapping of protein–DNA structural interfaces (rCLAMPS), a probabilistic approach that uses DNA-binding specificities for TFs from the same structural family to simultaneously infer both which nucleotide positions are contacted by particular amino acids within the TF as well as a recognition code that relates each base-contacting amino acid to nucleotide preferences at the DNA positions it contacts. We apply rCLAMPS to homeodomains, the second largest family of TFs in metazoans and show that it learns a highly effective recognition code that can predict de novo DNA-binding specificities for TFs. Furthermore, we show that the inferred amino acid–nucleotide contacts reveal whether and how nucleotide preferences at individual binding site positions are altered by mutations within TFs. Our approach is an important step toward automatically uncovering the determinants of protein–DNA specificity from large compendia of DNA-binding specificities and inferring the altered functionalities of TFs mutated in disease.
Discovery of topic evolution path and semantic relationship based on patent entity representation
Authors: Zhang Jinzhu , Liu Yue , Jiang Linqi , Shi Jialu
Published: September 2022

Purpose

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Design/methodology/approach

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

Findings

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

Originality/value

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.