Miracle-dz/UniFAR-FTUs huggingface dataset:
The scales and signatures of climate adaptation by the Arabidopsis transcriptome

Clines in allele frequency and trait variation can be highly informative for understanding how populations have historically adapted to climate variation across landscapes. However, as a consequence of the many complexities inherent to this process, these climate-associated differentiation patterns can be confounded, misleading, or obscured. Molecular phenotypes like gene expression levels are a potentially valuable means for resolving these complexities. Their intermediate position between genomes and organismal traits and their interrelatedness structured by gene regulatory networks can help parse how different climatic factors contribute to unique components of range-wide or region-specific diversity patterns. Here, we demonstrate these explanatory values of gene expression variation through integrative analyses of transcriptomic data from 665 Arabidopsis thaliana accessions. Differentiation of co-expressed genes is often associated with source site climate. Although some patterns hold range-wide, many other gene expression clines are specific to particular ancestry groups, reflecting how broad-scale and local combinations of selective agents differentially resolve functional interrelationships between plant defense, drought tolerance, and life history traits. We also extend these analyses to parse how different factors explain climate-associated variation in flowering time and its plasticity. Expression of key regulators FLC and SOC1 strongly predicts time to flower, consistent with previous work, but our results also highlight novel relationships that indicate as yet unexplored climate-related connections between defense signaling and flowering. Finally, we show that integrative models combining genotype and gene expression information predict variation in flowering time under ecologically realistic conditions more accurately than models based on either source alone. Significance Statement Populations often adapt to local conditions along climate gradients, and associations between climate parameters and traits or alleles often indicate a history of adaptive differentiation. However, such signals can be obscured or misleading due to the complex genetics underlying trait variation or other historical processes, frustrating our capacity to reveal how populations adapt to diverse climates. As a molecular intermediate between genetic polymorphisms and their impact on organismal traits, gene expression variation is a useful readout for addressing several of these difficulties. Here, we leverage transcriptomic data from hundreds of Arabidopsis thaliana accessions to reveal continental and region-specific patterns of climate-associated differentiation as well as investigate how gene expression adaptation at both scales shapes flowering time variation along climate gradients.
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network

The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by – and fitted to – experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Oja’s rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions.
A universal framework for detecting cis-regulatory diversity in DNA regulatory regions

High-throughput sequencing-based assays measure different biochemical activities pertaining to gene regulation, genome-wide. These activities include protein-DNA binding, enhancer-activity, open chromatin, and more. A major goal is to understand underlying sequence components, or motifs, that can explain the measured activity. It is usually not one motif, but a combination of motifs bound by cooperatively acting proteins that confers activity to such regions. Furthermore, although having a single type of activity, the regions can still be diverse, governed by different combinations of proteins/motifs. Current approaches do not take into account this issue of combinatorial diversity. We present a new statistical framework cisDiversity, which models regions as diverse modules characterized by combinations of motifs, while simultaneously learning the motifs themselves. We show that ChIP-seq data for the CTCF protein in fly contains diverse sequence structures, with most direct CTCF-binding sites situated far from promoters, giving insights into its co-factors and potential role in looping. Human CTCF-bound regions, on the other hand, have a different architecture. Because cisDiversity does not rely on knowledge of motifs, modules, cell-type, or organism, it is general enough to be applied to regions reported by most high-throughput assays. Indeed, enhancer predictions resulting from different assays—GRO-cap, STARR-seq, and those measuring chromatin structure—show distinct modules and combinations of TF binding sites, some specific to the assay. No module occurs universally in all enhancer-assays. Finally, analysis of accessible chromatin suggests that regions open in one cell-state encode information about future states, with certain modules staying open and others closing down later. The code is freely available at https://github.com/NarlikarLab/cisDIVERSITY.
H3k27ac-HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility

Genome-wide association studies (GWAS) have identified more than 140 prostate cancer (PrCa) risk regions which provide potential insights into causal mechanisms. Multiple lines of evidence show that a significant proportion of PrCa risk can be explained by germline causal variants that dysregulate nearby target genes in prostate-relevant tissues thus altering disease risk. The traditional approach to explore this hypothesis has been correlating GWAS variants with steady-state transcript levels, referred to as expression quantitative trait loci (eQTLs). In this work, we assess the utility of chromosome conformation capture (3C) coupled with immunoprecipitation (HiChIP) to identify target genes for PrCa GWAS risk loci. We find that interactome data confirms previously reported PrCa target genes identified through GWAS/eQTL overlap (e.g., MLPH). Interestingly, HiChIP identified links between PrCa GWAS variants and genes well-known to play a role in prostate cancer biology (e.g., AR) that are not detected by eQTL-based methods. We validate these findings through CRISPR interference (CRISPRi) perturbation of the variant-containing regulatory elements for NKX3-1 and AR in the LNCaP cell line. Our results demonstrate that looping data harbor additional information beyond eQTLs and expand the number of PrCa GWAS loci that can be linked to candidate susceptibility genes.
A multivariate method to correct for batch effects in microbiome data

Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to, and obscure any factors of interest. Existing batch correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce a new multivariate and non-parametric batch correction method based on Partial Least Squares Discriminant Analysis. PLSDA-batch first estimates treatment and batch variation with latent components to then subtract batch variation from the data. The resulting batch effect corrected data can then be input in any downstream statistical analysis. Two variants are also proposed to handle unbalanced batch x treatment designs and to include variable selection during component estimation. We compare our approaches with existing batch correction methods removeBatchEffect and ComBat on simulated and three case studies. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, and especially when batch effects have high variability. Reproducible code and vignettes are available on GitHub.
The anti-mycobacterial activity of Artemisia annua L is based on deoxyartemisinin and artemisinic acid

The discovery of antimalarial artemisinin from Artemisia annua L. is an example of how Traditional Chinese Medicine (TCM) may be exploited to meet a recognized need. In this study, we systemically investigated A. annua L. for its antimicrobial activity and assessed it as a source of bioactive natural products for anti-mycobacterial activity. We used a silica gel column to perform antimicrobial activity-guided purification of the A. annua leaf, whose identity was confirmed by rbcL DNA barcoding, and used UHPLC-HRMS and NMR to elucidate the structure of purified active compounds. The antimicrobial activity of crude extracts, isolated compounds and the control artemisinin (Apollo Scientific Ltd) was assessed against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium smegmatis strains by serial micro dilution method (31.25-1000 μg/mL). The isolated compounds were tested for synergistic effects against mycobacterium. Bioactive compounds were purified and identified as deoxyartemisinin and artemisinic acid. Artemisinic acid (MIC 250 μg/mL) was more effective in comparison to deoxyartemisinin (MIC 500 μg/mL) and artemisinin (MIC 1000 μg/mL) against M. smegmatis. We used a molecular docking approach to investigate the interactions between selected anti-mycobacterial compounds and proteins involved in vital physiological functions in M. tuberculosis, namely MtPks13, MtPknB, MtPanK, MtKasA, MtInhA and MtDprE1 and found artemisinic acid showed docking scores superior to the control inhibiters for MtKasA, suggesting it to be a potential nick for further in vitro biological evaluation and anti-TB drug design.
COVID-19 cytokines and the hyperactive immune response: Synergism of TNF-α and IFN-γ in triggering inflammation, tissue damage, and death

1 Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA 2 Department of Microbiology, Immunology, & Biochemistry, University of Tennessee Health Science Center, Memphis, TN, 38163, USA 3 The Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA 4 Hartwell Center for Bioinformatics & Biotechnology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA 5 Animal Resources Center and Veterinary Pathology Core, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA 6 Department of Infectious Disease, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
Transcriptomic Profiling of Fibropapillomatosis in Green Sea Turtles (Chelonia mydas) From South Texas

Sea turtle fibropapillomatosis (FP) is a tumor promoting disease that is one of several threats globally to endangered sea turtle populations. The prevalence of FP is highest in green sea turtle (Chelonia mydas) populations, and historically has shown considerable temporal growth. FP tumors can significantly affect the ability of turtles to forage for food and avoid predation and can grow to debilitating sizes. In the current study, based in South Texas, we have applied transcriptome sequencing to FP tumors and healthy control tissue to study the gene expression profiles of FP. By identifying differentially expressed turtle genes in FP, and matching these genes to their closest human ortholog we draw on the wealth of human based knowledge, specifically human cancer, to identify new insights into the biology of sea turtle FP. We show that several genes aberrantly expressed in FP tumors have known tumor promoting biology in humans, including CTHRC1 and NLRC5, and provide support that disruption of the Wnt signaling pathway is a feature of FP. Further, we profiled the expression of current targets of immune checkpoint inhibitors from human oncology in FP tumors and identified potential candidates for future studies.
Ultrastructural view of astrocyte-astrocyte and astrocyte-synapse contacts within the hippocampus

Astrocytes branch out and make contact at their interfaces. However, the ultrastructural interactions of astrocytes and astrocytes with their surroundings, including the spatial-location selectivity of astrocyte-synapse contacts, remain unknown. Here, the branching architecture of three neighboring astrocytes, their contact interfaces, and their surrounding neurites and synapses have been traced and 3D reconstructed using serial block-face scanning electron microscopy (SBF-SEM). Our reconstructions reveal extensive reflexive, loop-like processes that serve as scaffolds to neurites and give rise to spongiform astrocytic morphology. At the astrocyte-astrocyte interface, a cluster of process-process contacts were identified, which biophysically explains the existence of low inter-astrocytic electrical resistance. Additionally, we found that synapses uniformly made contact with the entire astrocyte, from soma to terminal processes, and can be ensheathed by two neighboring astrocytes. Lastly, in contrast to densely packed vesicles at the synaptic boutons, vesicle-like structures were scant within astrocytes. Together, these ultrastructural details should expand our understanding of functional astrocyte-astrocyte and astrocyte-neuron interactions.
Promotion of USP4 to TGF-β1-induced EMT in renal tubular epithelial cells is regulated by Akt through stabilizing TβRI

Objective We aimed to explore the role of ubiquitin-specific peptidase-4 (USP4) in TGF-β1 induced epithelial-mesenchymal transition (EMT) during renal fibrosis, and investigated that if Akt inactivation exerted a critical effect on EMT via USP4/TβRI pathway. Methods USP4, pAkt and TβRI proteins in the obstructed kidneys from unilateral ureteral obstruction (UUO) rats were detected by immunohistochemistry assay or western blot method. E-cadherin, α-SMA, USP4 and pAkt protein expression in NRK-52E cells at different concentration of TGF-β1 were detected at different time. Further, NRK-52E cells were transfected with USP4-specific siRNA (si-USP4), and then stimulated with 10 ng/ml TGF-β1 for 24h to detect E-cadherin, α-SMA, E-cadherin and TβRI by immunofluorescent double staining assay. Pretreated with PI3K inhibitor LY294002, protein expression levels of pAkt, E-cadherin, α-SMA were detected. Meanwhile, the location of USP4 was visualized by immunofluorescent assay in NRK-52E cells. Results The expression of USP4 and TβRI was significantly upregulated in the tubular epithelial cells of UUO rats. We also found that TGF-β1 upregulated USP4 and activated Akt in NRK-52E cells during EMT. In vitro, downregulation of USP4 inhibited TβRI expression and partially reversed EMT stimulated by TGF-β1. In the meantime, blunted phosphorylation of Akt with LY294002 promoted the E-cadherin expression, and inhibited α-SMA expression in response to TGF-β1. However, inactivation of Akt could reverse EMT process, but failed to induce USP4 to shuttle between the nucleus and the cytoplasm in NRK-52E cells stimulated by TGF-β1. Conclusions These data implied that USP4 was a harmful molecule induced by TGF-β1, regulated by Akt activation and promoted TGF-β1-induced EMT via TβRI in tubular epithelial cells during renal fibrosis.
Tracking pre-mRNA maturation across subcellular compartments identifies developmental gene regulation through intron retention and nuclear anchoring

To globally assess the distribution and processing of gene transcripts across subcellular compartments, we developed extensive RNA-seq datasets of both polyA+ and total RNA from chromatin, nucleoplasm and cytoplasm of mouse ESC, neuronal progenitors, and neurons. We identified protein-coding genes whose polyadenylated transcripts were more abundant in chromatin than cytoplasm. We defined introns exhibiting cotranscriptional splicing, complete intron retention in cytoplasmic RNA, and many introns retained in nucleoplasmic and chromatin RNA but absent from cytoplasmic RNA, including new introns controlled during neuronal development. In particular, we found that polyadenylated Gabbr1 transcripts are expressed in mESC but remain sequestered on chromatin until neuronal differentiation when they are processed and released to the cytoplasm. This developmental regulation of splicing and chromatin association demonstrates that the abundance of polyadenylated RNA is not always an indicator of functional gene expression. Our datasets provide a rich resource for analyzing many other aspects of mRNA maturation.
Target lipidomics reveals associations between serum sphingolipids and insulin sensitivity by the glucose clamp

Background This study aimed to systematically investigate the associations between serum sphingolipids and insulin sensitivity as well as insulin secretion. This study also aimed to reveal potential predictors for insulin sensitivity or give perceptive insight into disease processes. Methods We conducted a lipidomics evaluation of molecularly distinct SPs in the serum of 86 consecutive Chinese adults with or without obesity and diabetes using electrospray ionization mass spectrometry coupled with liquid chromatography. The GIR30 was measured under steady conditions to assess insulin sensitivity by the gold standard hyperinsulinemic-euglycemic clamp. We created the ROC curves to detect the serum SMs diagnostic value and establish the diagnosis of insulin sensitivity. Results Differential correlation network analysis illustrated correlations amongst lipids, insulin sensitivity, insulin secretion and other clinical indexes. Total and subspecies of serum SMs and globotriaosylceramides (Gb3s) were positively related to GIR30, free FAs (FFA 16:1, FFA20:4), some long chain GM3 and complex ceramide GluCers showed strong negative correlations with GIR30. Notably, ROC curves showed that SM/Cer and SM d18:0/26:0 may be good serum lipid predictors of diagnostic indicators of insulin sensitivity close to conventional clinical indexes such as 1/HOMA-IR (all areas under the curve >0.80) based on GIR30 as standard diagnostic criteria. Conclusions These results provide novel associations between serum sphingolipid between insulin sensitivity measured by the hyperinsulinemic-euglycemic clamp. We further identify two specific SPs that may represent prognostic biomarkers for insulin sensitivity.
Prevalence and Associated Factors of Agricultural Technology Adoption and Teff Productivity in Basso Liben District, East Gojjame Zone, North west Ethiopia

Teff productivity and Teff technology adoption in Ethiopia is low and it requiring immediate attention for policymakers and decision-makers. This study was conducted to identify the determinant factors that affect Teff technology adoption and Teff productivity in Basso Liben district, East Gojjam Zone, Northwest Ethiopia. A cross-sectional study design was conducted among 190 households. Multivariable linear and logistic regressions were employed to identify the factors associated with Teff production and Teff technology adoption respectively. Of a total of 190 households considered in the study, 77.9% were Teff technology adopter. Household head gender (male) (OR=7.644), family size (OR=1.149), age of household head (OR=0.873), row planting (use)(OR=257.2), credit access (yes)(OR=3.141), manure(use)(OR=0.042) were significance associated with Teff technology adoption in the study area. Age of household head (β = 0.079), Education level (primary)(β = −0.612), total land holding (β = 5.107), annual income(β=0.0051), extension service (no)(β = −0.635), row planting (yes) (β = 1.409), organic fertilizer (no)(β = −0.946) were significance associated with teff production in the study area. In this study, a low prevalence of agricultural technology adoption and Teff production and various associated agricultural technology adoption and Teff production factors have been identified in the study area. Thus, the concerned stockholders should intervene in agricultural technology adoption and Teff production via different extension service and by considering household size, community-based household head education, and efficient use landholding in hectare.
CuBlock: a cross-platform normalization method for gene-expression microarrays

Motivation Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups. Results We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct data sets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these data sets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study. Availability CuBlock can be downloaded from https://www.mathworks. com/matlabcentral/fileexchange/77882-cublock Contact xouse@anaxomics.com, xavier.daura@uab.cat Supplementary information Supplementary data are available at bioRxiv online.