p65 signaling dynamics drive the developmental progression of hematopoietic stem and progenitor cells through cell cycle regulation.

Most gene functions have been discovered through phenotypic observations under loss of function experiments that lack temporal control. However, cell signaling relies on limited transcriptional effectors, having to be re-used temporally and spatially within the organism. Despite that, the dynamic nature of signaling pathways have been overlooked due to the difficulty on their assessment, resulting in important bottlenecks. Here, we have utilized the rapid and synchronized developmental transitions occurring within the zebrafish embryo, in conjunction with custom NF-kB reporter embryos driving destabilized fluorophores that report signaling dynamics in real time. We reveal that NF-kB signaling works as a clock that controls the developmental progression of hematopoietic stem and progenitor cells (HSPCs) by two p65 activity waves that inhibit cell cycle. Temporal disruption of each wave results in contrasting phenotypic outcomes: loss of HSPCs due to impaired specification versus proliferative expansion and failure to delaminate from their niche. We also show functional conservation during human hematopoietic development using iPSC models. Our work identifies p65 as a previously unrecognized contributor to cell cycle regulation, revealing why and when pro-inflammatory signaling is required during HSPC development. It highlights the importance of considering and leveraging cell signaling as a temporally dynamic entity.

Dynamic relationships among pathways producing hydrocarbons and fatty acids of maize silk cuticular waxes.

The hydrophobic cuticle is the first line of defense between aerial portions of plants and the external environment. On maize (Zea mays L.) silks, the cuticular cutin matrix is infused with cuticular waxes, consisting of a homologous series of very long-chain fatty acids (VLCFAs), aldehydes, and hydrocarbons. Together with VLC fatty-acyl-CoAs (VLCFA-CoAs), these metabolites serve as precursors, intermediates, and end-products of the cuticular wax biosynthetic pathway. To deconvolute the potentially confounding impacts of the change in silk microenvironment and silk development on this pathway, we profiled cuticular waxes on the silks of the inbreds B73 and Mo17, and their reciprocal hybrids. Multivariate interrogation of these metabolite abundance data demonstrates that VLCFA-CoAs and total free VLCFAs are positively correlated with the cuticular wax metabolome, and this metabolome is primarily affected by changes in the silk microenvironment and plant genotype. Moreover, the genotype effect on the pathway explains the increased accumulation of cuticular hydrocarbons with a concomitant reduction in cuticular VLCFA accumulation on B73 silks, suggesting that the conversion of VLCFA-CoAs to hydrocarbons is more effective in B73 than Mo17. Statistical modeling of the ratios between cuticular hydrocarbons and cuticular VLCFAs reveals a significant role of precursor chain length in determining this ratio. This study establishes the complexity of the product-precursor relationships within the silk cuticular wax-producing network by dissecting both the impact of genotype and the allocation of VLCFA-CoA precursors to different biological processes and demonstrates that longer chain VLCFA-CoAs are preferentially utilized for hydrocarbon biosynthesis.

Nod1-dependent NF-kB activation initiates hematopoietic stem cell specification in response to small Rho GTPases.

Uncovering the mechanisms regulating hematopoietic specification not only would overcome current limitations related to hematopoietic stem and progenitor cell (HSPC) transplantation, but also advance cellular immunotherapies. However, generating functional human induced pluripotent stem cell (hiPSC)-derived HSPCs and their derivatives has been elusive, necessitating a better understanding of the developmental mechanisms that trigger HSPC specification. Here, we reveal that early activation of the Nod1-Ripk2-NF-kB inflammatory pathway in endothelial cells (ECs) primes them to switch fate towards definitive hemogenic endothelium, a pre-requisite to specify HSPCs. Our genetic and chemical embryonic models show that HSPCs fail to specify in the absence of Nod1 and its downstream kinase Ripk2 due to a failure on hemogenic endothelial (HE) programming, and that small Rho GTPases coordinate the activation of this pathway. Manipulation of NOD1 in a human system of definitive hematopoietic differentiation indicates functional conservation. This work establishes the RAC1-NOD1-RIPK2-NF-kB axis as a critical intrinsic inductor that primes ECs prior to HE fate switch and HSPC specification. Manipulation of this pathway could help derive a competent HE amenable to specify functional patient specific HSPCs and their derivatives for the treatment of blood disorders.

An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species.

Understanding the microbial genomic contributors to antimicrobial resistance (AMR) is essential for early detection of emerging AMR infections, a pressing global health threat in human and veterinary medicine. Here we used whole genome sequencing and antibiotic susceptibility test data from 980 disease causing Escherichia coli isolated from companion and farm animals to model AMR genotypes and phenotypes for 24 antibiotics. We determined the strength of genotype-to-phenotype relationships for 197 AMR genes with elastic net logistic regression. Model predictors were designed to evaluate different potential modes of AMR genotype translation into resistance phenotypes. Our results show a model that considers the presence of individual AMR genes and total number of AMR genes present from a set of genes known to confer resistance was able to accurately predict isolate resistance on average (mean F1 score = 98.0%, SD = 2.3%, mean accuracy = 98.2%, SD = 2.7%). However, fitted models sometimes varied for antibiotics in the same class and for the same antibiotic across animal hosts, suggesting heterogeneity in the genetic determinants of AMR resistance. We conclude that an interpretable AMR prediction model can be used to accurately predict resistance phenotypes across multiple host species and reveal testable hypotheses about how the mechanism of resistance may vary across antibiotics within the same class and across animal hosts for the same antibiotic.

Unsupervised contrastive peak caller for ATAC-seq.

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) is a common assay to identify chromatin accessible regions by using a Tn5 transposase that can access, cut, and ligate adapters to DNA fragments for subsequent amplification and sequencing. These sequenced regions are quantified and tested for enrichment in a process referred to as "peak calling." Most unsupervised peak calling methods are based on simple statistical models and suffer from elevated false positive rates. Newly developed supervised deep learning methods can be successful, but they rely on high quality labeled data for training, which can be difficult to obtain. Moreover, though biological replicates are recognized to be important, there are no established approaches for using replicates in the deep learning tools, and the approaches available for traditional methods either cannot be applied to ATAC-seq, where control samples may be unavailable, or are post hoc and do not capitalize on potentially complex, but reproducible signal in the read enrichment data. Here, we propose a novel peak caller that uses unsupervised contrastive learning to extract shared signals from multiple replicates. Raw coverage data are encoded to obtain low-dimensional embeddings and optimized to minimize a contrastive loss over biological replicates. These embeddings are passed to another contrastive loss for learning and predicting peaks and decoded to denoised data under an autoencoder loss. We compared our replicative contrastive learner (RCL) method with other existing methods on ATAC-seq data, using annotations from ChromHMM genomic labels and transcription factor ChIP-seq as noisy truth. RCL consistently achieved the best performance.

Predicting allosteric pockets in protein biological assemblages.

MOTIVATION: Allostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages. RESULTS: Out of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein's global dynamics. AVAILABILITY AND IMPLEMENTATION: APOP is freely available as an open-source code (https://github.com/Ambuj-UF/APOP) and as a web server at https://apop.bb.iastate.edu/.

Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers.

MOTIVATION: Amplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during polymerase chain reaction (PCR) and sequencing. One solution attaches unique molecular identifiers (UMIs) to sample sequences before amplification. Counting UMIs instead of sequences provides unbiased estimates of abundance. While modern methods improve over naïve counting by UMI identity, most do not account for UMI reuse or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences. RESULTS: We introduce Deduplication and Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological amplicon sequences and accurately estimate their deduplicated abundance. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/DormanLab/AmpliCI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CAPG: comprehensive allopolyploid genotyper.

MOTIVATION: Genotyping by sequencing is a powerful tool for investigating genetic variation in plants, but many economically important plants are allopolyploids, where homoeologous similarity obscures the subgenomic origin of reads and confounds allelic and homoeologous SNPs. Recent polyploid genotyping methods use allelic frequencies, rate of heterozygosity, parental cross or other information to resolve read assignment, but good subgenomic references offer the most direct information. The typical strategy aligns reads to the joint reference, performs diploid genotyping within each subgenome, and filters the results, but persistent read misassignment results in an excess of false heterozygous calls. RESULTS: We introduce the Comprehensive Allopolyploid Genotyper (CAPG), which formulates an explicit likelihood to weight read alignments against both subgenomic references and genotype individual allopolyploids from whole-genome resequencing data. We demonstrate CAPG in allotetraploids, where it performs better than Genome Analysis Toolkit's HaplotypeCaller applied to reads aligned to the combined subgenomic references. AVAILABILITY AND IMPLEMENTATION: Code and tutorials are available at https://github.com/Kkulkarni1/CAPG.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Coarse-graining protein structures into their dynamic communities with DCI, a dynamic community identifier.

SUMMARY: A new dynamic community identifier (DCI) is presented that relies upon protein residue dynamic cross-correlations generated by Gaussian elastic network models to identify those residue clusters exhibiting motions within a protein. A number of examples of communities are shown for diverse proteins, including GPCRs. It is a tool that can immediately simplify and clarify the most essential functional moving parts of any given protein. Proteins usually can be subdivided into groups of residues that move as communities. These are usually densely packed local sub-structures, but in some cases can be physically distant residues identified to be within the same community. The set of these communities for each protein are the moving parts. The ways in which these are organized overall can aid in understanding many aspects of functional dynamics and allostery. DCI enables a more direct understanding of functions including enzyme activity, action across membranes and changes in the community structure from mutations or ligand binding. The DCI server is freely available on a web site (https://dci.bb.iastate.edu/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Retrospective evaluation of a dose-dependent effect of angiotensin-converting enzyme inhibitors on long-term outcome in dogs with cardiac disease.

BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEIs) are commonly prescribed in dogs, but the ideal dosage is unknown. HYPOTHESIS/OBJECTIVES: In dogs with cardiac disease, a dose-response relationship exists for ACEIs with respect to long-term outcome. ANIMALS: One hundred forty-four dogs with cardiac disease, 63 with current or prior congestive heart failure. METHODS: Retrospective medical record review. Cox proportional hazards models were used to determine variables associated with 2-year survival or survival from first-onset congestive heart failure (CHF). RESULTS: Median initial ACEI dosage was 0.84 (interquartile range [IQR], 0.56-0.98) mg/kg/day, and 108/144 (75%) of dogs received q12h dosing. No clinically relevant changes in renal function test results, serum electrolyte concentrations, or blood pressure occurred between initial prescription of ACEI and first reevaluation (median, 14 days later). In univariable analysis, higher ACEI dose was associated with increased survival from first-onset CHF (P = .005), and within the subgroup of dogs in CHF at the time of ACEI prescription, higher ACEI dose was associated with improved survival at 2 years (P = .04). In multivariable analysis, q12h dose frequency of ACEI (hazard ratio [HR], 0.30; 95% CI, 0.10-0.88; P = .03) and higher serum potassium concentration at visit 1 (HR, 0.39; 95% CI, 0.16-0.97; P = .04) were predictive of 2-year survival. The ACEIs were well-tolerated, with only 8/144 (5.6%) dogs having ACEI dose decreased or discontinued because of adverse effects. CONCLUSIONS AND CLINICAL IMPORTANCE: Twice daily dose frequency might optimize the cardioprotective benefit of ACEIs.