Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data

FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening

Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. Notably, FaissMolLib employs the highly efficient Faiss search algorithm, outperforming the Tanimoto algorithm in identifying similar molecules with its tighter clustering in scatter plots and lower mean, standard deviation, and variance in key molecular properties. This feature enables FaissMolLib to screen 2.8 million compounds in just 0.05 seconds, offering researchers an efficient, easily deployable solution for virtual screening on laptops and building unique compound databases. This significant advancement holds great potential for accelerating drug discovery efforts and enhancing chemical data analysis. FaissMolLib is freely available at http://liuhaihan.gnway.cc:80. The code and dataset of FaissMolLib are freely available at https://github.com/Superhaihan/FiassMolLib.

Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models

BACKGROUND

Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE).

OBJECTIVE

The purpose of this study was to test whether integrating imaging data into an existing ML model increases its ability to predict major adverse events (MAEs; procedure-related major complications and procedure-related deaths) and lengthy procedures (≥100 minutes).

METHODS

We hypothesized certain features-(1) lead angulation, (2) coil percentage inside the superior vena cava (SVC), and (3) number of overlapping leads in the SVC-detected from a pre-TLE plain anteroposterior chest radiograph (CXR) would improve prediction of MAE and long procedural times. A deep-learning convolutional neural network was developed to automatically detect these CXR features.

RESULTS

A total of 1050 cases were included, with 24 MAEs (2.3%) . The neural network was able to detect (1) heart border with 100% accuracy; (2) coils with 98% accuracy; and (3) acute angle in the right ventricle and SVC with 91% and 70% accuracy, respectively. The following features significantly improved MAE prediction: (1) ≥50% coil within the SVC; (2) ≥2 overlapping leads in the SVC; and (3) acute lead angulation. Balanced accuracy (0.74-0.87), sensitivity (68%-83%), specificity (72%-91%), and area under the curve (AUC) (0.767-0.962) all improved with imaging biomarkers. Prediction of lengthy procedures also improved: balanced accuracy (0.76-0.86), sensitivity (75%-85%), specificity (63%-87%), and AUC (0.684-0.913).

CONCLUSION

Risk prediction tools integrating imaging biomarkers significantly increases the ability of ML models to predict risk of MAE and long procedural time related to TLE.

A web-based survey on the occurrence of emotional blunting in patients with major depressive disorder in Japan: Patient perceptions and attitudes

AIMS

To determine the prevalence and impact of emotional blunting (EB) in patients with major depressive disorder (MDD) in Japan, and identify treatment needs for EB using patients' perceptions and attitudes.

METHODS

Eligible patients in Japan (aged 18-59 years) who reported a diagnosis of MDD and antidepressant medication use for >3 months were eligible to complete an online survey. The primary outcome was the prevalence of EB, self-reported using a validated screening question. Secondary outcomes included the correlation between EB symptoms (measured by the Oxford Depression Questionnaire [ODQ]) and scores on the Patient Health Questionnaire 9-item (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), Work and Social Adjustment Scale (WSAS), and the EuroQol 5-Dimension 5-Levels questionnaire (EQ-5D-5L). Descriptive questions were used to explore patients' perceptions and attitudes toward EB.

RESULTS

In total, 3376 patients were included in the analysis (56% male; 48% aged 50-59 years). Overall, 67.1% of patients self-reported symptoms of EB, with 10% rating these as severe. The mean (SD) ODQ total score was 78.2 (21.5), which increased with worsening EB symptoms. There were correlations between ODQ total scores and the PHQ-9, GAD-7, WSAS, and EQ-5D-5L scores (correlation coefficients: 0.67, 0.55, 0.56, -0.51, respectively; all p < 0.0001). Descriptive analyses showed that one-third of patients reporting EB symptoms did not tell their physician, with two-thirds finding these symptoms distressing and likely to affect recovery.

CONCLUSION

EB is an important clinical issue in Japan that needs to be considered alongside functional recovery when managing treatment of patients with MDD.

Seclidemstat blocks the transcriptional function of multiple FET-fusion oncoproteins

Genes encoding the RNA-binding proteins F US, E WSR1, and T AF15 (FET proteins) are involved in chromosomal translocations in rare sarcomas. FET-rearranged sarcomas are often aggressive malignancies affecting patients of all ages. New therapies are needed. These translocations fuse the 5' portion of the FET gene with a 3' partner gene encoding a transcription factor (TF). The resulting fusion proteins are oncogenic TFs with a FET protein low complexity domain (LCD) and a DNA binding domain. FET fusion proteins have proven stubbornly difficult to target directly and promising strategies target critical co-regulators. One candidate is lysine specific demethylase 1 (LSD1). LSD1 is recruited by multiple FET fusions, including EWSR1::FLI1. LSD1 promotes EWSR1::FLI1 activity and treatment with the noncompetitive inhibitor SP-2509 blocks EWSR1::FLI1 transcriptional function. A similar molecule, seclidemstat (SP-2577), is currently in clinical trials for FET-rearranged sarcomas ( NCT03600649 ). However, whether seclidemstat has pharmacological activity against FET fusions has not been demonstrated. Here, we evaluate the in vitro potency of seclidemstat against multiple FET-rearranged sarcoma cell lines, including Ewing sarcoma, desmoplastic small round cell tumor, clear cell sarcoma, and myxoid liposarcoma. We also define the transcriptomic effects of seclidemstat treatment and evaluated the activity of seclidemstat against FET fusion transcriptional regulation. Seclidemstat showed potent activity in cell viability assays across FET-rearranged sarcomas and disrupted the transcriptional function of all tested fusions. Though epigenetic and targeted inhibitors are unlikely to be effective as a single agents in the clinic, these data suggest seclidemstat remains a promising new treatment strategy for patients with FET-rearranged sarcomas.

SIGNIFICANCE

Here, we show the noncompetitive inhibitor, seclidemstat, has in vitro activity against multiple FET fusion proteins that cause a number of rare and aggressive sarcomas. These data represent one of the largest analyses of FET fusion activity across multiple malignancies and are a valuable resource for those studying FET-rearranged sarcomas.

Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data

MOTIVATION

In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs.

RESULTS

To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest.

AVAILABILITY AND IMPLEMENTATION

STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

Prediction of protein-RNA interactions from single-cell transcriptomic data

Proteins are crucial in regulating every aspect of RNA life, yet understanding their interactions with coding and noncoding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on physico-chemical principles can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). Here, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be predicted from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. The incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long noncoding RNAs, and enables the identification of hub RBPs and RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. The software is freely available at https://github.com/tartaglialabIIT/scRAPID.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

Assessing the quality of constructed technosols enabled holistic monitoring of ecological restoration

The soil quality index (SQI) serves as a general ecological restoration indicator, however, statistics approaches that accurately assess the minimum data set (MDS) for SQI remain susceptible. The present study aims to evaluate the short-term reclamation results at the Ferro-Carvão stream and propose a system for ecological restoration monitoring, by selecting influential attributes and indexing soil quality. We hypothesized that the reclamation activities at the Ferro-Carvão stream, referred to as the "Marco zero" (MZ) area, can bring its soil quality to levels comparable to those of the native area. We collected soil samples at 0-20 and 20-40 cm depths from transects of MZ and reference sites (R1 and R2). Principal component analysis showed the MDS for each soil depth. Permutational analysis of variance, in conjunction with Nonmetric Multidimensional Scaling, exposed relationships between transects of areas. An additive non-linear factorial algorithm allowed SQI assessment. The results indicated a similar soil quality between transects of areas at 0-20 cm depth, whereas a dissimilarity at 20-40 cm. To sum up, reclamation activities allowed MZ-constructed Technosol to present a soil quality similar to native areas. The soil quality assessment at both depths offered insights into reclamation activities' immediate and long-term impacts on the Ferro-Carvão stream. This robust framework effectively monitors ecological restoration progress and guides future efforts in post-mining and post-dam collapse sites.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g., siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Furthermore, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N = 16) or cKp (N = 33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression, the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49), respectively. These findings can be used to inform the identification of hvKp.IMPORTANCEHypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all five of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which, in turn, would assist in optimizing patient care.

Population structure and adaptability analysis of Schizothorax o'connori based on whole-genome resequencing

BACKGROUND

Schizothorax o'connori is an endemic fish distributed in the upper and lower reaches of the Yarlung Zangbo River in China. It has experienced a fourth round of whole gene replication events and is a good model for exploring the genetic differentiation and environmental adaptability of fish in the Qinghai-Tibet Plateau. The uplift of the Qinghai-Tibet Plateau has led to changes in the river system, thereby affecting gene exchange and population differentiation between fish populations. With the release of fish whole genome data, whole genome resequencing has been widely used in genetic evolutionary analysis and screening of selected genes in fish, which can better elucidate the genetic basis and molecular environmental adaptation mechanisms of fish. Therefore, our purpose of this study was to understand the population structure and adaptive characteristics of S. o'connori using the whole-genome resequencing method.

RESULTS

The results showed that 23,602,746 SNPs were identified from seven populations, mostly distributed on chromosomes 2 and 23. There was no significant genetic differentiation between the populations, and the genetic diversity was relatively low. However, the Zangga population could be separated from the Bomi, Linzhi, and Milin populations in the cluster analysis. Based on historical dynamics analysis of the population, the size of the ancestral population of S. o'connori was affected by the late accelerated uplift of the Qinghai Tibet Plateau and the Fourth Glacial Age. The selected sites were mostly enriched in pathways related to DNA repair and energy metabolism.

CONCLUSION

Overall, the whole-genome resequencing analysis provides valuable insights into the population structure and adaptive characteristics of S. o'connori. There was no obvious genetic differentiation at the genome level between the S. o'connori populations upstream and downstream of the Yarlung Zangbo River. The current distribution pattern and genetic diversity are influenced by the late accelerated uplift of the Qinghai Tibet Plateau and the Fourth Ice Age. The selected sites of S. o'connori are enriched in the energy metabolism and DNA repair pathways to adapt to the low temperature and strong ultraviolet radiation environment at high altitude.

RAMZIS: a bioinformatic toolkit for rigorous assessment of the alterations to glycoprotein composition that occur during biological processes

Motivation

Glycosylation elaborates the structures and functions of glycoproteins; glycoproteins are common post-translationally modified proteins and are heterogeneous and non-deterministically synthesized as an evolutionarily driven mechanism that elaborates the functions of glycosylated gene products. Glycoproteins, accounting for approximately half of all proteins, require specialized proteomics data analysis methods due to micro- and macro-heterogeneities as a given glycosite can be divided into several glycosylated forms, each of which must be quantified. Sampling of heterogeneous glycopeptides is limited by mass spectrometer speed and sensitivity, resulting in missing values. In conjunction with the low sample size inherent to glycoproteomics, a specialized toolset is needed to determine if observed changes in glycopeptide abundances are biologically significant or due to data quality limitations.

Results

We developed an R package, Relative Assessment of m/z Identifications by Similarity (RAMZIS), that uses similarity metrics to guide researchers to a more rigorous interpretation of glycoproteomics data. RAMZIS uses a permutation test to generate contextual similarity, which assesses the quality of mass spectral data and outputs a graphical demonstration of the likelihood of finding biologically significant differences in glycosylation abundance datasets. Investigators can assess dataset quality, holistically differentiate glycosites, and identify which glycopeptides are responsible for glycosylation pattern change. RAMZIS is validated by theoretical cases and a proof-of-concept application. RAMZIS enables comparison between datasets too stochastic, small, or sparse for interpolation while acknowledging these issues in its assessment. Using this tool, researchers will be able to rigorously define the role of glycosylation and the changes that occur during biological processes.

Availability and implementation

https://github.com/WillHackett22/RAMZIS.

Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China

Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using data-mining methods. Most isolates within same hosts possessed the same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Supervised machine learning classifiers revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials, primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs.

A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT

PURPOSE

To develop a machine learning-based methodology for patient-specific radiation dosimetry in thoracic and abdomen CT.

METHODS

Three hundred and thirty-one thoracoabdominal radiotherapy-planning CT examinations with the respective organ/patient contours were collected retrospectively for the development and validation of segmentation 3D-UNets. Moreover, 97 diagnostic thoracic and 89 diagnostic abdomen CT examinations were collected retrospectively. For each of the diagnostic CT examinations, personalized MC dosimetry was performed. The data derived from MC simulations along with the respective CT data were used for the training and validation of a dose prediction deep neural network (DNN). An algorithm was developed to utilize the trained models and perform patient-specific organ dose estimates for thoracic and abdomen CT examinations. The doses estimated with the DNN were compared with the respective doses derived from MC simulations. A paired t-test was conducted between the DNN and MC results. Furthermore, the time efficiency of the proposed methodology was assessed.

RESULTS

The mean percentage differences (range) between DNN and MC dose estimates for the lungs, liver, spleen, stomach, and kidneys were 7.2 % (0.2-24.1 %), 5.5 % (0.4-23.0 %), 7.9 % (0.6-22.3 %), 6.9 % (0.0-23.0 %) and 6.7 % (0.3-22.6 %) respectively. The differences between DNN and MC dose estimates were not significant (p-value = 0.12). Moreover, the mean processing time of the proposed workflow was 99 % lower than the respective time needed for MC-based dosimetry.

CONCLUSIONS

The proposed methodology can be used for rapid and accurate patient-specific dosimetry in chest and abdomen CT.

Truncating the spliceosomal 'rope protein' Prp45 results in Htz1 dependent phenotypes

Spliceosome assembly contributes an important but incompletely understood aspect of splicing regulation. Prp45 is a yeast splicing factor which runs as an extended fold through the spliceosome, and which may be important for bringing its components together. We performed a whole genome analysis of the genetic interaction network of the truncated allele of PRP45 (prp45(1-169)) using synthetic genetic array technology and found chromatin remodellers and modifiers as an enriched category. In agreement with related studies, H2A.Z-encoding HTZ1, and the components of SWR1, INO80, and SAGA complexes represented prominent interactors, with htz1 conferring the strongest growth defect. Because the truncation of Prp45 disproportionately affected low copy number transcripts of intron-containing genes, we prepared strains carrying intronless versions of SRB2, VPS75, or HRB1, the most affected cases with transcription-related function. Intron removal from SRB2, but not from the other genes, partly repaired some but not all the growth phenotypes identified in the genetic screen. The interaction of prp45(1-169) and htz1Δ was detectable even in cells with SRB2 intron deleted (srb2Δi). The less truncated variant, prp45(1-330), had a synthetic growth defect with htz1Δ at 16°C, which also persisted in the srb2Δi background. Moreover, htz1Δ enhanced prp45(1-330) dependent pre-mRNA hyper-accumulation of both high and low efficiency splicers, genes ECM33 and COF1, respectively. We conclude that while the expression defects of low expression intron-containing genes contribute to the genetic interactome of prp45(1-169), the genetic interactions between prp45 and htz1 alleles demonstrate the sensitivity of spliceosome assembly, delayed in prp45(1-169), to the chromatin environment.

Viral expansion after transfer is a primary driver of influenza A virus transmission bottlenecks

For many viruses, narrow bottlenecks acting during transmission sharply reduce genetic diversity in a recipient host relative to the donor. Since genetic diversity represents adaptive potential, such losses of diversity are though to limit the opportunity for viral populations to undergo antigenic change and other adaptive processes. Thus, a detailed picture of evolutionary dynamics during transmission is critical to understanding the forces driving viral evolution at an epidemiologic scale. To advance this understanding, we used a novel barcoded virus library and a guinea pig model of transmission to decipher where in the transmission process diversity is lost for influenza A viruses. In inoculated guinea pigs, we show that a high level of viral genetic diversity is maintained across time. Continuity in the barcodes detected furthermore indicates that stochastic effects are not pronounced within inoculated hosts. Importantly, in both aerosol-exposed and direct contact-exposed animals, we observed many barcodes at the earliest time point(s) positive for infectious virus, indicating robust transfer of diversity through the environment. This high viral diversity is short-lived, however, with a sharp decline seen 1-2 days after initiation of infection. Although major losses of diversity at transmission are well described for influenza A virus, our data indicate that events that occur following viral transfer and during the earliest stages of natural infection have a predominant role in this process. This finding suggests that immune selection may have greater opportunity to operate during influenza A transmission than previously recognized.

Globally shared TCR repertoires within the tumor-infiltrating lymphocytes of patients with metastatic gynecologic cancer

Gynecologic cancer, including ovarian cancer and endometrial cancer, is characterized by morphological and molecular heterogeneity. Germline and somatic testing are available for patients to screen for pathogenic variants in genes such as BRCA1/2. Tissue expression levels of immunogenomic markers such as PD-L1 are also being used in clinical research. The basic therapeutic approach to gynecologic cancer combines surgery with chemotherapy. Immunotherapy, while not yet a mainstream treatment for gynecologic cancers, is advancing, with Dostarlimab recently receiving approval as a treatment for endometrial cancer. The goal remains to harness stimulated immune cells in the bloodstream to eradicate multiple metastases, a feat currently deemed challenging in a typical clinical setting. For the discovery of novel immunotherapy-based tumor targets, tumor-infiltrating lymphocytes (TILs) give a key insight on tumor-related immune activities by providing T cell receptor (TCR) sequences. Understanding the TCR repertoires of TILs in metastatic tissues and the circulation is important from an immunotherapy standpoint, as a subset of T cells in the blood have the potential to help kill tumor cells. To explore the relationship between distant tissue biopsy regions and blood circulation, we investigated the TCR beta chain (TCRβ) in bulk tumor and matched blood samples from 39 patients with gynecologic cancer. We found that the TCR clones of TILs at different tumor sites were globally shared within patients and had high overlap with the TCR clones in peripheral blood.

Integrative analysis reveals therapeutic potential of pyrvinium pamoate in Merkel cell carcinoma

Merkel Cell Carcinoma (MCC) is a highly aggressive neuroendocrine cutaneous malignancy arising from either ultraviolet-induced mutagenesis or Merkel cell polyomavirus (MCPyV) integration. It is the only known neuroendocrine tumor (NET) with a virus etiology. Despite extensive research, our understanding of the molecular mechanisms driving the transition from normal cells to MCC remains limited. To address this knowledge gap, we assessed the impact of inducible MCPyV T antigens into normal human fibroblasts by performing RNA sequencing. Our findings suggested that the WNT signaling pathway plays a critical role in the development of MCC. To test this model, we bioinformatically evaluated various perturbagens for their ability to reverse the MCC gene expression signature and identified pyrvinium pamoate, an FDA-approved anthelminthic drug known for its anti-tumor potential in multiple cancers. Leveraging transcriptomic, network, and molecular analyses, we found that pyrvinium effectively targets multiple MCC vulnerabilities. Specifically, pyrvinium not only reverses the neuroendocrine features of MCC by modulating canonical and non-canonical WNT signaling pathways but also inhibits cancer cell growth by activating the p53-mediated apoptosis pathway, disrupting mitochondrial function, and inducing endoplasmic reticulum (ER) stress. Pyrvinium also effectively inhibits tumor growth in an MCC mouse xenograft model. These findings offer new avenues for the development of therapeutic strategies for neuroendocrine cancer and highlight the utility of pyrvinium as a potential treatment for MCC.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combination of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g. siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Further, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combination of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N=16) or cKp (N=33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥ 4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49) respectively. These findings can be used to inform the identification of hvKp.

Importance

Hypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all 5 of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥ 4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which in turn would assist in optimizing patient care.

A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis

Background

Psoriasis is a chronic immune-mediated inflammatory systemic disease with skin manifestations characterized by erythematous, scaly, itchy and/or painful plaques resulting from hyperproliferation of keratinocytes. Certolizumab pegol [CZP], a PEGylated antigen binding fragment of a humanized monoclonal antibody against TNF-alpha, is approved for the treatment of moderate-to-severe plaque psoriasis. Patients with psoriasis present clinical and molecular variability, affecting response to treatment. Herein, we utilized an in silico approach to model the effects of CZP in a virtual population (vPop) with moderate-to-severe psoriasis. Our proof-of-concept study aims to assess the performance of our model in generating a vPop and defining CZP response variability based on patient profiles.

Methods

We built a quantitative systems pharmacology (QSP) model of a clinical trial-like vPop with moderate-to-severe psoriasis treated with two dosing schemes of CZP (200 mg and 400 mg, both every two weeks for 16 weeks, starting with a loading dose of CZP 400 mg at weeks 0, 2, and 4). We applied different modelling approaches: (i) an algorithm to generate vPop according to reference population values and comorbidity frequencies in real-world populations; (ii) physiologically based pharmacokinetic (PBPK) models of CZP dosing schemes in each virtual patient; and (iii) systems biology-based models of the mechanism of action (MoA) of the drug.

Results

The combination of our different modelling approaches yielded a vPop distribution and a PBPK model that aligned with existing literature. Our systems biology and QSP models reproduced known biological and clinical activity, presenting outcomes correlating with clinical efficacy measures. We identified distinct clusters of virtual patients based on their psoriasis-related protein predicted activity when treated with CZP, which could help unravel differences in drug efficacy in diverse subpopulations. Moreover, our models revealed clusters of MoA solutions irrespective of the dosing regimen employed.

Conclusion

Our study provided patient specific QSP models that reproduced clinical and molecular efficacy features, supporting the use of computational methods as modelling strategy to explore drug response variability. This might shed light on the differences in drug efficacy in diverse subpopulations, especially useful in complex diseases such as psoriasis, through the generation of mechanistically based hypotheses.

The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer

Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given the expensive and complexity of biological experiments, computational methods serve as effective tools in predicting cancer drug sensitivity. In this study, we proposed a novel method called Multi-Omics Integrated Collective Variational Autoencoders (MOICVAE), which leverages integrated omics knowledge, including genomic and transcriptomic data, to fill in missing cancer-drug associations and enhance drug sensitivity prediction. Our method employs an encoder-decoder network to learn latent feature representations from cell lines. These learned feature vectors are then fed into a collective variational autoencoder network to train an association matrix. We evaluated MOICVAE on the GDSC and CCLE benchmark datasets using 10-fold cross-validation and achieved impressive AUCs of 0.856 and 0.808, respectively, outperforming state-of-the-art methods. Furthermore, on the TCGA dataset, consisting of 25 drugs across 7 cancer types, MOICVAE exhibited an average AUC of 0.91 in predicting drug sensitivity. Additionally, significant differences were observed in survival, tumor inflammatory assessment, and tumor microenvironment between the predicted drug-sensitive and drug-resistant groups. These results are consistent with predictions made on the METABRIC dataset. Moreover, we discovered that fusing omics data based on mRNA and CNV (copy number variations) yielded superior results in drug sensitivity prediction. MOICVAE not only achieved higher accuracy in drug sensitivity prediction but also provided additional value for combining immunotherapy with chemotherapy, offering patients with more precise treatment options. The code and dataset for MOICVAE are freely available at https://github.com/wanggnoc/MOICVAE.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.

RAMZIS: a bioinformatic toolkit for rigorous assessment of the alterations to glycoprotein structure that occur during biological processes

Motivation

Glycosylation elaborates the structures and functions of glycoproteins; glycoproteins are common post-translationally modified proteins and are heterogeneous and non-deterministically syn-thesized as an evolutionarily driven mechanism that elaborates the functions of glycosylated gene products. While glycoproteins account for approximately half of all proteins, their macro- and micro-heterogeneity requires specialized proteomics data analysis methods as a given glycosite can be divided into several glycosylated forms, each of which must be quantified. Sampling of heterogeneous glycopeptides is limited by mass spectrometer speed and sensitivity, resulting in missing values. In conjunction with the low sample size inherent to glycoproteomics, this necessitated specialized statistical metrics to identify if observed changes in glycopeptide abundances are biologically significant or due to data quality limitations.

Results

We developed an R package, Relative Assessment of m/z Identifications by Similarity (RAMZIS), that uses similarity metrics to guide biomedical researchers to a more rigorous interpretation of glycoproteomics data. RAMZIS uses contextual similarity to assess the quality of mass spectral data and generates graphical output that demonstrates the likelihood of finding biologically significant differences in glycosylation abundance dataset. Investigators can assess dataset quality, holistically differentiate glycosites, and identify which glycopeptides are responsible for glycosylation pattern expression change. Herein RAMZIS approach is validated by theoretical cases and by a proof-of-concept application. RAMZIS enables comparison between datasets too stochastic, small, or sparse for interpolation while acknowledging these issues in its assessment. Using our tool, researchers will be able to rigor-ously define the role of glycosylation and the changes that occur during biological processes.

Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA

Circulating tumour DNA (ctDNA) can be used to detect and profile residual tumour cells persisting after curative intent therapy1. The study of large patient cohorts incorporating longitudinal plasma sampling and extended follow-up is required to determine the role of ctDNA as a phylogenetic biomarker of relapse in early-stage non-small-cell lung cancer (NSCLC). Here we developed ctDNA methods tracking a median of 200 mutations identified in resected NSCLC tissue across 1,069 plasma samples collected from 197 patients enrolled in the TRACERx study2. A lack of preoperative ctDNA detection distinguished biologically indolent lung adenocarcinoma with good clinical outcome. Postoperative plasma analyses were interpreted within the context of standard-of-care radiological surveillance and administration of cytotoxic adjuvant therapy. Landmark analyses of plasma samples collected within 120 days after surgery revealed ctDNA detection in 25% of patients, including 49% of all patients who experienced clinical relapse; 3 to 6 monthly ctDNA surveillance identified impending disease relapse in an additional 20% of landmark-negative patients. We developed a bioinformatic tool (ECLIPSE) for non-invasive tracking of subclonal architecture at low ctDNA levels. ECLIPSE identified patients with polyclonal metastatic dissemination, which was associated with a poor clinical outcome. By measuring subclone cancer cell fractions in preoperative plasma, we found that subclones seeding future metastases were significantly more expanded compared with non-metastatic subclones. Our findings will support (neo)adjuvant trial advances and provide insights into the process of metastatic dissemination using low-ctDNA-level liquid biopsy.

Implementing voluntary assisted dying laws in healthcare: Exploring beliefs to identify implementation hurdles

The number of countries introducing voluntary assisted dying (VAD) laws is increasing. Actively taking steps to end a person's life is contentious so implementing these laws into healthcare services presents unique challenges. Theoretically underpinned by the Advocacy Coalition Framework, this study identified the beliefs of classes of stakeholders who engaged with the parliamentary process associated with the introduction of VAD laws in Queensland, Australia. Submissions about VAD made to a parliamentary inquiry were allocated to a class and qualitatively analysed to identify underlying beliefs. The data were then subjected to statistical analysis including nonmetric dimensional scaling and one-way analysis of similarity. Data visualisation techniques were used to generate a chord map and heatmap, to identify the belief types, as well as similarities and differences between beliefs and among classes. Fourteen different beliefs were identified in the 91 reviewed submissions. Six were deep core beliefs and eight were policy core beliefs. Beliefs could be associated with a positive or negative sentiment toward VAD. In this study, the class of Health Services expressed more negative sentiments to VAD than neutral or positive sentiments. The sentiments expressed by the class of Health Professionals were equally divided between positive, neutral and negative. These findings provide important insights for implementors as laws become operational. In particular, for organisations that provide health services, clear articulation of their stance in relevant policy and guidance documents is recommended.

Clustered PHD domains in KMT2/MLL proteins are attracted by H3K4me3 and H3 acetylation-rich active promoters and enhancers

Histone lysine-specific methyltransferase 2 (KMT2A-D) proteins, alternatively called mixed lineage leukemia (MLL1-4) proteins, mediate positive transcriptional memory. Acting as the catalytic subunits of human COMPASS-like complexes, KMT2A-D methylate H3K4 at promoters and enhancers. KMT2A-D contain understudied highly conserved triplets and a quartet of plant homeodomains (PHDs). Here, we show that all clustered (multiple) PHDs localize to the well-defined loci of H3K4me3 and H3 acetylation-rich active promoters and enhancers. Surprisingly, we observe little difference in binding pattern between PHDs from promoter-specific KMT2A-B and enhancer-specific KMT2C-D. Fusion of the KMT2A CXXC domain to the PHDs drastically enhances their preference for promoters over enhancers. Hence, the presence of CXXC domains in KMT2A-B, but not KMT2C-D, may explain the promoter/enhancer preferences of the full-length proteins. Importantly, targets of PHDs overlap with KMT2A targets and are enriched in genes involved in the cancer pathways. We also observe that PHDs of KMT2A-D are mutated in cancer, especially within conserved folding motifs (Cys4HisCys2Cys/His). The mutations cause a domain loss-of-function. Taken together, our data suggest that PHDs of KMT2A-D guide the full-length proteins to active promoters and enhancers, and thus play a role in positive transcriptional memory.

Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer's disease

Human middle temporal gyrus (MTG) is a vulnerable brain region in early Alzheimer's disease (AD), but little is known about the molecular mechanisms underlying this regional vulnerability. Here we utilize the 10 × Visium platform to define the spatial transcriptomic profile in both AD and control (CT) MTG. We identify unique marker genes for cortical layers and the white matter, and layer-specific differentially expressed genes (DEGs) in human AD compared to CT. Deconvolution of the Visium spots showcases the significant difference in particular cell types among cortical layers and the white matter. Gene co-expression analyses reveal eight gene modules, four of which have significantly altered co-expression patterns in the presence of AD pathology. The co-expression patterns of hub genes and enriched pathways in the presence of AD pathology indicate an important role of cell-cell-communications among microglia, oligodendrocytes, astrocytes, and neurons, which may contribute to the cellular and regional vulnerability in early AD. Using single-molecule fluorescent in situ hybridization, we validated the cell-type-specific expression of three novel DEGs (e.g., KIF5A, PAQR6, and SLC1A3) and eleven previously reported DEGs associated with AD pathology (i.e., amyloid beta plaques and intraneuronal neurofibrillary tangles or neuropil threads) at the single cell level. Our results may contribute to the understanding of the complex architecture and neuronal and glial response to AD pathology of this vulnerable brain region.

Evolving ribonucleocapsid assembly/packaging signals in the genomes of the human and animal coronaviruses: targeting, transmission and evolution

A world-wide COVID-19 pandemic intensified strongly the studies of molecular mechanisms related to the coronaviruses. The origin of coronaviruses and the risks of human-to-human, animal-to-human and human-to-animal transmission of coronaviral infections can be understood only on a broader evolutionary level by detailed comparative studies. In this paper, we studied ribonucleocapsid assembly-packaging signals (RNAPS) in the genomes of all seven known pathogenic human coronaviruses, SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-HKU1, HCoV-229E and HCoV-NL63 and compared them with RNAPS in the genomes of the related animal coronaviruses including SARS-Bat-CoV, MERS-Camel-CoV, MHV, Bat-CoV MOP1, TGEV and one of camel alphacoronaviruses. RNAPS in the genomes of coronaviruses were evolved due to weakly specific interactions between genomic RNA and N proteins in helical nucleocapsids. Combining transitional genome mapping and Jaccard correlation coefficients allows us to perform the analysis directly in terms of underlying motifs distributed over the genome. In all coronaviruses, RNAPS were distributed quasi-periodically over the genome with the period about 54 nt biased to 57 nt and to 51 nt for the genomes longer and shorter than that of SARS-CoV, respectively. The comparison with the experimentally verified packaging signals for MERS-CoV, MHV and TGEV proved that the distribution of particular motifs is strongly correlated with the packaging signals. We also found that many motifs were highly conserved in both characters and positioning on the genomes throughout the lineages that make them promising therapeutic targets. The mechanisms of encapsidation can affect the recombination and co-infection as well.Communicated by Ramaswamy H. Sarma.

Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors

Background

In most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models.

Results

Among the 8 models evaluated in the “stratified” 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91–0.96, 0.81–0.88, and 80.1–86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77–0.82, F-score 0.53–0.69, and B. Acc. 60.0–68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%).

Conclusions

Logistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.

Heavy Chain Constant Region Usage in Antibodies to Peptidylarginine Deiminase 4 as a Marker of Disease Subsets in Rheumatoid Arthritis

OBJECTIVE

The study of autoantibody isotypes in autoimmune diseases is useful for identifying clinically relevant endotypes. This study was undertaken to study the prevalence and clinical significance of different isotypes and IgG subclasses of anti-peptidylarginine deiminase 4 (anti-PAD4) autoantibodies in individuals with rheumatoid arthritis (RA).

METHODS

In 196 RA subjects and 64 healthy controls, anti-PAD4 antibody types were determined using enzyme-linked immunosorbent assay. We investigated associations between anti-PAD4 antibodies and clinical outcomes, and relevant features were confirmed in an independent RA cohort.

RESULTS

Anti-PAD4 IgG1, anti-PAD4 IgG2, anti-PAD4 IgG3, anti-PAD4 IgG4, anti-PAD4 IgA, and anti-PAD4 IgE antibodies were more frequent in RA patients than healthy controls (P < 0.001). Anti-PAD4 IgG1, anti-PAD4 IgG3, and anti-PAD4 IgE were associated with distinct clinical features. Anti-PAD4 IgG1 was predictive of progressive radiographic joint damage (odds ratio [OR] 4.88, P = 0.005), especially in RA patients without baseline joint damage (40% versus 0%, P = 0.003) or in those negative for anti-cyclic citrullinated peptide and/or rheumatoid factor (OR 32; P = 0.009). IgG1 was also associated with higher levels of C-reactive protein (P = 0.006) and interleukin-6 (P = 0.021). RA patients with anti-PAD4 IgG3 had higher baseline joint damage scores (median Sharp/van der Heijde score 13 versus 7, P = 0.046), while those with anti-PAD4 IgE had higher Disease Activity Score in 28 joints (median 4.0 versus 3.5, P = 0.025), more frequent rheumatoid nodules (31% versus 16%, P = 0.025), and more frequent interstitial lung disease (ground-glass opacification) (24% versus 9%, P = 0.014). Anti-PAD4 IgG1 antibody associations with joint damage were corroborated in an independent RA cohort.

CONCLUSION

Anti-PAD4 IgG1, anti-PAD4 IgG3, and anti-PAD4 IgE antibodies identify discrete disease subsets in RA, suggesting that heavy chain usage drives distinct effector mechanisms of anti-PAD4 antibodies in RA.

Protecting boreal caribou habitat can help conserve biodiversity and safeguard large quantities of soil carbon in Canada

Boreal caribou require large areas of undisturbed habitat for persistence. They are listed as threatened with the risk of extinction in Canada because of landscape changes induced by human activities and resource extraction. Here we ask: Can the protection of habitat for boreal caribou help Canada meet its commitments under the United Nations Convention on Biological Diversity and United Nations Framework Convention on Climate Change? We identified hotspots of high conservation value within the distribution of boreal caribou based on: (1) three measures of biodiversity for at risk species (species richness, unique species and taxonomic diversity); (2) climate refugia or areas forecasted to remain unchanged under climate change; and, (3) areas of high soil carbon that could add to Canada's greenhouse gas emissions if released into the atmosphere. We evaluated the overlap among hotspot types and how well hotspots were represented in Canada's protected and conserved areas network. While hotspots are widely distributed across the boreal caribou distribution, with nearly 80% of the area falling within at least one hotspot type, only 3% of the distribution overlaps three or more hotspots. Moreover, the protected and conserved areas network only captures about 10% of all hotspots within the boreal caribou distribution. While the protected and conserved areas network adequately represents hotspots with high numbers of at risk species, areas occupied by unique species, as well as the full spectrum of areas occupied by different taxa, are underrepresented. Climate refugia and soil carbon hotspots also occur at lower percentages than expected. These findings illustrate the potential co-benefits of habitat protection for caribou to biodiversity and ecosystem services and suggest caribou may be a good proxy for future protected areas planning and for developing effective conservation strategies in regional assessments.

Multi-objective optimization can balance trade-offs among boreal caribou, biodiversity, and climate change objectives when conservation hotspots do not overlap

The biodiversity and climate change crises have led countries-including Canada-to commit to protect more land and inland waters and to stabilize greenhouse gas concentrations. Canada is also obligated to recover populations of at-risk species, including boreal caribou. Canada has the opportunity to expand its protected areas network to protect hotspots of high value for biodiversity and climate mitigation. However, co-occurrence of hotspots is rare. Here we ask: is it possible to expand the network to simultaneously protect areas important for boreal caribou, other species at risk, climate refugia, and carbon stores? We used linear programming to prioritize areas for protection based on these conservation objectives, and assessed how prioritization for multiple, competing objectives affected the outcome for each individual objective. Our multi-objective approach produced reasonably strong representation of value across objectives. Although trade-offs were required, the multi-objective outcome was almost always better than when we ignored one objective to maximize value for another, highlighting the risk of assuming that a plan based on one objective will also result in strong outcomes for others. Multi-objective optimization approaches could be used to plan for protected areas networks that address biodiversity and climate change objectives, even when hotspots do not co-occur.

Comparative single-cell transcriptomes of dose and time dependent epithelial–mesenchymal spectrums

Epithelial–mesenchymal transition (EMT) is a cellular process involved in development and disease progression. Intermediate EMT states were observed in tumors and fibrotic tissues, but previous in vitro studies focused on time-dependent responses with single doses of signals; it was unclear whether single-cell transcriptomes support stable intermediates observed in diseases. Here, we performed single-cell RNA-sequencing with human mammary epithelial cells treated with multiple doses of TGF-β. We found that dose-dependent EMT harbors multiple intermediate states at nearly steady state. Comparisons of dose- and time-dependent EMT transcriptomes revealed that the dose-dependent data enable higher sensitivity to detect genes associated with EMT. We identified cell clusters unique to time-dependent EMT, reflecting cells en route to stable states. Combining dose- and time-dependent cell clusters gave rise to accurate prognosis for cancer patients. Our transcriptomic data and analyses uncover a stable EMT continuum at the single-cell resolution, and complementary information of two types of single-cell experiments.

Pyridazinones and Structurally Related Derivatives with Anti-Inflammatory Activity

Persistent inflammation contributes to a number of diseases; therefore, control of the inflammatory response is an important therapeutic goal. In an effort to identify novel anti-inflammatory compounds, we screened a library of pyridazinones and structurally related derivatives that were used previously to identify N-formyl peptide receptor (FPR) agonists. Screening of the compounds for their ability to inhibit lipopolysaccharide (LPS)-induced nuclear factor κB (NF-κB) transcriptional activity in human THP1-Blue monocytic cells identified 48 compounds with anti-inflammatory activity. Interestingly, 34 compounds were FPR agonists, whereas 14 inhibitors of LPS-induced NF-κB activity were not FPR agonists, indicating that they inhibited different signaling pathways. Further analysis of the most potent inhibitors showed that they also inhibited LPS-induced production of interleukin 6 (IL-6) by human MonoMac-6 monocytic cells, again verifying their anti-inflammatory properties. Structure–activity relationship (SAR) classification models based on atom pair descriptors and physicochemical ADME parameters were developed to achieve better insight into the relationships between chemical structures of the compounds and their biological activities, and we found that there was little correlation between FPR agonist activity and inhibition of LPS-induced NF-κB activity. Indeed, Cmpd43, a well-known pyrazolone-based FPR agonist, as well as FPR1 and FPR2 peptide agonists had no effect on the LPS-induced NF-κB activity in THP1-Blue cells. Thus, some FPR agonists reported to have anti-inflammatory activity may actually mediate their effects through FPR-independent pathways, as it is suggested by our results with this series of compounds. This could explain how treatment with some agonists known to be inflammatory (i.e., FPR1 agonists) could result in anti-inflammatory effects. Further research is clearly needed to define the molecular targets of pyridazinones and structurally related compounds with anti-inflammatory activity and to define their relationships (if any) to FPR signaling events.

Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models

COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs.

The lung microbiome in HIV-positive patients with active pulmonary tuberculosis

Tuberculosis poses one of the greatest infectious disease threats of our time, especially when associated with human immunodeficiency virus (HIV) infection. Very little data is available on the lung microbiome in pulmonary tuberculosis (PTB) in HIV-positive patients. Three patient cohorts were studied: (i) HIV-positive with no respiratory disease (control cohort), (ii) HIV-positive with pneumonia and (iii) HIV-positive with PTB. Sputum specimens were collected in all patients and where possible a paired BALF was collected. DNA extraction was performed using the QIAamp DNA mini kit (QIAGEN, Germany) and extracted DNA specimens were sent to Inqaba Biotechnical Industries (Pty) Ltd for 16S rRNA gene sequence analysis using the Illumina platform (Illumina Inc, USA). Data analysis was performed using QIMME II and R Studio version 3.6.2 (2020). The lung microbiomes of patients with PTB, in the context of HIV co-infection, were dominated by Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. Loss of biodiversity and dysbiosis was found in these patients when compared to the HIV-positive control cohort. Microbial community structure was also distinct from the control cohort, with the dominance of genera such as Achromobacter, Mycobacterium, Acinetobacter, Stenotrophomonas and Pseudomonas in those patients with PTB. This is the first study to describe the lung microbiome in patients with HIV and PTB co-infection and to compare findings with an HIV-positive control cohort. The lung microbiomes of patients with HIV and PTB were distinct from the HIV-positive control cohort without PTB, with an associated loss of microbial diversity.

Exploring disease interrelationships in patients with lymphatic disorders: A single center retrospective experience

BACKGROUND

The lymphatic contribution to the circulation is of paramount importance in regulating fluid homeostasis, immune cell trafficking/activation and lipid metabolism. In comparison to the blood vasculature, the impact of the lymphatics has been underappreciated, both in health and disease, likely due to a less well-delineated anatomy and function. Emerging data suggest that lymphatic dysfunction can be pivotal in the initiation and development of a variety of diseases across broad organ systems. Understanding the clinical associations between lymphatic dysfunction and non-lymphatic morbidity provides valuable evidence for future investigations and may foster the discovery of novel biomarkers and therapies.

METHODS

We retrospectively analysed the electronic medical records of 724 patients referred to the Stanford Center for Lymphatic and Venous Disorders. Patients with an established lymphatic diagnosis were assigned to groups of secondary lymphoedema, lipoedema or primary lymphovascular disease. Individuals found to have no lymphatic disorder were served as the non-lymphatic controls. The prevalence of comorbid conditions was enumerated. Pairwise co-occurrence pattern analyses, validated by Jaccard similarity tests, was utilised to investigate disease-disease interrelationships.

RESULTS

Comorbidity analyses underscored the expected relationship between the presence of secondary lymphoedema and those diseases that damage the lymphatics. Cardiovascular conditions were common in all lymphatic subgroups. Additionally, statistically significant alteration of disease-disease interrelationships was noted in all three lymphatic categories when compared to the control population.

CONCLUSIONS

The presence or absence of a lymphatic disease significantly influences disease interrelationships in the study cohorts. As a physiologic substrate, the lymphatic circulation may be an underappreciated participant in disease pathogenesis. These relationships warrant further, prospective scrutiny and study.

Identifying the Sources of Intestinal Colonization With Extended-Spectrum β-Lactamase-Producing Escherichia coli in Healthy Infants in the Community

The prevalence of fecal colonization with extended-spectrum β-lactamase-producing Escherichia coli (ESBL-Ec) among children in low- and middle-income countries is alarmingly high. This study aimed to identify the sources of ESBL-Ec colonization in children &lt; 1 year old through comparative analysis of E. coli isolates from child stool, child’s mother stool, and point-of-use drinking water from 46 rural households in Bangladesh. The pairwise similarity in antibiotic susceptibility of E. coli from all three sources was evaluated, followed by phylogenetic clustering using enterobacterial repetitive intergenic consensus polymerase chain reaction and whole-genome sequence analysis of the isolates. Matching antibiotic susceptibility and enterobacterial repetitive intergenic consensus polymerase chain reaction patterns were found among ESBL-Ec isolates from child–mother dyads of 24 and 11 households, respectively, from child–water dyads of 5 and 4 households, respectively, and from child–mother–water triads of 3 and 4 households, respectively. Whole-genome sequence analysis of 30 isolates from 10 households revealed that ESBL-Ec from children in five households (50%) was clonally related to ESBL-Ec either from their mothers (2 households), drinking water sources (2 households), or both mother and drinking-water sources (1 household) based on serotype, phylogroup, sequence type, antibiotic resistance genes, mobile genetic elements, core single-nucleotide polymorphisms, and whole-genome multilocus sequence typing. Overall, this study provides empirical evidence that ESBL-Ec colonization in children is linked to the colonization status of mothers and exposure to the household environments contaminated with ESBL-Ec. Interventions such as improved hygiene practices and a safe drinking water supply may help reduce the transmission of ESBL-Ec at the household level.

A non-parametric model: free analysis of actigraphic recordings of acute insomnia patients

Both parametric and non-parametric approaches to time-series analysis have advantages and drawbacks. Parametric methods, although powerful and widely used, can yield inconsistent results due to the oversimplification of the observed phenomena. They require the setting of arbitrary constants for their creation and refinement, and, although these constants relate to assumptions about the observed systems, it can lead to erroneous results when treating a very complex problem with a sizable list of unknowns. Their non-parametric counterparts, instead, are more widely applicable but present a higher detrimental sensitivity to noise and low density in the data. For the case of approximately periodic phenomena, such as human actigraphic time series, parametric methods are widely used and concepts such as acrophase are key in chronobiology, especially when studying healthy and diseased human populations. In this work, we present a non-parametric method of analysis of actigraphic time series from insomniac patients and healthy age-matched controls. The method is fully data-driven, reproduces previous results in the context of activity offset delay and, crucially, extends the concept of acrophase not only to circadian but also for ultradian spectral components.

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. There is an urgent need to develop novel anti-AD therapies; however, drug discovery is a time-consuming, expensive, and high-risk process. Drug repositioning, on the other hand, is an attractive approach to identify drugs for AD treatment. Thus, we developed a novel deep learning method called DOTA (Drug repositioning approach using Optimal Transport for Alzheimer's disease) to repurpose effective FDA-approved drugs for AD. Specifically, DOTA consists of two major autoencoders: (1) a multi-modal autoencoder to integrate heterogeneous drug information and (2) a Wasserstein variational autoencoder to identify effective AD drugs. Using our approach, we predict that antipsychotic drugs with circadian effects, such as quetiapine, aripiprazole, risperidone, suvorexant, brexpiprazole, olanzapine, and trazadone, will have efficacious effects in AD patients. These drugs target important brain receptors involved in memory, learning, and cognition, including serotonin 5-HT2A, dopamine D2, and orexin receptors. In summary, DOTA repositions promising drugs that target important biological pathways and are predicted to improve patient cognition, circadian rhythms, and AD pathogenesis.

Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules

Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.

Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series

Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., alphabets, is divided into strong, medium, and weak using two user-specified thresholds. With the tri-partitioned alphabet, the tri-state takes the form of a matrix. One order contains the whole variables. The other is a feature vector that includes the most likely occurring strong, medium, and weak symbols. Second, a tri-partition strategy based on the deviation degree is proposed. We introduce the piecewise and symbolic aggregate approximation techniques to polymerize and discretize the original MTS. This way, the symbol is stronger and has a bigger deviation. Moreover, most popular numerical or symbolic similarity or distance metrics can be combined. Third, we propose an along–across similarity model to obtain the k-nearest matrix neighbors. This model considers the associations among the time stamps and variables simultaneously. Fourth, we design two post-filling strategies to obtain a completed tri-state. The experimental results from the four-domain datasets show that (1) the tri-state has greater recall but lower precision; (2) the two post-filling strategies can slightly improve the recall; and (3) the along–across similarity model composed by the Triangle and Jaccard metrics are first recommended for new datasets.

Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals

The United Nations' (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing efforts to map relationships among SDGs are either theoretical investigations of sustainability concepts, or empirical analyses of development indicators and policy simulations. We present an alternative approach, which describes and quantifies the complex network of SDG interdependencies by applying computational methods to policy and scientific documents. Methods of Natural Language Processing are used to measure overlaps in international policy discourse around SDGs, as represented by the corpus of all existing UN progress reports about each goal (N = 85 reports). We then examine if SDG interdependencies emerging from UN discourse are reflected in patterns of integration and collaboration in SDG-related science, by analyzing data on all scientific articles addressing relevant SDGs in the past two decades (N = 779,901 articles). Results identify a strong discursive divide between environmental goals and all other SDGs, and unexpected interdependencies between SDGs in different areas. While UN discourse partially aligns with integration patterns in SDG-related science, important differences are also observed between priorities emerging in UN and global scientific discourse. We discuss implications and insights for scientific research and policy on sustainable development after COVID-19.

Methods to Develop an in silico Clinical Trial: Computational Head-to-Head Comparison of Lisdexamfetamine and Methylphenidate

Regulatory agencies encourage computer modeling and simulation to reduce the time and cost of clinical trials. Although still not classified in formal guidelines, system biology-based models represent a powerful tool for generating hypotheses with great molecular detail. Herein, we have applied a mechanistic head-to-head in silico clinical trial (ISCT) between two treatments for attention-deficit/hyperactivity disorder, to wit lisdexamfetamine (LDX) and methylphenidate (MPH). The ISCT was generated through three phases comprising (i) the molecular characterization of drugs and pathologies, (ii) the generation of adult and children virtual populations (vPOPs) totaling 2,600 individuals and the creation of physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models, and (iii) data analysis with artificial intelligence methods. The characteristics of our vPOPs were in close agreement with real reference populations extracted from clinical trials, as did our PBPK models with in vivo parameters. The mechanisms of action of LDX and MPH were obtained from QSP models combining PBPK modeling of dosing schemes and systems biology-based modeling technology, i.e., therapeutic performance mapping system. The step-by-step process described here to undertake a head-to-head ISCT would allow obtaining mechanistic conclusions that could be extrapolated or used for predictions to a certain extent at the clinical level. Altogether, these computational techniques are proven an excellent tool for hypothesis-generation and would help reach a personalized medicine.