Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Distinguishing features of fold-switching proteins

Though many folded proteins assume one stable structure that performs one function, a small-but-increasing number remodel their secondary and tertiary structures and change their functions in response to cellular stimuli. These fold-switching proteins regulate biological processes and are associated with autoimmune dysfunction, severe acute respiratory syndrome coronavirus-2 infection, and more. Despite their biological importance, it is difficult to computationally predict fold switching. With the aim of advancing computational prediction and experimental characterization of fold switchers, this review discusses several features that distinguish fold-switching proteins from their single-fold and intrinsically disordered counterparts. First, the isolated structures of fold switchers are less stable and more heterogeneous than single folders but more stable and less heterogeneous than intrinsically disordered proteins (IDPs). Second, the sequences of single fold, fold switching, and intrinsically disordered proteins can evolve at distinct rates. Third, proteins from these three classes are best predicted using different computational techniques. Finally, late-breaking results suggest that single folders, fold switchers, and IDPs have distinct patterns of residue-residue coevolution. The review closes by discussing high-throughput and medium-throughput experimental approaches that might be used to identify new fold-switching proteins.

Construction and Application of Polygenic Risk Scores in Autoimmune Diseases

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.

Methodological assessment of systematic reviews and meta-analyses on COVID-19: A meta-epidemiological study

RATIONALE, AIMS, AND OBJECTIVES

COVID-19 has caused an ongoing public health crisis. Many systematic reviews and meta-analyses have been performed to synthesize evidence for better understanding this new disease. However, some concerns have been raised about rapid COVID-19 research. This meta-epidemiological study aims to methodologically assess the current systematic reviews and meta-analyses on COVID-19.

METHODS

We searched in various databases for systematic reviews with meta-analyses published between 1 January 2020 and 31 October 2020. We extracted their basic characteristics, data analyses, evidence appraisal, and assessment of publication bias and heterogeneity.

RESULTS

We identified 295 systematic reviews on COVID-19. The median time from submission to acceptance was 33 days. Among these systematic reviews, 73.9% evaluated clinical manifestations or comorbidities of COVID-19. Stata was the most used software programme (43.39%). The odds ratio was the most used effect measure (34.24%). Moreover, 28.14% of the systematic reviews did not present evidence appraisal. Among those reporting the risk of bias results, 14.64% of studies had a high risk of bias. Egger's test was the most used method for assessing publication bias (38.31%), while 38.66% of the systematic reviews did not assess publication bias. The I2 statistic was widely used for assessing heterogeneity (92.20%); many meta-analyses had high values of I2 . Among the meta-analyses using the random-effects model, 75.82% did not report the methods for model implementation; among those meta-analyses reporting implementation methods, the DerSimonian-Laird method was the most used one.

CONCLUSIONS

The current systematic reviews and meta-analyses on COVID-19 might suffer from low transparency, high heterogeneity, and suboptimal statistical methods. It is recommended that future systematic reviews on COVID-19 strictly follow well-developed guidelines. Sensitivity analyses may be performed to examine how the synthesized evidence might depend on different methods for appraising evidence, assessing publication bias, and implementing meta-analysis models.

Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases

Objective: Background incidence rates are routinely used in safety studies to evaluate an association of an exposure and outcome. Systematic research on sensitivity of rates to the choice of the study parameters is lacking. Materials and Methods: We used 12 data sources to systematically examine the influence of age, race, sex, database, time-at-risk, season and year, prior observation and clean window on incidence rates using 15 adverse events of special interest for COVID-19 vaccines as an example. For binary comparisons we calculated incidence rate ratios and performed random-effect meta-analysis. Results: We observed a wide variation of background rates that goes well beyond age and database effects previously observed. While rates vary up to a factor of 1,000 across age groups, even after adjusting for age and sex, the study showed residual bias due to the other parameters. Rates were highly influenced by the choice of anchoring (e.g., health visit, vaccination, or arbitrary date) for the time-at-risk start. Anchoring on a healthcare encounter yielded higher incidence comparing to a random date, especially for short time-at-risk. Incidence rates were highly influenced by the choice of the database (varying by up to a factor of 100), clean window choice and time-at-risk duration, and less so by secular or seasonal trends. Conclusion: Comparing background to observed rates requires appropriate adjustment and careful time-at-risk start and duration choice. Results should be interpreted in the context of study parameter choices.

A sequence-based method for predicting extant fold switchers that undergo α-helix ↔ β-strand transitions

Extant fold-switching proteins remodel their secondary structures and change their functions in response to cellular stimuli, regulating biological processes and affecting human health. Despite their biological importance, these proteins remain understudied. Predictive methods are needed to expedite the process of discovering and characterizing more of these shapeshifting proteins. Most previous approaches require a solved structure or all-atom simulations, greatly constraining their use. Here, we propose a high-throughput sequence-based method for predicting extant fold switchers that transition from α-helix in one conformation to β-strand in the other. This method leverages two previous observations: (a) α-helix ↔ β-strand prediction discrepancies from JPred4 are a robust predictor of fold switching, and (b) the fold-switching regions (FSRs) of some extant fold switchers have different secondary structure propensities when expressed by themselves (isolated FSRs) than when expressed within the context of their parent protein (contextualized FSRs). Combining these two observations, we ran JPred4 on 99-fold-switching proteins and found strong correspondence between predicted and experimentally observed α-helix ↔ β-strand discrepancies. To test the overall robustness of this finding, we randomly selected regions of proteins not expected to switch folds (single-fold proteins) and found significantly fewer predicted α-helix ↔ β-strand discrepancies. Combining these discrepancies with the overall percentage of predicted secondary structure, we developed a classifier to identify extant fold switchers (Matthews correlation coefficient of .71). Although this classifier had a high false-negative rate (7/17), its false-positive rate was very low (2/136), suggesting that it can be used to predict a subset of extant fold switchers from a multitude of available genomic sequences.

Detecting drug-drug interactions between therapies for COVID-19 and concomitant medications through the FDA adverse event reporting system

Background: COVID-19 patients with underlying medical conditions are vulnerable to drug-drug interactions (DDI) due to the use of multiple medications. We conducted a discovery-driven data analysis to identify potential DDIs and associated adverse events (AEs) in COVID-19 patients from the FDA Adverse Event Reporting System (FAERS), a source of post-market drug safety.

Materials and Methods: We investigated 18,589 COVID-19 AEs reported in the FAERS database between 2020 and 2021. We applied multivariate logistic regression to account for potential confounding factors, including age, gender, and the number of unique drug exposures. The significance of the DDIs was determined using both additive and multiplicative measures of interaction. We compared our findings with the Liverpool database and conducted a Monte Carlo simulation to validate the identified DDIs.

Results: Out of 11,337 COVID-19 drug-Co-medication-AE combinations investigated, our methods identified 424 signals statistically significant, covering 176 drug-drug pairs, composed of 13 COVID-19 drugs and 60 co-medications. Out of the 176 drug-drug pairs, 20 were found to exist in the Liverpool database. The empirical p-value obtained based on 1,000 Monte Carlo simulations was less than 0.001. Remdesivir was discovered to interact with the largest number of concomitant drugs (41). Hydroxychloroquine was detected to be associated with most AEs (39). Furthermore, we identified 323 gender- and 254 age-specific DDI signals.

Conclusion: The results, particularly those not found in the Liverpool database, suggest a subsequent need for further pharmacoepidemiology and/or pharmacology studies.

A Dual-Filtration System for Single-Cell Sequencing of Circulating Tumor Cells and Clusters in HCC

Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide. Identification and sequencing of circulating tumor (CT) cells and clusters may allow for noninvasive molecular characterization of HCC, which is an unmet need, as many patients with HCC do not undergo biopsy. We evaluated CT cells and clusters, collected using a dual-filtration system in patients with HCC. We collected and filtered whole blood from patients with HCC and selected individual CT cells and clusters with a micropipette. Reverse transcription, polymerase chain reaction, and library preparation were performed using a SmartSeq2 protocol, followed by single-cell RNA sequencing (scRNAseq) on an Illumina MiSeq V3 platform. Of the 8 patients recruited, 6 had identifiable CT cells or clusters. Median age was 64 years old; 7 of 8 were male; and 7 of 8 had and Barcelona Clinic Liver Cancer stage C. We performed scRNAseq of 38 CT cells and 33 clusters from these patients. These CT cells and clusters formed two distinct groups. Group 1 had significantly higher expression than group 2 of markers associated with epithelial phenotypes (CDH1 [Cadherin 1], EPCAM [epithelial cell adhesion molecule], ASGR2 [asialoglycoprotein receptor 2], and KRT8 [Keratin 8]), epithelial-mesenchymal transition (VIM [Vimentin]), and stemness (PROM1 [CD133], POU5F1 [POU domain, class 5, transcription factor 1], NOTCH1, STAT3 [signal transducer and activator of transcription 3]) (P < 0.05 for all). Patients with identifiable group 1 cells or clusters had poorer prognosis than those without them (median overall survival 39 vs. 384 days; P = 0.048 by log-rank test). Conclusion: A simple dual-filtration system allows for isolation and sequencing of CT cells and clusters in HCC and may identify cells expressing candidate genes known to be involved in cancer biology. Presence of CT cells/clusters expressing candidate genes is associated with poorer prognosis in advanced-stage HCC.

Optimal COVID-19 therapeutic candidate discovery using the CANDO platform

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

NAIR: Network Analysis of Immune Repertoire

T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.

Association between fat-infiltrated axillary lymph nodes on screening mammography and cardiometabolic disease

Objective

Ectopic fat deposition within and around organs is a stronger predictor of cardiometabolic disease status than body mass index (BMI). Fat deposition within the lymphatic system is poorly understood. This study examined the association between the prevalence of cardiometabolic disease and ectopic fat deposition within axillary lymph nodes (LNs) visualized on screening mammograms.

Methods

A cross-sectional study was conducted on 834 women presenting for full-field digital screening mammography. The status of fat-infiltrated LNs was assessed based on the size and morphology of axillary LNs from screening mammograms. The prevalence of cardiometabolic disease was retrieved from the electronic medical records, including type 2 diabetes mellitus (T2DM), hypertension, dyslipidemia, high blood glucose, cardiovascular disease, stroke, and non-alcoholic fatty liver disease.

Results

Fat-infiltrated axillary LNs were associated with a high prevalence of T2DM among all women (adjusted odds ratio: 3.92, 95% CI: [2.40, 6.60], p-value < 0.001) and in subgroups of women with and without obesity. Utilizing the status of fatty LNs improved the classification of T2DM status in addition to age and BMI (1.4% improvement in the area under the receiver operating characteristic curve).

Conclusion

Fat-infiltrated axillary LNs visualized on screening mammograms were associated with the prevalence of T2DM. If further validated, fat-infiltrated axillary LNs may represent a novel imaging biomarker of T2DM in women undergoing screening mammography.

Engineering the future of 3D pathology

In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.

Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers

Research on detecting Tuberculosis (TB) findings on chest radiographs (or Chest X-rays: CXR) using convolutional neural networks (CNNs) has demonstrated superior performance due to the emergence of publicly available, large-scale datasets with expert annotations and availability of scalable computational resources. However, these studies use only the frontal CXR projections, i.e., the posterior-anterior (PA), and the anterior-posterior (AP) views for analysis and decision-making. Lateral CXRs which are heretofore not studied help detect clinically suspected pulmonary TB, particularly in children. Further, Vision Transformers (ViTs) with built-in self-attention mechanisms have recently emerged as a viable alternative to the traditional CNNs. Although ViTs demonstrated notable performance in several medical image analysis tasks, potential limitations exist in terms of performance and computational efficiency, between the CNN and ViT models, necessitating a comprehensive analysis to select appropriate models for the problem under study. This study aims to detect TB-consistent findings in lateral CXRs by constructing an ensemble of the CNN and ViT models. Several models are trained on lateral CXR data extracted from two large public collections to transfer modality-specific knowledge and fine-tune them for detecting findings consistent with TB. We observed that the weighted averaging ensemble of the predictions of CNN and ViT models using the optimal weights computed with the Sequential Least-Squares Quadratic Programming method delivered significantly superior performance (MCC: 0.8136, 95% confidence intervals (CI): 0.7394, 0.8878, p < 0.05) compared to the individual models and other ensembles. We also interpreted the decisions of CNN and ViT models using class-selective relevance maps and attention maps, respectively, and combined them to highlight the discriminative image regions contributing to the final output. We observed that (i) the model accuracy is not related to disease region of interest (ROI) localization and (ii) the bitwise-AND of the heatmaps of the top-2-performing models delivered significantly superior ROI localization performance in terms of mean average precision [mAP@(0.1 0.6) = 0.1820, 95% CI: 0.0771,0.2869, p < 0.05], compared to other individual models and ensembles. The code is available at https://github.com/sivaramakrishnan-rajaraman/Ensemble-of-CNN-and-ViT-for-TB-detection-in-lateral-CXR.

Deciphering the Role of 3D Genome Organization in Breast Cancer Susceptibility

Cancer risk by environmental exposure is modulated by an individual's genetics and age at exposure. This age-specific period of susceptibility is referred to as the "Window of Susceptibility" (WOS). Rats have a similar WOS for developing breast cancer. A previous study in rat identified an age-specific long-range regulatory interaction for the cancer gene, Pappa, that is associated with breast cancer susceptibility. However, the global role of three-dimensional genome organization and downstream gene expression programs in the WOS is not known. Therefore, we generated Hi-C and RNA-seq data in rat mammary epithelial cells within and outside the WOS. To systematically identify higher-order changes in 3D genome organization, we developed NE-MVNMF that combines network enhancement followed by multitask non-negative matrix factorization. We examined three-dimensional genome organization dynamics at the level of individual loops as well as higher-order domains. Differential chromatin interactions tend to be associated with differentially up-regulated genes with the WOS and recapitulate several human SNP-gene interactions associated with breast cancer susceptibility. Our approach identified genomic blocks of regions with greater overall differences in contact count between the two time points when the cluster assignments change and identified genes and pathways implicated in early carcinogenesis and cancer treatment. Our results suggest that WOS-specific changes in 3D genome organization are linked to transcriptional changes that may influence susceptibility to breast cancer.

RobMedNAS: searching robust neural network architectures for medical image synthesis

Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS's efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.

Assessing the utility and accuracy of ICD10-CM non-traumatic subarachnoid hemorrhage codes for intracranial aneurysm research

BACKGROUND

The 10th revision of International Classification of Disease, Clinical Modification (ICD10-CM) increased the number of codes to identify non-traumatic subarachnoid hemorrhage from 1 to 22. ICD10-CM codes are able to specify the location of aneurysms causing subarachnoid hemorrhage (aSAH); however, it is not clear how frequently or accurately these codes are being used in practice.

OBJECTIVE

To systematically evaluate the usage and accuracy of location-specific ICD10-CM codes for aSAH.

METHODS

We extracted all uses of ICD10-CM codes for non-traumatic subarachnoid hemorrhage (I60.x) during the first 3 years following the implementation of ICD10-CM from the billing module of the electronic health record (EHR) for UCHealth. For those codes that specified aSAH location (I60.0-I60.6), EHR documentation was reviewed to determine whether there was an active aSAH, any patient history of aSAH, or unruptured intracranial aneurysm/s and the locations of those outcomes.

RESULTS

Between 1 October 2015 and 30 September 2018, there were 3119 instances of non-traumatic subarachnoid hemorrhage ICD10-CM codes (I60.00-I60.9), of which 297 (9.5%) code instances identified aSAH location (I60.0-I60.6). The usage of location-specific codes increased from 5.7% in 2015 to 11.2% in 2018. These codes accurately identified current aSAH (64%), any patient history of aSAH (84%), and any patient history of intracranial aneurysm (87%). The accuracy of identified outcome location was 53% in current aSAH, 72% for any history of aSAH, and 76% for any history of an intracranial aneurysm.

CONCLUSIONS

Researchers should use ICD10-CM codes with caution when attempting to detect active aSAH and/or aneurysm location.

Genetic predisposition may not improve prediction of cardiac surgery-associated acute kidney injury

Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury. Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models. Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10-8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10-6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000-1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320-3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372-0.835, p = 0.005) were significant in the full model. Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.

Crucial Roles of microRNA-16-5p and microRNA-27b-3p in Ameloblast Differentiation Through Regulation of Genes Associated With Amelogenesis Imperfecta

Amelogenesis imperfecta is a congenital disorder within a heterogeneous group of conditions characterized by enamel hypoplasia. Patients suffer from early tooth loss, social embarrassment, eating difficulties, and pain due to an abnormally thin, soft, fragile, and discolored enamel with poor aesthetics and functionality. The etiology of amelogenesis imperfecta is complicated by genetic interactions. To identify mouse amelogenesis imperfecta-related genes (mAIGenes) and their respective phenotypes, we conducted a systematic literature review and database search and found and curated 70 mAIGenes across all of the databases. Our pathway enrichment analysis indicated that these genes were enriched in tooth development-associated pathways, forming four distinct groups. To explore how these genes are regulated and affect the phenotype, we predicted microRNA (miRNA)-gene interaction pairs using our bioinformatics pipeline. Our miRNA regulatory network analysis pinpointed that miR-16-5p, miR-27b-3p, and miR-23a/b-3p were hub miRNAs. The function of these hub miRNAs was evaluated through ameloblast differentiation assays with/without the candidate miRNA mimics using cultured mouse ameloblast cells. Our results revealed that overexpression of miR-16-5p and miR-27b-3p, but not miR-23a/b-3p, significantly inhibited ameloblast differentiation through regulation of mAIGenes. Thus, our study shows that miR-16-5p and miR-27b-3p are candidate pathogenic miRNAs for amelogenesis imperfecta.

Monitoring environmental microbiomes: Alignment of microbiology and computational biology competencies within a culturally integrated curriculum and research framework

We have developed a flexible undergraduate curriculum that leverages the place-based research of environmental microbiomes to increase the number of Indigenous researchers in microbiology, data science and scientific computing. Monitoring Environmental Microbiomes (MEM) provides a curriculum and research framework designed to integrate an Indigenous approach when conducting authentic scientific research and to build interest and confidence at the undergraduate level. MEM has been successfully implemented as a short summer workshop to introduce computing practices in microbiome analysis. Based on self-assessed student knowledge of topics and skills, increased scientific confidence and interest in genomics careers were observed. We propose MEM be incorporated in a scalable course-based research experience for undergraduate institutions, including tribal colleges and universities, community colleges and other minority serving institutions. This coupled curricular and research framework explicitly considers cultural perspectives, access and equity to train a diverse future workforce that is more informed to engage in microbiome research and to translate microbiome science to benefit community and environmental health.

Identification of Known and Novel Long Noncoding RNAs Potentially Responsible for the Effects of Bone Mineral Density (BMD) Genomewide Association Study (GWAS) Loci

Osteoporosis, characterized by low bone mineral density (BMD), is the most common complex disease affecting bone and constitutes a major societal health problem. Genome-wide association studies (GWASs) have identified over 1100 associations influencing BMD. It has been shown that perturbations to long noncoding RNAs (lncRNAs) influence BMD and the activities of bone cells; however, the extent to which lncRNAs are involved in the genetic regulation of BMD is unknown. Here, we combined the analysis of allelic imbalance (AI) in human acetabular bone fragments with a transcriptome-wide association study (TWAS) and expression quantitative trait loci (eQTL) colocalization analysis using data from the Genotype-Tissue Expression (GTEx) project to identify lncRNAs potentially responsible for GWAS associations. We identified 27 lncRNAs in bone that are located in proximity to a BMD GWAS association and harbor single-nucleotide polymorphisms (SNPs) demonstrating AI. Using GTEx data we identified an additional 31 lncRNAs whose expression was associated (false discovery rate [FDR] correction < 0.05) with BMD through TWAS and had a colocalizing eQTL (regional colocalization probability [RCP] > 0.1). The 58 lncRNAs are located in 43 BMD associations. To further support a causal role for the identified lncRNAs, we show that 23 of the 58 lncRNAs are differentially expressed as a function of osteoblast differentiation. Our approach identifies lncRNAs that are potentially responsible for BMD GWAS associations and suggest that lncRNAs play a role in the genetics of osteoporosis. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Controlled variable selection in Weibull mixture cure models for high-dimensional data

Medical breakthroughs in recent years have led to cures for many diseases. The mixture cure model (MCM) is a type of survival model that is often used when a cured fraction exists. Many have sought to identify genomic features associated with a time-to-event outcome which requires variable selection strategies for high-dimensional spaces. Unfortunately, currently few variable selection methods exist for MCMs especially when there are more predictors than samples. This study develops high-dimensional penalized Weibull MCMs, which allow for identification of prognostic factors associated with both cure status and/or survival. We demonstrated how such models may be estimated using two different iterative algorithms. The model-X knockoffs method was combined with these algorithms to control the false discovery rate (FDR) in variable selection. Through extensive simulation studies, our penalized MCMs have been shown to outperform alternative methods on multiple metrics and achieve high statistical power with FDR being controlled. In an acute myeloid leukemia (AML) application with gene expression data, our proposed approach identified 14 genes associated with potential cure and 12 genes with time-to-relapse, which may help inform treatment decisions for AML patients.

Hepatic macrophage mediated immune response in liver steatosis driven carcinogenesis

Obesity confers an independent risk for carcinogenesis. Classically viewed as a genetic disease, owing to the discovery of tumor suppressors and oncogenes, genetic events alone are not sufficient to explain the progression and development of cancers. Tumor development is often associated with metabolic and immunological changes. In particular, obesity is found to significantly increase the mortality rate of liver cancer. As its role is not defined, a fundamental question is whether and how metabolic changes drive the development of cancer. In this review, we will dissect the current literature demonstrating that liver lipid dysfunction is a critical component driving the progression of cancer. We will discuss the involvement of inflammation in lipid dysfunction driven liver cancer development with a focus on the involvement of liver macrophages. We will first discuss the association of steatosis with liver cancer. This will be followed with a literature summary demonstrating the importance of inflammation and particularly macrophages in the progression of liver steatosis and highlighting the evidence that macrophages and macrophage produced inflammatory mediators are critical for liver cancer development. We will then discuss the specific inflammatory mediators and their roles in steatosis driven liver cancer development. Finally, we will summarize the molecular pattern (PAMP and DAMP) as well as lipid particle signals that are involved in the activation, infiltration and reprogramming of liver macrophages. We will also discuss some of the therapies that may interfere with lipid metabolism and also affect liver cancer development.

COLLAPSE: A representation learning framework for identification and characterization of protein structural sites

The identification and characterization of the structural sites which contribute to protein function are crucial for understanding biological mechanisms, evaluating disease risk, and developing targeted therapies. However, the quantity of known protein structures is rapidly outpacing our ability to functionally annotate them. Existing methods for function prediction either do not operate on local sites, suffer from high false positive or false negative rates, or require large site-specific training datasets, necessitating the development of new computational methods for annotating functional sites at scale. We present COLLAPSE (Compressed Latents Learned from Aligned Protein Structural Environments), a framework for learning deep representations of protein sites. COLLAPSE operates directly on the 3D positions of atoms surrounding a site and uses evolutionary relationships between homologous proteins as a self-supervision signal, enabling learned embeddings to implicitly capture structure-function relationships within each site. Our representations generalize across disparate tasks in a transfer learning context, achieving state-of-the-art performance on standardized benchmarks (protein-protein interactions and mutation stability) and on the prediction of functional sites from the Prosite database. We use COLLAPSE to search for similar sites across large protein datasets and to annotate proteins based on a database of known functional sites. These methods demonstrate that COLLAPSE is computationally efficient, tunable, and interpretable, providing a general-purpose platform for computational protein analysis.

Network-based analysis predicts interacting genetic modifiers from a meta-mapping study of spike-wave discharge in mice

Absence seizures are characterized by brief lapses in awareness accompanied by a hallmark spike-and-wave discharge (SWD) electroencephalographic pattern and are common to genetic generalized epilepsies (GGEs). While numerous genes have been associated with increased risk, including some Mendelian forms with a single causal allele, most cases of GGE are idiopathic and there are many unknown genetic modifiers of GGE influencing risk and severity. In a previous meta-mapping study, crosses between transgenic C57BL/6 and C3HeB/FeJ strains, each carrying one of three SWD-causing mutations (Gabrg2tm1Spet(R43Q) , Scn8a8j or Gria4spkw1 ), demonstrated an antagonistic epistatic interaction between loci on mouse chromosomes 2 and 7 influencing SWD. These results implicate universal modifiers in the B6 background that mitigate SWD severity through a common pathway, independent of the causal mutation. In this study, we prioritized candidate modifiers in these interacting loci. Our approach integrated human genome-wide association results with gene interaction networks and mouse brain gene expression to prioritize candidate genes and pathways driving variation in SWD outcomes. We considered candidate genes that are functionally associated with human GGE risk genes and genes with evidence for coding or non-coding allele effects between the B6 and C3H backgrounds. Our analyses output a summary ranking of gene pairs, one gene from each locus, as candidates for explaining the epistatic interaction. Our top-ranking gene pairs implicate microtubule function, cytoskeletal stability and cell cycle regulation as novel hypotheses about the source of SWD variation across strain backgrounds, which could clarify underlying mechanisms driving differences in GGE severity in humans.

Part 2: Drug Interactions Involving Cannabis Products in Persons Aged 18 and Over: A Summary of Published Case Reports and Analysis of the FDA Adverse Event Reporting System

The increasing utilization of cannabis products combined with lack of data regarding potential cannabis-prescription drug interactions is concerning. This study aimed to review published case reports and FDA Adverse Event Reporting System (FAERS) spontaneous reports to assess cannabis-drug interactions in persons aged 18 and over. A literature search identified 20 case reports that were each assessed for drug interaction causality using the Drug Interaction Probability Scale. Data collected from the FAERS revealed a greater proportion of reports mentioning serious outcomes, including death, when cannabis was used concomitantly with controlled substances compared to noncontrolled substances. Fisher's exact test showed a statistically significant difference between the controlled and noncontrolled groups (p = 0.043). Overall, these findings emphasize the need for additional research and vigilant monitoring of cannabis use when combined with other medications.