Schlafen2 mutation in mice causes an osteopetrotic phenotype due to a decrease in the number of osteoclast progenitors

Using multi-scale genomics to associate poorly annotated genes with rare diseases

BACKGROUND

Next-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic disorders. However, a substantial portion of sequenced patients remains undiagnosed. This may be attributed not only to the challenges posed by harder-to-detect variants, such as non-coding and structural variations but also to the existence of variants in genes not previously associated with the patient's clinical phenotype. This study introduces EvORanker, an algorithm that integrates unbiased data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes.

METHODS

EvORanker utilizes clinical data, multi-scale phylogenetic profiling, and other omics data to prioritize disease-associated genes. It was evaluated on solved exomes and simulated genomes, compared with existing methods, and applied to 6260 knockout genes with mouse phenotypes lacking human associations. Additionally, EvORanker was made accessible as a user-friendly web tool.

RESULTS

In the analyzed exomic cohort, EvORanker accurately identified the "true" disease gene as the top candidate in 69% of cases and within the top 5 candidates in 95% of cases, consistent with results from the simulated dataset. Notably, EvORanker outperformed existing methods, particularly for poorly annotated genes. In the case of the 6260 knockout genes with mouse phenotypes, EvORanker linked 41% of these genes to observed human disease phenotypes. Furthermore, in two unsolved cases, EvORanker successfully identified DLGAP2 and LPCAT3 as disease candidates for previously uncharacterized genetic syndromes.

CONCLUSIONS

We highlight clade-based phylogenetic profiling as a powerful systematic approach for prioritizing potential disease genes. Our study showcases the efficacy of EvORanker in associating poorly annotated genes to disease phenotypes observed in patients. The EvORanker server is freely available at https://ccanavati.shinyapps.io/EvORanker/ .

Cross-species identification of cancer resistance-associated genes that may mediate human cancer risk

Cancer is a predominant disease across animals. We applied a comparative genomics approach to systematically characterize genes whose conservation levels correlate positively (PC) or negatively (NC) with cancer resistance estimates across 193 vertebrates. Pathway analysis reveals that NC genes are enriched for metabolic functions and PC genes in cell cycle regulation, DNA repair, and immune response, pointing to their corresponding roles in mediating cancer risk. We find that PC genes are less tolerant to loss-of-function (LoF) mutations, are enriched in cancer driver genes, and are associated with germline mutations that increase human cancer risk. Their relevance to cancer risk is further supported via the analysis of mouse functional genomics and cancer mortality of zoo mammals' data. In sum, our study describes a cross-species genomic analysis pointing to candidate genes that may mediate human cancer risk.

The Non-Erythropoietic EPO Analogue Cibinetide Inhibits Osteoclastogenesis In Vitro and Increases Bone Mineral Density in Mice

The two erythropoietin (EPO) receptor forms mediate different cellular responses to erythropoietin. While hematopoiesis is mediated via the homodimeric EPO receptor (EPOR), tissue protection is conferred via a heteromer composed of EPOR and CD131. In the skeletal system, EPO stimulates osteoclast precursors and induces bone loss. However, the underlying molecular mechanisms are still elusive. Here, we evaluated the role of the heteromeric complex in bone metabolism in vivo and in vitro by using Cibinetide (CIB), a non-erythropoietic EPO analogue that exclusively binds the heteromeric receptor. CIB is administered either alone or in combination with EPO. One month of CIB treatment significantly increased the cortical (~5.8%) and trabecular (~5.2%) bone mineral density in C57BL/6J WT female mice. Similarly, administration of CIB for five consecutive days to female mice that concurrently received EPO on days one and four, reduced the number of osteoclast progenitors, defined by flow cytometry as Lin⁻CD11b⁻Ly6C^hi CD115⁺, by 42.8% compared to treatment with EPO alone. In addition, CIB alone or in combination with EPO inhibited osteoclastogenesis in vitro. Our findings introduce CIB either as a stand-alone treatment, or in combination with EPO, as an appealing candidate for the treatment of the bone loss that accompanies EPO treatment.

Schlafens: Emerging Proteins in Cancer Cell Biology

Schlafens (SLFN) are a family of genes widely expressed in mammals, including humans and rodents. These intriguing proteins play different roles in regulating cell proliferation, cell differentiation, immune cell growth and maturation, and inhibiting viral replication. The emerging evidence is implicating Schlafens in cancer biology and chemosensitivity. Although Schlafens share common domains and a high degree of homology, different Schlafens act differently. In particular, they show specific and occasionally opposing effects in some cancer types. This review will briefly summarize the history, structure, and non-malignant biological functions of Schlafens. The roles of human and mouse Schlafens in different cancer types will then be outlined. Finally, we will discuss the implication of Schlafens in the anti-tumor effect of interferons and the use of Schlafens as predictors of chemosensitivity.

CladeOScope: functional interactions through the prism of clade-wise co-evolution

Mapping co-evolved genes via phylogenetic profiling (PP) is a powerful approach to uncover functional interactions between genes and to associate them with pathways. Despite many successful endeavors, the understanding of co-evolutionary signals in eukaryotes remains partial. Our hypothesis is that 'Clades', branches of the tree of life (e.g. primates and mammals), encompass signals that cannot be detected by PP using all eukaryotes. As such, integrating information from different clades should reveal local co-evolution signals and improve function prediction. Accordingly, we analyzed 1028 genomes in 66 clades and demonstrated that the co-evolutionary signal was scattered across clades. We showed that functionally related genes are frequently co-evolved in only parts of the eukaryotic tree and that clades are complementary in detecting functional interactions within pathways. We examined the non-homologous end joining pathway and the UFM1 ubiquitin-like protein pathway and showed that both demonstrated distinguished co-evolution patterns in specific clades. Our research offers a different way to look at co-evolution across eukaryotes and points to the importance of modular co-evolution analysis. We developed the 'CladeOScope' PP method to integrate information from 16 clades across over 1000 eukaryotic genomes and is accessible via an easy to use web server at http://cladeoscope.cs.huji.ac.il.

Bone tissue and histological and molecular events during development of the long bones

The bones are of mesenchymal or ectomesenchymal origin, form the skeleton of most vertebrates, and are essential for locomotion and organ protection. As a living tissue they are highly vascularized and remodelled throughout life to maintain intact. Bones consist of osteocytes entrapped in a mineralized extracellular matrix, and via their elaborated network of cytoplasmic processes they do not only communicate with each other but also with the cells on the bone surface (bone lining cells). Bone tissue develops through a series of fine-tuned processes, and there are two modes of bone formation, referred to either as intramembranous or endochondral ossification. In intramembranous ossification, bones develop directly from condensations of mesenchymal cells, and the flat bones of the skull, the clavicles and the perichondral bone cuff develop via this process. The bones of the axial (ribs and vertebrae) and the appendicular skeleton (e.g. upper and lower limbs) form through endochondral ossification where mesenchyme turns into a cartilaginous intermediate with the shape of the future skeletal element that is gradually replaced by bone. Endochondral ossification occurs in all vertebrate taxa and its onset involves differentiation of the chondrocytes, mineralization of the extracellular cartilage matrix and vascularization of the intermediate, followed by disintegration and resorption of the cartilage, bone formation, and finally - after complete ossification of the cartilage model - the establishment of an avascular articular cartilage. The epiphyseal growth plate regulates the longitudinal growth of the bones, achieved by a balanced proliferation and elimination of chondrocytes, and the question whether the late hypertrophic chondrocytes die or transform into osteogenic cells is still being hotly debated. The complex processes leading to endochondral ossification have been studied for over a century, and this review aims to give an overview of the histological and molecular events, arising from the long bones' (e.g. femur, tibia) development. The fate of the hypertrophic chondrocytes will be discussed in the light of new findings obtained from cell tracking studies.

Optimization of co-evolution analysis through phylogenetic profiling reveals pathway-specific signals

SUMMARY

The exponential growth in available genomic data is expected to reach full sequencing of a million genomes in the coming decade. Improving and developing methods to analyze these genomes and to reveal their utility is of major interest in a wide variety of fields, such as comparative and functional genomics, evolution and bioinformatics. Phylogenetic profiling is an established method for predicting functional interactions between proteins based on similarities in their evolutionary patterns across species. Proteins that function together (i.e. generate complexes, interact in the same pathways or improve adaptation to environmental niches) tend to show coordinated evolution across the tree of life. The normalized phylogenetic profiling (NPP) method takes into account minute changes in proteins across species to identify protein co-evolution. Despite the success of this method, it is still not clear what set of parameters is required for optimal use of co-evolution in predicting functional interactions. Moreover, it is not clear if pathway evolution or function should direct parameter choice. Here, we create a reliable and usable NPP construction pipeline. We explore the effect of parameter selection on functional interaction prediction using NPP from 1028 genomes, both separately and in various value combinations. We identify several parameter sets that optimize performance for pathways with certain biological annotation. This work reveals the importance of choosing the right parameters for optimized function prediction based on a biological context.

AVAILABILITY AND IMPLEMENTATION

Source code and documentation are available on GitHub: https://github.com/iditam/CompareNPPs.

CONTACT

yuvaltab@ekmd.huji.ac.il.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ACE2 Co-evolutionary Pattern Suggests Targets for Pharmaceutical Intervention in the COVID-19 Pandemic

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spillover infection in December 2019 has caused an unprecedented pandemic. SARS-CoV-2, as other coronaviruses, binds its target cells through the angiotensin-converting enzyme 2 (ACE2) receptor. Accordingly, this makes ACE2 research essential for understanding the zoonotic nature of coronaviruses and identifying novel drugs. Here we present a systematic analysis of the ACE2 conservation and co-evolution protein network across 1,671 eukaryotes, revealing an unexpected conservation pattern in specific metazoans, plants, fungi, and protists. We identified the co-evolved protein network and pinpointed a list of drugs that target this network by using data integration from different sources. Our computational analysis found widely used drugs such as nonsteroidal anti-inflammatory drugs and vasodilators. These drugs are expected to perturb the ACE2 network affecting infectivity as well as the pathophysiology of the disease.

•

Mapping the ACE2 conservation pattern in 146 mammal reservoirs and 1,671 eukaryotes

•

Identification of genes that show a similar evolutionary pattern as ACE2

•

Generation of an ACE2 protein network using co-evolution and data integration

•

Drugs-to-network analyses mapped 145 drugs that perturbed the ACE2 network

Mapping global and local coevolution across 600 species to identify novel homologous recombination repair genes

The homologous recombination repair (HRR) pathway repairs DNA double-strand breaks in an error-free manner. Mutations in HRR genes can result in increased mutation rate and genomic rearrangements, and are associated with numerous genetic disorders and cancer. Despite intensive research, the HRR pathway is not yet fully mapped. Phylogenetic profiling analysis, which detects functional linkage between genes using coevolution, is a powerful approach to identify factors in many pathways. Nevertheless, phylogenetic profiling has limited predictive power when analyzing pathways with complex evolutionary dynamics such as the HRR. To map novel HRR genes systematically, we developed clade phylogenetic profiling (CladePP). CladePP detects local coevolution across hundreds of genomes and points to the evolutionary scale (e.g., mammals, vertebrates, animals, plants) at which coevolution occurred. We found that multiscale coevolution analysis is significantly more biologically relevant and sensitive to detect gene function. By using CladePP, we identified dozens of unrecognized genes that coevolved with the HRR pathway, either globally across all eukaryotes or locally in different clades. We validated eight genes in functional biological assays to have a role in DNA repair at both the cellular and organismal levels. These genes are expected to play a role in the HRR pathway and might lead to a better understanding of missing heredity in HRR-associated cancers (e.g., heredity breast and ovarian cancer). Our platform presents an innovative approach to predict gene function, identify novel factors related to different diseases and pathways, and characterize gene evolution.