The future of rapid and automated single-cell data analysis using reference mapping.

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

Host traits shape virome composition and virus transmission in wild small mammals.

Bats, rodents, and shrews are the most important animal sources of human infectious diseases. However, the evolution and transmission of viruses among them remain largely unexplored. Through the meta-transcriptomic sequencing of internal organ and fecal samples from 2,443 wild bats, rodents, and shrews sampled from four Chinese habitats, we identified 669 viruses, including 534 novel viruses, thereby greatly expanding the mammalian virome. Our analysis revealed high levels of phylogenetic diversity, identified cross-species virus transmission events, elucidated virus origins, and identified cases of invertebrate viruses in mammalian hosts. Host order and sample size were the most important factors impacting virome composition and patterns of virus spillover. Shrews harbored a high richness of viruses, including many invertebrate-associated viruses with multi-organ distributions, whereas rodents carried viruses with a greater capacity for host jumping. These data highlight the remarkable diversity of mammalian viruses in local habitats and their ability to emerge in new hosts.

Neural Circuits for Emotion.

Emotions are fundamental to our experience and behavior, affecting and motivating all aspects of our lives. Scientists of various disciplines have been fascinated by emotions for centuries, yet even today vigorous debates abound about how to define emotions and how to best study their neural underpinnings. Defining emotions from an evolutionary perspective and acknowledging their important functional roles in supporting survival allows the study of emotion states in diverse species. This approach enables taking advantage of modern tools in behavioral, systems, and circuit neurosciences, allowing the precise dissection of neural mechanisms and behavior underlying emotion processes in model organisms. Here we review findings about the neural circuit mechanisms underlying emotion processing across species and try to identify points of convergence as well as important next steps in the pursuit of understanding how emotions emerge from neural activity.

Landscape and Regulation of m6A and m6Am Methylome across Human and Mouse Tissues.

N6-methyladenosine (m6A), the most abundant internal mRNA modification, and N6,2'-O-dimethyladenosine (m6Am), found at the first-transcribed nucleotide, are two reversible epitranscriptomic marks. However, the profiles and distribution patterns of m6A and m6Am across human and mouse tissues are poorly characterized. Here, we report the m6A and m6Am methylome through profiling of 43 human and 16 mouse tissues and demonstrate strongest tissue specificity for the brain tissues. A small subset of tissue-specific m6A peaks can also readily classify tissue types. The overall m6A and m6Am level is partially correlated with the expression level of their writers and erasers. Additionally, the m6A-containing regions are enriched for SNPs. Furthermore, cross-species analysis revealed that species rather than tissue type is the primary determinant of methylation. Collectively, our study provides an in-depth resource for dissecting the landscape and regulation of the m6A and m6Am epitranscriptomic marks across mammalian tissues.

INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction.

An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constraints in time and cost of the associated 'wet lab' experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method that incorporates orthology data using a new 'quintuplet' neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intraspecies and cross-species tasks using strict evaluation datasets. We show that INTREPPPID's orthologous locality loss increases performance because of the biological relevance of the orthologue data and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community.

CACIMAR: cross-species analysis of cell identities, markers, regulations, and interactions using single-cell RNA sequencing data.

Transcriptomic analysis across species is increasingly used to reveal conserved gene regulations which implicate crucial regulators. Cross-species analysis of single-cell RNA sequencing (scRNA-seq) data provides new opportunities to identify the cellular and molecular conservations, especially for cell types and cell type-specific gene regulations. However, few methods have been developed to analyze cross-species scRNA-seq data to uncover both molecular and cellular conservations. Here, we built a tool called CACIMAR, which can perform cross-species analysis of cell identities, markers, regulations, and interactions using scRNA-seq profiles. Based on the weighted sum models of the conserved features, we developed different conservation scores to measure the conservation of cell types, regulatory networks, and intercellular interactions. Using publicly available scRNA-seq data on retinal regeneration in mice, zebrafish, and chick, we demonstrated four main functions of CACIMAR. First, CACIMAR allows to identify conserved cell types even in evolutionarily distant species. Second, the tool facilitates the identification of evolutionarily conserved or species-specific marker genes. Third, CACIMAR enables the identification of conserved intracellular regulations, including cell type-specific regulatory subnetworks and regulators. Lastly, CACIMAR provides a unique feature for identifying conserved intercellular interactions. Overall, CACIMAR facilitates the identification of evolutionarily conserved cell types, marker genes, intracellular regulations, and intercellular interactions, providing insights into the cellular and molecular mechanisms of species evolution.

Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology.

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO among the most influential genes composing the latent space for integration. Subsequently, during the alignment of latent spaces, each composed of species-specific genes, it is possible to identify functional annotations of genes missing from public orthology databases. We evaluated our approach with four different single-cell sequencing datasets focusing on cell-type prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms related methods working without prior knowledge when predicting unseen cell types based on other species' data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.

Elephants as an animal model for self-domestication.

Humans are unique in their sophisticated culture and societal structures, their complex languages, and their extensive tool use. According to the human self-domestication hypothesis, this unique set of traits may be the result of an evolutionary process of self-induced domestication, in which humans evolved to be less aggressive and more cooperative. However, the only other species that has been argued to be self-domesticated besides humans so far is bonobos, resulting in a narrow scope for investigating this theory limited to the primate order. Here, we propose an animal model for studying self-domestication: the elephant. First, we support our hypothesis with an extensive cross-species comparison, which suggests that elephants indeed exhibit many of the features associated with self-domestication (e.g., reduced aggression, increased prosociality, extended juvenile period, increased playfulness, socially regulated cortisol levels, and complex vocal behavior). Next, we present genetic evidence to reinforce our proposal, showing that genes positively selected in elephants are enriched in pathways associated with domestication traits and include several candidate genes previously associated with domestication. We also discuss several explanations for what may have triggered a self-domestication process in the elephant lineage. Our findings support the idea that elephants, like humans and bonobos, may be self-domesticated. Since the most recent common ancestor of humans and elephants is likely the most recent common ancestor of all placental mammals, our findings have important implications for convergent evolution beyond the primate taxa, and constitute an important advance toward understanding how and why self-domestication shaped humans' unique cultural niche.

Rats respond to aversive emotional arousal of human handlers with the activation of the basolateral and central amygdala.

Reading danger signals may save an animal's life, and learning about threats from others allows avoiding first-hand aversive and often fatal experiences. Fear expressed by other individuals, including those belonging to other species, may indicate the presence of a threat in the environment and is an important social cue. Humans and other animals respond to conspecifics' fear with increased activity of the amygdala, the brain structure crucial for detecting threats and mounting an appropriate response to them. It is unclear, however, whether the cross-species transmission of threat information involves similar mechanisms, e.g., whether animals respond to the aversively induced emotional arousal of humans with activation of fear-processing circuits in the brain. Here, we report that when rats interact with a human caregiver who had recently undergone fear conditioning, they show risk assessment behavior and enhanced amygdala activation. The amygdala response involves its two major parts, the basolateral and central, which detect a threat and orchestrate defensive responses. Further, we show that humans who learn about a threat by observing another aversively aroused human, similar to rats, activate the basolateral and centromedial parts of the amygdala. Our results demonstrate that rats detect the emotional arousal of recently aversively stimulated caregivers and suggest that cross-species social transmission of threat information may involve similar neural circuits in the amygdala as the within-species transmission.

Single-Cell Profiling Uncovers Evolutionary Divergence of Hypocretin/Orexin Neuronal Subpopulations.

Brain nuclei are traditionally defined by their anatomy, activity, and expression of specific markers. The hypothalamus contains discrete neuronal populations that coordinate fundamental behavioral functions, including sleep and wakefulness, in all vertebrates. Particularly, the diverse roles of hypocretin/orexin (Hcrt)-releasing neurons suggest functional heterogeneity among Hcrt neurons. Using single-cell RNA sequencing (scRNA-seq) and high-resolution imaging of the adult male and female zebrafish hypothalamic periventricular zone, we identified 21 glutamatergic and 28 GABAergic cell types. Integration of zebrafish and mouse scRNA-seq revealed evolutionary conserved and divergent hypothalamic cell types. The expression of specific genes, including npvf, which encodes a sleep-regulating neuropeptide, was enriched in subsets of glutamatergic Hcrt neurons in both larval and adult zebrafish. The genetic profile, activity, and neurite processing of the neuronal subpopulation that coexpresses both Hcrt and Npvf (Hcrt+Npvf+) differ from other Hcrt neurons. These interspecies findings provide a unified annotation of hypothalamic cell types and suggest that the heterogeneity of Hcrt neurons enables multifunctionality, such as consolidation of both wake and sleep by the Hcrt- and Npvf-releasing neuronal subpopulation.

A cross-species approach using an in vivo evaluation platform in mice demonstrates that sequence variation in human RABEP2 modulates ischemic stroke outcomes.

Ischemic stroke, caused by vessel blockage, results in cerebral infarction, the death of brain tissue. Previously, quantitative trait locus (QTL) mapping of cerebral infarct volume and collateral vessel number identified a single, strong genetic locus regulating both phenotypes. Additional studies identified RAB GTPase-binding effector protein 2 (Rabep2) as the casual gene. However, there is yet no evidence that variation in the human ortholog of this gene plays any role in ischemic stroke outcomes. We established an in vivo evaluation platform in mice by using adeno-associated virus (AAV) gene replacement and verified that both mouse and human RABEP2 rescue the mouse Rabep2 knockout ischemic stroke volume and collateral vessel phenotypes. Importantly, this cross-species complementation enabled us to experimentally investigate the functional effects of coding sequence variation in human RABEP2. We chose four coding variants from the human population that are predicted by multiple in silico algorithms to be damaging to RABEP2 function. In vitro and in vivo analyses verify that all four led to decreased collateral vessel connections and increased infarct volume. Thus, there are naturally occurring loss-of-function alleles. This cross-species approach will expand the number of targets for therapeutics development for ischemic stroke.

Molecular basis of hippopotamus ACE2 binding to SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a wide range of hosts, including hippopotami, which are semi-aquatic mammals and phylogenetically closely related to Cetacea. In this study, we characterized the binding properties of hippopotamus angiotensin-converting enzyme 2 (hiACE2) to the spike (S) protein receptor binding domains (RBDs) of the SARS-CoV-2 prototype (PT) and variants of concern (VOCs). Furthermore, the cryo-electron microscopy (cryo-EM) structure of the SARS-CoV-2 PT S protein complexed with hiACE2 was resolved. Structural and mutational analyses revealed that L30 and F83, which are specific to hiACE2, played a crucial role in the hiACE2/SARS-CoV-2 RBD interaction. In addition, comparative and structural analysis of ACE2 orthologs suggested that the cetaceans may have the potential to be infected by SARS-CoV-2. These results provide crucial molecular insights into the susceptibility of hippopotami to SARS-CoV-2 and suggest the potential risk of SARS-CoV-2 VOCs spillover and the necessity for surveillance. IMPORTANCE: The hippopotami are the first semi-aquatic artiodactyl mammals wherein SARS-CoV-2 infection has been reported. Exploration of the invasion mechanism of SARS-CoV-2 will provide important information for the surveillance of SARS-CoV-2 in hippopotami, as well as other semi-aquatic mammals and cetaceans. Here, we found that hippopotamus ACE2 (hiACE2) could efficiently bind to the RBDs of the SARS-CoV-2 prototype (PT) and variants of concern (VOCs) and facilitate the transduction of SARS-CoV-2 PT and VOCs pseudoviruses into hiACE2-expressing cells. The cryo-EM structure of the SARS-CoV-2 PT S protein complexed with hiACE2 elucidated a few critical residues in the RBD/hiACE2 interface, especially L30 and F83 of hiACE2 which are unique to hiACE2 and contributed to the decreased binding affinity to PT RBD compared to human ACE2. Our work provides insight into cross-species transmission and highlights the necessity for monitoring host jumps and spillover events on SARS-CoV-2 in semi-aquatic/aquatic mammals.

Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results.

The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization.

Ape Origins of Human Malaria.

African apes harbor at least twelve Plasmodium species, some of which have been a source of human infection. It is now well established that Plasmodium falciparum emerged following the transmission of a gorilla parasite, perhaps within the last 10,000 years, while Plasmodium vivax emerged earlier from a parasite lineage that infected humans and apes in Africa before the Duffy-negative mutation eliminated the parasite from humans there. Compared to their ape relatives, both human parasites have greatly reduced genetic diversity and an excess of nonsynonymous mutations, consistent with severe genetic bottlenecks followed by rapid population expansion. A putative new Plasmodium species widespread in chimpanzees, gorillas, and bonobos places the origin of Plasmodium malariae in Africa. Here, we review what is known about the origins and evolutionary history of all human-infective Plasmodium species, the time and circumstances of their emergence, and the diversity, host specificity, and zoonotic potential of their ape counterparts.

Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues.

N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection over the past decades, there were rooms left to improve the predictive accuracy and the efficiency. In this paper, we proposed an improved method for predicting m6A modification sites, which was based on bi-directional gated recurrent unit (Bi-GRU) and convolutional neural networks (CNN), called Deepm6A-MT. The Deepm6A-MT has two input channels. One is to use an embedding layer followed by the Bi-GRU and then by the CNN, and another is to use one-hot encoding, dinucleotide one-hot encoding, and nucleotide chemical property codes. We trained and evaluated the Deepm6A-MT both by the 5-fold cross-validation and the independent test. The empirical tests showed that the Deepm6A-MT achieved the state of the art performance. In addition, we also conducted the cross-species and the cross-tissues tests to further verify the Deepm6A-MT for effectiveness and efficiency. Finally, for the convenience of academic research, we deployed the Deepm6A-MT to the web server, which is accessed at the URL http://www.biolscience.cn/Deepm6A-MT/.

Transcriptional and functional motifs defining renal function revealed by single-nucleus RNA sequencing.

Recent advances in single-cell sequencing provide a unique opportunity to gain novel insights into the diversity, lineage, and functions of cell types constituting a tissue/organ. Here, we performed a single-nucleus study of the adult Drosophila renal system, consisting of Malpighian tubules and nephrocytes, which shares similarities with the mammalian kidney. We identified 11 distinct clusters representing renal stem cells, stellate cells, regionally specific principal cells, garland nephrocyte cells, and pericardial nephrocytes. Characterization of the transcription factors specific to each cluster identified fruitless (fru) as playing a role in stem cell regeneration and Hepatocyte nuclear factor 4 (Hnf4) in regulating glycogen and triglyceride metabolism. In addition, we identified a number of genes, including Rho guanine nucleotide exchange factor at 64C (RhoGEF64c), Frequenin 2 (Frq2), Prip, and CG1093 that are involved in regulating the unusual star shape of stellate cells. Importantly, the single-nucleus dataset allows visualization of the expression at the organ level of genes involved in ion transport and junctional permeability, providing a systems-level view of the organization and physiological roles of the tubules. Finally, a cross-species analysis allowed us to match the fly kidney cell types to mouse kidney cell types and planarian protonephridia, knowledge that will help the generation of kidney disease models. Altogether, our study provides a comprehensive resource for studying the fly kidney.

Enveloped viruses show increased propensity to cross-species transmission and zoonosis.

The transmission of viruses between different host species is a major source of emerging diseases and is of particular concern in the case of zoonotic transmission from mammals to humans. Several zoonosis risk factors have been identified, but it is currently unclear which viral traits primarily determine this process as previous work has focused on a few hundred viruses that are not representative of actual viral diversity. Here, we investigate fundamental virological traits that influence cross-species transmissibility and zoonotic propensity by interrogating a database of over 12,000 mammalian virus-host associations. Our analysis reveals that enveloped viruses tend to infect more host species and are more likely to be zoonotic than nonenveloped viruses, while other viral traits such as genome composition, structure, size, or the viral replication compartment play a less obvious role. This contrasts with the previous notion that viral envelopes did not significantly impact or even reduce zoonotic risk and should help better prioritize outbreak prevention efforts. We suggest several mechanisms by which viral envelopes could promote cross-species transmissibility, including structural flexibility of receptor-binding proteins and evasion of viral entry barriers.

Screening of novel disease genes of sepsis-induced myocardial Disfunction by RNA sequencing and bioinformatics analysis.

BACKGROUND: There is still a lack of effective treatment for sepsis-induced myocardial dysfunction (SIMD), while the pathogenesis of SIMD still remains largely unexplained. METHODS: RNA sequencing results (GSE267388 and GSE79962) were used for cross-species integrative analysis. Bioinformatic analyses were used to delve into function, tissue- and cell- specificity, and interactions of genes. External datasets and qRT-PCR experiments were used for validation. L1000 FWD was used to predict targeted drugs, and 3D structure files were used for molecular docking. RESULTS: Based on bioinformatic analyses, ten differentially expressed genes were selected as genes of interest, seven of which were verified to be significantly differential expression. Bucladesine was considered as a potential targeted drug for SIMD, which banded to seven target proteins primarily by forming hydrogen bonds. CONCLUSION: It was considered that Cebpd, Timp1, Pnp, Osmr, Tgm2, Cp, and Asb2 were novel disease genes, while bucladesine was a potential therapeutic drug, of SIMD.

A Cross-Species Neuroimaging Study of Sex Chromosome Dosage Effects on Human and Mouse Brain Anatomy.

All eutherian mammals show chromosomal sex determination with contrasting sex chromosome dosages (SCDs) between males (XY) and females (XX). Studies in transgenic mice and humans with sex chromosome trisomy (SCT) have revealed direct SCD effects on regional mammalian brain anatomy, but we lack a formal test for cross-species conservation of these effects. Here, we develop a harmonized framework for comparative structural neuroimaging and apply this to systematically profile SCD effects on regional brain anatomy in both humans and mice by contrasting groups with SCT (XXY and XYY) versus XY controls. Total brain size was substantially altered by SCT in humans (significantly decreased by XXY and increased by XYY), but not in mice. Robust and spatially convergent effects of XXY and XYY on regional brain volume were observed in humans, but not mice, when controlling for global volume differences. However, mice do show subtle effects of XXY and XYY on regional volume, although there is not a general spatial convergence in these effects within mice or between species. Notwithstanding this general lack of conservation in SCT effects, we detect several brain regions that show overlapping effects of XXY and XYY both within and between species (cerebellar, parietal, and orbitofrontal cortex), thereby nominating high priority targets for future translational dissection of SCD effects on the mammalian brain. Our study introduces a generalizable framework for comparative neuroimaging in humans and mice and applies this to achieve a cross-species comparison of SCD effects on the mammalian brain through the lens of SCT.SIGNIFICANCE STATEMENT Sex chromosome dosage (SCD) affects neuroanatomy and risk for psychopathology in humans. Performing mechanistic studies in the human brain is challenging but possible in mouse models. Here, we develop a framework for cross-species neuroimaging analysis and use this to show that an added X- or Y-chromosome significantly alters human brain anatomy but has muted effects in the mouse brain. However, we do find evidence for conserved cross-species impact of an added chromosome in the fronto-parietal cortices and cerebellum, which point to regions for future mechanistic dissection of sex chromosome dosage effects on brain development.

GERDH: an interactive multi-omics database for cross-species data mining in horticultural crops.

Horticultural plants contribute immensely to the quality of human's life. The rapid development of omics studies on horticultural plants has resulted in large volumes of valuable growth- and development-related data. Genes that are essential for growth and development are highly conserved in evolution. Cross-species data mining reduces the impact of species heterogeneity and has been extensively used for conserved gene identification. Owing to the lack of a comprehensive database for cross-species data mining using multi-omics data from all horticultural plant species, the current resources in this field are far from satisfactory. Here, we introduce GERDH (https://dphdatabase.com), a database platform for cross-species data mining among horticultural plants, based on 12 961 uniformly processed publicly available omics libraries from more than 150 horticultural plant accessions, including fruits, vegetables and ornamental plants. Important and conserved genes that are essential for a specific biological process can be obtained by cross-species analysis module with interactive web-based data analysis and visualization. Moreover, GERDH is equipped with seven online analysis tools, including gene expression, in-species analysis, epigenetic regulation, gene co-expression, enrichment/pathway and phylogenetic analysis. By interactive cross-species analysis, we identified key genes contributing to postharvest storage. By gene expression analysis, we explored new functions of CmEIN3 in flower development, which was validated by transgenic chrysanthemum analysis. We believe that GERDH will be a useful resource for key gene identification and will allow for omics big data to be more available and accessible to horticultural plant community members.