Identification of a Human Clonogenic Progenitor with Strict Monocyte Differentiation Potential: A Counterpart of Mouse cMoPs.

Monocytes give rise to macrophages and dendritic cells (DCs) under steady-state and inflammatory conditions, thereby contributing to host defense and tissue pathology. A common monocyte progenitor (cMoP) that is strictly committed to the monocyte lineage has been recently identified in mice. Here, we identified human cMoPs as a CLEC12AhiCD64hi subpopulation of conventional granulocyte-monocyte progenitors (cGMPs) in umbilical cord blood and in bone marrow. Human cMoPs gave rise to monocyte subsets without showing any potential for differentiating into myeloid or lymphoid cells. Within the cGMP population, we also identified revised GMPs that completely lacked DC and lymphoid potential. Collectively, our findings expand and revise the current understanding of human myeloid cell differentiation pathways.

scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning.

Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing (scRNA-seq) data that allows the study of heterogeneity across multiple cell populations. Currently, this is most commonly done using unsupervised clustering algorithms, which project single-cell expression data into a lower dimensional space and then cluster cells based on their distances from each other. However, as these methods do not use reference datasets, they can only achieve a rough classification of cell-types, and it is difficult to improve the recognition accuracy further. To effectively solve this issue, we propose a novel supervised annotation method, scDeepInsight. The scDeepInsight method is capable of performing manifold assignments. It is competent in executing data integration through batch normalization, performing supervised training on the reference dataset, doing outlier detection and annotating cell-types on query datasets. Moreover, it can help identify active genes or marker genes related to cell-types. The training of the scDeepInsight model is performed in a unique way. Tabular scRNA-seq data are first converted to corresponding images through the DeepInsight methodology. DeepInsight can create a trainable image transformer to convert non-image RNA data to images by comprehensively comparing interrelationships among multiple genes. Subsequently, the converted images are fed into convolutional neural networks such as EfficientNet-b3. This enables automatic feature extraction to identify the cell-types of scRNA-seq samples. We benchmarked scDeepInsight with six other mainstream cell annotation methods. The average accuracy rate of scDeepInsight reached 87.5%, which is more than 7% higher compared with the state-of-the-art methods.

Advances in AI and machine learning for predictive medicine.

The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction. This review explores a revolutionary shift in predictive modeling through the application of deep learning (DL), specifically convolutional neural networks (CNNs). Using transformation methods such as DeepInsight, omics data with independent variables in tabular (table-like, including vector) form can be turned into image-like representations, enabling CNNs to capture latent features effectively. This approach not only enhances predictive power but also leverages transfer learning, reducing computational time, and improving performance. However, integrating CNNs in predictive omics data analysis is not without challenges, including issues related to model interpretability, data heterogeneity, and data size. Addressing these challenges requires a multidisciplinary approach, involving collaborations between ML experts, bioinformatics researchers, biologists, and medical doctors. This review illuminates these complexities and charts a course for future research to unlock the full predictive potential of CNNs in omics data analysis and related fields.

DeepFeature: feature selection in nonimage data using convolutional neural network.

Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical tests. Among these methods, deep neural networks are currently among the most advanced approaches and, in particular, convolutional neural networks (CNNs) have been shown to perform excellently for a variety of difficult tasks. Despite that applications of this type of networks to high-dimensional omics data and, most importantly, meaningful interpretation of the results returned from such models in a biomedical context remains an open problem. Here we present, an approach applying a CNN to nonimage data for feature selection. Our pipeline, DeepFeature, can both successfully transform omics data into a form that is optimal for fitting a CNN model and can also return sets of the most important genes used internally for computing predictions. Within the framework, the Snowfall compression algorithm is introduced to enable more elements in the fixed pixel framework, and region accumulation and element decoder is developed to find elements or genes from the class activation maps. In comparative tests for cancer type prediction task, DeepFeature simultaneously achieved superior predictive performance and better ability to discover key pathways and biological processes meaningful for this context. Capabilities offered by the proposed framework can enable the effective use of powerful deep learning methods to facilitate the discovery of causal mechanisms in high-dimensional biomedical data.

Variants encoding a restricted carboxy-terminal domain of SLC12A2 cause hereditary hearing loss in humans.

Hereditary hearing loss is challenging to diagnose because of the heterogeneity of the causative genes. Further, some genes involved in hereditary hearing loss have yet to be identified. Using whole-exome analysis of three families with congenital, severe-to-profound hearing loss, we identified a missense variant of SLC12A2 in five affected members of one family showing a dominant inheritance mode, along with de novo splice-site and missense variants of SLC12A2 in two sporadic cases, as promising candidates associated with hearing loss. Furthermore, we detected another de novo missense variant of SLC12A2 in a sporadic case. SLC12A2 encodes Na+, K+, 2Cl- cotransporter (NKCC) 1 and plays critical roles in the homeostasis of K+-enriched endolymph. Slc12a2-deficient mice have congenital, profound deafness; however, no human variant of SLC12A2 has been reported as associated with hearing loss. All identified SLC12A2 variants mapped to exon 21 or its 3'-splice site. In vitro analysis indicated that the splice-site variant generates an exon 21-skipped SLC12A2 mRNA transcript expressed at much lower levels than the exon 21-included transcript in the cochlea, suggesting a tissue-specific role for the exon 21-encoded region in the carboy-terminal domain. In vitro functional analysis demonstrated that Cl- influx was significantly decreased in all SLC12A2 variants studied. Immunohistochemistry revealed that SLC12A2 is located on the plasma membrane of several types of cells in the cochlea, including the strial marginal cells, which are critical for endolymph homeostasis. Overall, this study suggests that variants affecting exon 21 of the SLC12A2 transcript are responsible for hereditary hearing loss in humans.

Association between high immune activity and worse prognosis in uveal melanoma and low-grade glioma in TCGA transcriptomic data.

BACKGROUND: Immune status in the tumor microenvironment is an important determinant of cancer progression and patient prognosis. Although a higher immune activity is often associated with a better prognosis, this trend is not absolute and differs across cancer types. We aimed to give insights into why some cancers do not show better survival despite higher immunity by assessing the relationship between different biological factors, including cytotoxicity, and patient prognosis in various cancer types using RNA-seq data collected by The Cancer Genome Atlas. RESULTS: Results showed that a higher immune activity was associated with worse overall survival in patients with uveal melanoma and low-grade glioma, which are cancers of immune-privileged sites. In these cancers, epithelial or endothelial mesenchymal transition and inflammatory state as well as immune activation had a notable negative correlation with patient survival. Further analysis using additional single-cell data of uveal melanoma and glioma revealed that epithelial or endothelial mesenchymal transition was mainly induced in retinal pigment cells or endothelial cells that comprise the blood-retinal and blood-brain barriers, which are unique structures of the eye and central nervous system, respectively. Inflammation was mainly promoted by macrophages, and their infiltration increased significantly in response to immune activation. Furthermore, we found the expression of inflammatory chemokines, particularly CCL5, was strongly correlated with immune activity and associated with poor survival, particularly in these cancers, suggesting that these inflammatory mediators are potential molecular targets for therapeutics. CONCLUSIONS: In uveal melanoma and low-grade glioma, inflammation from macrophages and epithelial or endothelial mesenchymal transition are particularly associated with a poor prognosis. This implies that they loosen the structures of the blood barrier and impair homeostasis and further recruit immune cells, which could result in a feedback loop of additional inflammatory effects leading to runaway conditions.

Utility of tissue-specific gene expression scores for gene prioritization in Mendelian diseases.

In genetic testing of Mendelian diseases, it is a bioinformatics challenge to effectively prioritize disease-causing candidate genes listed from massively parallel sequencing. Tissue specificity of the gene expression levels may give a clue because it may reflect tissue-specific disease manifestation. However, considering poor correlations between mRNA and protein expression in some genes, it is not clear whether transcriptomics- or proteomics-based tissue specificity should be used to prioritize candidate genes. Therefore, we compared the efficiency of tissue-specific scores (TS scores) obtained from transcriptome and proteome data in prioritizing candidate genes for whole exome sequencing (WES) analysis of Mendelian disease patients. We show that both Protein and RNA TS scores are useful in prioritizing candidate genes in WES analysis, although diseases like coagulopathies get more benefit from Protein TS score. This study may provide useful evidence in developing new methods to effectively identify novel disease-causing genes.

Four pedigrees with aminoacyl-tRNA synthetase abnormalities.

Aminoacyl tRNA synthetases (ARSs) are highly conserved enzymes that link amino acids to their cognate tRNAs. Thirty-seven ARSs are known and their deficiencies cause various genetic disorders. Variants in some ARSs are associated with the autosomal dominant inherited form of axonal neuropathy, including Charcot-Marie-Tooth (CMT) disease. Variants of genes encoding ARSs often cause disorders in an autosomal recessive fashion. The clinical features of cytosolic ARS deficiencies are more variable, including systemic features. Deficiencies of ARSs localized in the mitochondria are often associated with neurological disorders including Leigh and early-onset epileptic syndromes. Whole exome sequencing (WES) is an efficient way to identify the genes causing various symptoms in patients. We identified 4 pedigrees with novel compound heterozygous variants in ARS genes (WARS1, MARS1, AARS2, and PARS2) by WES. Some unique manifestations were noted. The number of patients with ARSs has been increasing since the application of WES. Our findings broaden the known genetic and clinical spectrum associated with ARS variants.

SPECTRA: a tool for enhanced brain wave signal recognition.

BACKGROUND: Brain wave signal recognition has gained increased attention in neuro-rehabilitation applications. This has driven the development of brain-computer interface (BCI) systems. Brain wave signals are acquired using electroencephalography (EEG) sensors, processed and decoded to identify the category to which the signal belongs. Once the signal category is determined, it can be used to control external devices. However, the success of such a system essentially relies on significant feature extraction and classification algorithms. One of the commonly used feature extraction technique for BCI systems is common spatial pattern (CSP). RESULTS: The performance of the proposed spatial-frequency-temporal feature extraction (SPECTRA) predictor is analysed using three public benchmark datasets. Our proposed predictor outperformed other competing methods achieving lowest average error rates of 8.55%, 17.90% and 20.26%, and highest average kappa coefficient values of 0.829, 0.643 and 0.595 for BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, respectively. CONCLUSIONS: Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.

Forecasting the spread of COVID-19 using LSTM network.

BACKGROUND: The novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed. RESULTS: The results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand's data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19. CONCLUSION: The forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19.

Landscape of prognostic signatures and immunogenomics of the AXL/GAS6 axis in renal cell carcinoma.

BACKGROUND: Cabozantinib is an oral tyrosine kinase inhibitor in renal cell carcinoma (RCC), whose targets include oncogenic AXL and unique ligand GAS6. Critical gaps in basic knowledge need to be addressed to devise an exclusive biomarker and candidate when targeting the AXL/GAS6 axis. METHODS: To clarify the effects of the AXL/GAS6 axis on RCC, we herein performed a large-scale immunogenomic analysis and single-cell counts including various metastatic organs and histological subtypes of RCC. We further applied genome-wide mutation analyses and methylation arrays. RESULTS: Varying patterns of AXL and GAS6 expression were observed throughout primary RCC tumours and metastases. Scoring individual AXL/GAS6 levels in the tumour centre and invasive margin, namely, the AXL/GAS6 score, showed a good ability to predict the prognosis of clear cell RCC. Metastasis- and histological subtype-specific differences in the AXL/GAS6 score existed since lung metastasis and the papillary subtype were weakly related to the AXL/GAS6 axis. Cell-by-cell immunohistological assessments clarified an immunosuppressive environment in tumours with high AXL/GAS6 scores. Genomic alterations in the PI3K-mTOR pathway and DNA methylation profiling revealed distinct differences with the AXL/GAS6 score in ccRCC. CONCLUSION: The AXL/GAS6 scoring system could predict the outcome of prognosis and work as a robust biomarker for the immunogenomic state in RCC.

Multiplexed single-cell pathology reveals the association of CD8 T-cell heterogeneity with prognostic outcomes in renal cell carcinoma.

Despite the high sensitivity of renal cell carcinoma (RCC) to immunotherapy, RCC has been recognized as an unusual disease in which CD8+ T-cell infiltration into the tumor beds is related to a poor prognosis. To approach the inner landscape of immunobiology of RCC, we performed multiplexed seven-color immunohistochemistry (CD8, CD39, PD-1, Foxp3, PD-L1, and pan-cytokeratin AE1/AE3 with DAPI), which revealed the automated single-cell counts and calculations of individual cell-to-cell distances. In total, 186 subjects were included, in which CD39 was used as a marker for distinguishing tumor-specific (CD39+) and bystander (CD39-) T-cells. Our clear cell RCC cohort also revealed a poor prognosis if the tumor showed increased CD8+ T-cell infiltration. Intratumoral CD8+CD39+ T-cells as well as their exhausted CD8+CD39+PD-1+ T-cells in the central tumor areas enabled the subgrouping of patients according to malignancy. Analysis using specimens post-antiangiogenic treatment revealed a dramatic increase in proliferative Treg fraction Foxp3+PD-1+ cells, suggesting a potential mechanism of hyperprogressive disease after uses of anti-PD-1 antibody. Our cell-by-cell study platform provided spatial information on tumors, where bystander CD8+CD39- T-cells were dominant in the invasive margin areas. We uncovered a potential interaction between CD8+CD39+PD-1+ T-cells and Foxp3+PD-1+ Treg cells due to cell-to-cell proximity, forming a spatial niche more specialized in immunosuppression under PD-1 blockade. A paradigm shift to the immunosuppressive environment was more obvious in metastatic lesions; rather the infiltration of Foxp3+ and Foxp3+PD-1+ Treg cells was more pronounced. With this multiplexed single-cell pathology technique, we revealed further insight into the immunobiological standing of RCC.

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.

Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.

Association of an IGHV3-66 gene variant with Kawasaki disease.

In a meta-analysis of three GWAS for susceptibility to Kawasaki disease (KD) conducted in Japan, Korea, and Taiwan and follow-up studies with a total of 11,265 subjects (3428 cases and 7837 controls), a significantly associated SNV in the immunoglobulin heavy variable gene (IGHV) cluster in 14q33.32 was identified (rs4774175; OR = 1.20, P = 6.0 × 10-9). Investigation of nonsynonymous SNVs of the IGHV cluster in 9335 Japanese subjects identified the C allele of rs6423677, located in IGHV3-66, as the most significant reproducible association (OR = 1.25, P = 6.8 × 10-10 in 3603 cases and 5731 controls). We observed highly skewed allelic usage of IGHV3-66, wherein the rs6423677 A allele was nearly abolished in the transcripts in peripheral blood mononuclear cells of both KD patients and healthy adults. Association of the high-expression allele with KD strongly indicates some active roles of B-cells or endogenous immunoglobulins in the disease pathogenesis. Considering that significant association of SNVs in the IGHV region with disease susceptibility was previously known only for rheumatic heart disease (RHD), a complication of acute rheumatic fever (ARF), these observations suggest that common B-cell related mechanisms may mediate the symptomology of KD and ARF as well as RHD.

Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles.

BACKGROUND: DNA-binding proteins perform important roles in cellular processes and are involved in many biological activities. These proteins include crucial protein-DNA binding domains and can interact with single-stranded or double-stranded DNA, and accordingly classified as single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins (DSBs). Computational prediction of SSBs and DSBs helps in annotating protein functions and understanding of protein-binding domains. RESULTS: Performance is reported using the DNA-binding protein dataset that was recently introduced by Wang et al., [1]. The proposed method achieved a sensitivity of 0.600, specificity of 0.792, AUC of 0.758, MCC of 0.369, accuracy of 0.744, and F-measure of 0.536, on the independent test set. CONCLUSION: The proposed method with the hidden Markov model (HMM) profiles for feature extraction, outperformed the benchmark method in the literature and achieved an overall improvement of approximately 3%. The source code and supplementary information of the proposed method is available at https://github.com/roneshsharma/Predict-DNA-binding-proteins/wiki.

Homozygous ADCY5 mutation causes early-onset movement disorder with severe intellectual disability.

BACKGROUND: Mutations of theADCY5 have been identified in patients with familial dyskinesia, early-onsetautosomal dominant chorea and dystonia, and benign hereditary chorea. Most ofthe ADCY5 mutations are de novo or transmitted in an autosomal dominantfashion. Only two pedigrees are known to show autosomal recessive inheritance. OBJECTIVES: We report twosiblings with severe ID, dystonic movement, and growth failure with unknownetiology. METHODS: We planned a proband-parentapproach using whole exome sequencing. RESULTS: Homozygous mutationin exon 21 of the ADCY5 (p.R1238W) was identified in the siblings. Althoughtheir parents were heterozygous for the mutation, they were free from clinicalmanifestations. CONCLUSIONS: Our results furtherexpand the phenotype/genotype correlations of the ADCY5-related disorders.Mutations of ADCY5 should be considered in pediatric patients with ID andinvoluntary movement.

Quantification of multicellular colonization in tumor metastasis using exome-sequencing data.

Metastasis is a major cause of cancer-related mortality, and it is essential to understand how metastasis occurs in order to overcome it. One relevant question is the origin of a metastatic tumor cell population. Although the hypothesis of a single-cell origin for metastasis from a primary tumor has long been prevalent, several recent studies using mouse models have supported a multicellular origin of metastasis. Human bulk whole-exome sequencing (WES) studies also have demonstrated a multiple "clonal" origin of metastasis, with different mutational compositions. Specifically, there has not yet been strong research to determine how many founder cells colonize a metastatic tumor. To address this question, under the metastatic model of "single bottleneck followed by rapid growth," we developed a method to quantify the "founder cell population size" in a metastasis using paired WES data from primary and metachronous metastatic tumors. Simulation studies demonstrated the proposed method gives unbiased results with sufficient accuracy in the range of realistic settings. Applying the proposed method to real WES data from four colorectal cancer patients, all samples supported a multicellular origin of metastasis and the founder size was quantified, ranging from 3 to 17 cells. Such a wide-range of founder sizes estimated by the proposed method suggests that there are large variations in genetic similarity between primary and metastatic tumors in the same subjects, which may explain the observed (dis)similarity of drug responses between tumors.

Predicting protein-peptide binding sites with a deep convolutional neural network.

MOTIVATION: Interactions between proteins and peptides influence biological functions. Predicting such bio-molecular interactions can lead to faster disease prevention and help in drug discovery. Experimental methods for determining protein-peptide binding sites are costly and time-consuming. Therefore, computational methods have become prevalent. However, existing models show extremely low detection rates of actual peptide binding sites in proteins. To address this problem, we employed a two-stage technique - first, we extracted the relevant features from protein sequences and transformed them into images applying a novel method and then, we applied a convolutional neural network to identify the peptide binding sites in proteins. RESULTS: We found that our approach achieves 67% sensitivity or recall (true positive rate) surpassing existing methods by over 35%.

MYCN de novo gain-of-function mutation in a patient with a novel megalencephaly syndrome.

BACKGROUND: In this study, we aimed to identify the gene abnormality responsible for pathogenicity in an individual with an undiagnosed neurodevelopmental disorder with megalencephaly, ventriculomegaly, hypoplastic corpus callosum, intellectual disability, polydactyly and neuroblastoma. We then explored the underlying molecular mechanism. METHODS: Trio-based, whole-exome sequencing was performed to identify disease-causing gene mutation. Biochemical and cell biological analyses were carried out to elucidate the pathophysiological significance of the identified gene mutation. RESULTS: We identified a heterozygous missense mutation (c.173C>T; p.Thr58Met) in the MYCN gene, at the Thr58 phosphorylation site essential for ubiquitination and subsequent MYCN degradation. The mutant MYCN (MYCN-T58M) was non-phosphorylatable at Thr58 and subsequently accumulated in cells and appeared to induce CCND1 and CCND2 expression in neuronal progenitor and stem cells in vitro. Overexpression of Mycn mimicking the p.Thr58Met mutation also promoted neuronal cell proliferation, and affected neuronal cell migration during corticogenesis in mouse embryos. CONCLUSIONS: We identified a de novo c.173C>T mutation in MYCN which leads to stabilisation and accumulation of the MYCN protein, leading to prolonged CCND1 and CCND2 expression. This may promote neurogenesis in the developing cerebral cortex, leading to megalencephaly. While loss-of-function mutations in MYCN are known to cause Feingold syndrome, this is the first report of a germline gain-of-function mutation in MYCN identified in a patient with a novel megalencephaly syndrome similar to, but distinct from, CCND2-related megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome. The data obtained here provide new insight into the critical role of MYCN in brain development, as well as the consequences of MYCN defects.

OPAL+: Length-Specific MoRF Prediction in Intrinsically Disordered Protein Sequences.

Intrinsically disordered proteins (IDPs) contain long unstructured regions, which play an important role in their function. These intrinsically disordered regions (IDRs) participate in binding events through regions called molecular recognition features (MoRFs). Computational prediction of MoRFs helps identify the potentially functional regions in IDRs. In this study, OPAL+, a novel MoRF predictor, is presented. OPAL+ uses separate models to predict MoRFs of varying lengths along with incorporating the hidden Markov model (HMM) profiles and physicochemical properties of MoRFs and their flanking regions. Together, these features help OPAL+ achieve a marginal performance improvement of 0.4-0.7% over its predecessor for diverse MoRF test sets. This performance improvement comes at the expense of increased run time as a result of the requirement of HMM profiles. OPAL+ is available for download at https://github.com/roneshsharma/OPAL-plus/wiki/OPAL-plus-Download.