Semi-supervised integration of single-cell transcriptomics data.

Batch effects in single-cell RNA-seq data pose a significant challenge for comparative analyses across samples, individuals, and conditions. Although batch effect correction methods are routinely applied, data integration often leads to overcorrection and can result in the loss of biological variability. In this work we present STACAS, a batch correction method for scRNA-seq that leverages prior knowledge on cell types to preserve biological variability upon integration. Through an open-source benchmark, we show that semi-supervised STACAS outperforms state-of-the-art unsupervised methods, as well as supervised methods such as scANVI and scGen. STACAS scales well to large datasets and is robust to incomplete and imprecise input cell type labels, which are commonly encountered in real-life integration tasks. We argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction.

Genetic Analysis Algorithm for the Study of Patients with Multiple Congenital Anomalies and Isolated Congenital Heart Disease.

Congenital anomalies (CA) affect 3-5% of newborns, representing the second-leading cause of infant mortality in Argentina. Multiple congenital anomalies (MCA) have a prevalence of 2.26/1000 births in newborns, while congenital heart diseases (CHD) are the most frequent CA with a prevalence of 4.06/1000 births. The aim of this study was to identify the genetic causes in Argentinian patients with MCA and isolated CHD. We recruited 366 patients (172 with MCA and 194 with isolated CHD) born between June 2015 and August 2019 at public hospitals. DNA from peripheral blood was obtained from all patients, while karyotyping was performed in patients with MCA. Samples from patients presenting conotruncal CHD or DiGeorge phenotype (n = 137) were studied using MLPA. Ninety-three samples were studied by array-CGH and 18 by targeted or exome next-generation sequencing (NGS). A total of 240 patients were successfully studied using at least one technique. Cytogenetic abnormalities were observed in 13 patients, while 18 had clinically relevant imbalances detected by array-CGH. After MLPA, 26 patients presented 22q11 deletions or duplications and one presented a TBX1 gene deletion. Following NGS analysis, 12 patients presented pathogenic or likely pathogenic genetic variants, five of them, found in KAT6B, SHH, MYH11, MYH7 and EP300 genes, are novel. Using an algorithm that combines molecular techniques with clinical and genetic assessment, we determined the genetic contribution in 27.5% of the analyzed patients.

Selecting putative drought-tolerance markers in two contrasting soybeans.

Identifying high-yield genotypes under low water availability is essential for soybean climate-smart breeding. However, a major bottleneck lies in phenotyping, particularly in selecting cost-efficient markers associated with stress tolerance and yield stabilization. Here, we conducted in-depth phenotyping experiments in two soybean genotypes with contrasting drought tolerance, MUNASQA (tolerant) and TJ2049 (susceptible), to better understand soybean stress physiology and identify/statistically validate drought-tolerance and yield-stabilization traits as potential breeding markers. Firstly, at the critical reproductive stage (R5), the molecular differences between the genotype's responses to mild water deficit were explored through massive analysis of cDNA ends (MACE)-transcriptomic and gene ontology. MUNASQA transcriptional profile, compared to TJ2049, revealed significant differences when responding to drought. Next, both genotypes were phenotyped under mild water deficit, imposed in vegetative (V3) and R5 stages, by evaluating 22 stress-response, growth, and water-use markers, which were subsequently correlated between phenological stages and with yield. Several markers showed high consistency, independent of the phenological stage, demonstrating the effectiveness of the phenotyping methodology and its possible use for early selection. Finally, these markers were classified and selected according to their cost-feasibility, statistical weight, and correlation with yield. Here, pubescence, stomatal density, and canopy temperature depression emerged as promising breeding markers for the early selection of drought-tolerant soybeans.

Type 1 Insulin-Like Growth Factor Receptor Nuclear Localization in High-Grade Glioma Cells Enhances Motility, Metabolism, and In Vivo Tumorigenesis.

Gliomas are the most frequent solid tumors in children. Among these, high-grade gliomas are less common in children than in adults, though they are similar in their aggressive clinical behavior. In adults, glioblastoma is the most lethal tumor of the central nervous system. Insulin-like growth factor 1 receptor (IGF1R) plays an important role in cancer biology, and its nuclear localization has been described as an adverse prognostic factor in different tumors. Previously, we have demonstrated that, in pediatric gliomas, IGF1R nuclear localization is significantly associated with high-grade tumors, worst clinical outcome, and increased risk of death. Herein we explore the role of IGF1R intracellular localization by comparing two glioblastoma cell lines that differ only in their IGF1R capacity to translocate to the nucleus. In vitro, IGF1R nuclear localization enhances glioblastoma cell motility and metabolism without affecting their proliferation. In vivo, IGF1R has the capacity to translocate to the nucleus and allows not only a higher proliferation rate and the earlier development of tumors but also renders the cells sensitive to OSI906 therapy. With this work, we provide evidence supporting the implications of the presence of IGF1R in the nucleus of glioma cells and a potential therapeutic opportunity for patients harboring gliomas with IGF1R nuclear localization.

scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.

SUMMARY: A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here, we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. scGate outperforms state-of-the-art single-cell classifiers and it can be applied to multiple modalities of single-cell data (e.g. RNA-seq, ATAC-seq, CITE-seq). scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from heterogeneous single-cell datasets. AVAILABILITY AND IMPLEMENTATION: scGate is available as an R package at https://github.com/carmonalab/scGate (https://doi.org/10.5281/zenodo.6202614). Several reproducible workflows describing the main functions and usage of the package on different single-cell modalities, as well as the code to reproduce the benchmark, can be found at https://github.com/carmonalab/scGate.demo (https://doi.org/10.5281/zenodo.6202585) and https://github.com/carmonalab/scGate.benchmark. Test data are available at https://doi.org/10.6084/m9.figshare.16826071. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comprehensive Evolutionary Analysis of Complete Epstein-Barr Virus Genomes from Argentina and Other Geographies.

The sequence variability of the Epstein-Barr virus has been extensively studied throughout previous years in isolates from various geographic regions and consequent variations at both genetic and genomic levels have been described. However, isolates from South America were underrepresented in these studies. Here, we sequenced 15 complete EBV genomes that we analyzed together with publicly available raw NGS data for 199 EBV isolates from other parts of the globe by means of a custom-built bioinformatic pipeline. The phylogenetic relations of the genomes, the geographic structure and variability of the data set, and the evolution rates for the whole genome and each gene were assessed. The present work contributes to overcoming the scarcity of complete EBV genomes from South America and is the most comprehensive geography-related variability study, which involved determining the actual contribution of each EBV gene to the geographic segregation of the entire genome. Moreover, to the best of our knowledge, we established for the first time the evolution rate for the entire EBV genome based on a host-virus codivergence-independent assumption and assessed their evolution rates on a gene-by-gene basis, which were related to the encoded protein function. Considering the evolution of dsDNA viruses with a codivergence-independent approach may lay the basis for future research on EBV evolution. The exhaustive bioinformatic analysis performed on this new dataset allowed us to draw a novel set of conclusions regarding the genome evolution of EBV.

TDR Targets 6: driving drug discovery for human pathogens through intensive chemogenomic data integration.

The volume of biological, chemical and functional data deposited in the public domain is growing rapidly, thanks to next generation sequencing and highly-automated screening technologies. These datasets represent invaluable resources for drug discovery, particularly for less studied neglected disease pathogens. To leverage these datasets, smart and intensive data integration is required to guide computational inferences across diverse organisms. The TDR Targets chemogenomics resource integrates genomic data from human pathogens and model organisms along with information on bioactive compounds and their annotated activities. This report highlights the latest updates on the available data and functionality in TDR Targets 6. Based on chemogenomic network models providing links between inhibitors and targets, the database now incorporates network-driven target prioritizations, and novel visualizations of network subgraphs displaying chemical- and target-similarity neighborhoods along with associated target-compound bioactivity links. Available data can be browsed and queried through a new user interface, that allow users to perform prioritizations of protein targets and chemical inhibitors. As such, TDR Targets now facilitates the investigation of drug repurposing against pathogen targets, which can potentially help in identifying candidate targets for bioactive compounds with previously unknown targets. TDR Targets is available at https://tdrtargets.org.

p.R209H GH1 variant challenges short stature assessment.

OBJECTIVE: to describe the marked variability in clinical and biochemical patterns that are associated with a p.R209H GH1 missense variant in a large Argentinean pedigree, which makes the diagnosis of GHD elusive. DESIGN: We describe a non-consanguineous pedigree composed by several individuals with short stature, including 2 pediatric patients with typical diagnosis of isolated growth hormone deficiency (IGHD) and 4 other siblings with severe short stature, low serum IGF-1 and IGFBP-3, but normal stimulated GH levels, suggesting growth hormone insensitivity (GHI) in the latter group. RESULTS: Patients with classical IGHD phenotype carried a heterozygous variant in GH1: c.626G>A (p.R209H). Data from the extended pedigree suggested GH1 as the initial candidate gene, which showed the same pathogenic heterozygous GH1 variant in the four siblings with short stature and a biochemical pattern of GHI. CONCLUSIONS: We suggest considering GH1 sequencing in children with short stature associated to low IGF-1 and IGFBP-3 serum levels, even in the context of normal response to growth hormone provocative testing (GHPT).

Safety of standardised treatments for haematologic malignancies as regards to testicular endocrine function in children and teenagers.

STUDY QUESTION: Does standardised treatments used in children and adolescents with haematologic malignancies, including acute lymphoblastic (ALL) or myeloid leukaemia (AML) and non-Hodgkin lymphoma (NHL), affect endocrine function of the developing testes? SUMMARY ANSWER: Therapy of haematologic malignancies do not provoke an overt damage of Sertoli and Leydig cell populations, as revealed by normal levels of anti-Müllerian hormone (AMH) and testosterone, but a mild primary testicular dysfunction may be observed, compensated by moderate gonadotropin elevation, during pubertal development. WHAT IS KNOWN ALREADY: Evidence exists on the deleterious effect that chemotherapy and radiotherapy have on germ cells, and some attention has been given to the effects on Leydig and Sertoli cells of the adult gonads, but information is virtually non-existent on the effects of oncologic treatment on testicular somatic cell components during childhood and adolescence. STUDY DESIGN, SIZE, DURATION: A retrospective, analytical, observational study included 97 boys with haematological malignancies followed at two tertiary paediatric public hospitals in Buenos Aires, Argentina, between 2002 and 2015. PARTICIPANTS/MATERIALS, SETTING, METHODS: Clinical records of males aged 1-18 years, referred with the diagnoses of ALL, AML or NHL for the assessment of gonadal function, were eligible. We assessed serum levels of AMH and FSH as biomarkers of Sertoli cell endocrine function and testosterone and LH as biomarkers of Leydig cell function. MAIN RESULTS AND THE ROLE OF CHANCE: All hormone levels were normal in the large majority of patients until early pubertal development. From Tanner stage G3 onwards, while serum AMH and testosterone kept within the normal ranges, gonadotropins reached mildly to moderately elevated values in up to 35.9% of the cases, indicating a compensated Sertoli and/or Leydig cell dysfunction, which generally did not require hormone replacement therapy. LIMITATIONS, REASONS FOR CAUTION: Serum inhibin B determination and semen analysis were not available for most patients; therefore, we could not conclude on potential fertility impairment or identify whether primary Sertoli cell dysfunction resulted in secondary depleted spermatogenesis or whether primary germ cell damage impacted Sertoli cell function. WIDER IMPLICATIONS OF THE FINDINGS: The regimens used in the treatment of boys and adolescents with ALL, AML or NHL in the past two decades seem relatively safe for endocrine testicular function; nonetheless, a mild primary testicular endocrine dysfunction may be observed, usually compensated by slightly elevated gonadotropin secretion by the pituitary in adolescents, and not requiring hormone replacement therapy. No clinically relevant risk factor, such as severity of the disease or treatment protocol, could be identified in association with the compensated endocrine dysfunction. STUDY FUNDING/COMPETING INTEREST(S): This work was partially funded by grants PIP 11220130100687 of Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and PICT 2016-0993 of Fondo para la Investigación Científica y Tecnológica (FONCYT), Argentina. R.A.R., R.P.G. and P.B. have received honoraria from CONICET (Argentina) for technology services using the AMH ELISA. L.A.A. is part-time employee of CSL Behring Argentina. The other authors have no conflicts of interest to disclose.

Recombination rates along the entire Epstein Barr virus genome display a highly heterogeneous landscape.

Epstein Barr virus (EBV) has a large DNA genome assumed to be stable, but also subject to mutational processes such as nucleotide substitution and recombination, the latter explored to a lesser extent. Moreover, differences in the extent of recombination events across herpes sub-families were recently reported. Given the relevance of recombination in viral evolution and its possible impact in pathogenesis, we aimed to fully characterize and quantify its extension in all available EBV complete genome by assessing global and local recombination rate values (⍴/bp). Our results provide the first EBV recombination map based on recombination rates assessment, both at a global and gene by gene level, where the mean value for the entire genome was 0.035 (HPDI 0.020-0.062) ⍴/bp. We quantified how this evolutionary process changes along the EBV genome, and proved it to be non-homogeneous, since regulatory regions depicted the lowest recombination rate values while repetitive regions the highest signal. Moreover, GC content rich regions seem not to be linked to high recombination rates as previously reported. At an intragenic level, four genes (EBNA3C, EBNA3B, BRRF2 and BBLF2-BBLF3) presented a recombination rate above genome average. We specifically quantified the signal strength among different recombination-initiators previously described features and concluded that those which elicited the greatest amount of changes in ⍴/bp, TGGAG and CCCAG, were two well characterized recombination inducing motifs in eukaryotic cells. Strikingly, although TGGAG was not the most frequently detected DNA motif across the EBV genome (697 hits), it still induced a significantly greater proportion of initiation events (0.025 events/hits) than other more represented motifs, p-value = 0.04; one tailed proportion test. Present results support the idea that diversity and evolution of herpesviruses are impacted by mechanisms, such as recombination, which extends beyond the usual consideration of point mutations.

Reprogramming human A375 amelanotic melanoma cells by catalase overexpression: Upregulation of antioxidant genes correlates with regression of melanoma malignancy and with malignant progression when downregulated.

Reactive oxygen species (ROS) are implicated in tumor transformation. The antioxidant system (AOS) protects cells from ROS damage. However, it is also hijacked by cancers cells to proliferate within the tumor. Thus, identifying proteins altered by redox imbalance in cancer cells is an attractive prognostic and therapeutic tool. Gene expression microarrays in A375 melanoma cells with different ROS levels after overexpressing catalase were performed. Dissimilar phenotypes by differential compensation to hydrogen peroxide scavenging were generated. The melanotic A375-A7 (A7) upregulated TYRP1, CNTN1 and UCHL1 promoting melanogenesis. The metastatic A375-G10 (G10) downregulated MTSS1 and TIAM1, proteins absent in metastasis. Moreover, differential coexpression of AOS genes (EPHX2, GSTM3, MGST1, MSRA, TXNRD3, MGST3 and GSR) was found in A7 and G10. Their increase in A7 improved its AOS ability and therefore, oxidative stress response, resembling less aggressive tumor cells. Meanwhile, their decrease in G10 revealed a disruption in the AOS and therefore, enhanced its metastatic capacity.These gene signatures, not only bring new insights into the physiopathology of melanoma, but also could be relevant in clinical prognostic to classify between non aggressive and metastatic melanomas.

Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing.

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules.

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.

Mining the modular structure of protein interaction networks.

BACKGROUND: Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. METHODOLOGY: We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera's cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. RESULTS: As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge.