Next-generation sequencing allows researchers to efficiently determine the sequences of hundreds of millions of short DNA fragments from an experiment. Many experiments use next-generation sequencing to count nucleic acid molecules in a population by sequencing small fragments of them and assigning them to different genomic features. To find the origins of those fragments, the corresponding sequences are aligned to the genome; these alignments can then be used in downstream analyses. However, this alignment process is complicated by the fact that the genome has many highly similar and repetitive sequences, making it difficult or impossible to unambiguously assign some sequences to a single genomic location. The common “solution” to this problem is to discard those sequencing reads that do not align to a single site; however, this can lead to significant biases and will hide an important part of the genome. To address this problem, we have developed SmartMap, which serves to process and appropriately weight the alignments of reads that map to more than one genomic location. This enables us to examine many genomic regions that were previously “invisible” to analysis and helps us draw new insights into the regulation and function of repetitive elements of the genome.

Background

The collagen 3 alpha 1 (COL3A1) rs1800255 polymorphism has been reported to be associated with women pelvic organ prolapse (POP) susceptibility, but the results of these previous studies have been contradictory. The objective of current study is to explore whether COL3A1 rs1800255 polymorphism confers risk to POP.

In bacteria, faithful genome inheritance requires the two replicated DNA molecules to be segregated at the opposite halves of the cell. ParABS, the most widespread bacterial DNA segregation system, is composed of a centromere sequence, parS, and two proteins, the ParA ATPase and the ParB DNA binding protein. Hundreds of ParB assemble dynamically to form clusters around parS, which then serve as substrates for ParA molecules to catalyze the positioning and segregation events. The nature of these clusters and their interaction with DNA have remained elusive. Here, we propose a realistic minimal model that captures quantitatively the peculiar DNA binding profile of ParB in the vicinity of parS in Escherichia coli. From the viewpoint of DNA, the only fitting parameter is the in vivo supercoiling density resulting from the removal of DNA helices by toposiomerases, which is in accord with previous independent estimations. From the viewpoint of ParB clusters, we predict that they behave like liquid-like protein condensates with unconventional boundaries. Namely, we predict boundaries to be leaky (i.e. not sharp) as a result of the non-equilibrium protein production, diffusion and dilution. Altogether, our work provides novel insights into bacterial DNA organization and intracellular liquid-liquid phase separation.

Genome-wide association studies (GWAS) have identified many loci associated with complex traits and diseases. Expression quantitative trait loci (eQTL) may help to explain mechanisms of GWAS associations, if the gene has a role as a mediator of the trait or disease. Loci that exhibit allelic heterogeneity, that is, loci containing multiple causal variants, offer the opportunity to investigate whether effects are concordant and proportional across eQTL and GWAS; if the gene is a partial mediator of the trait, the sign and size of the effects across distinct eQTL variants should be reflected in GWAS associations. Such a Mendelian Randomization (MR) analysis of individual loci is complicated by moderate sample sizes in eQTL studies and linkage disequilibrium (LD), resulting in complex patterns of estimated effect sizes for eQTL and GWAS. We develop a statistical model, MRLocus, with two steps: selection of eQTL SNPs to act as instruments in the MR analysis of a genetic locus, and estimation of the gene-to-trait mediation effect taking instrument uncertainty into account. In simulation, the method has higher accuracy and better uncertainty measures compared to other competing methods, and we compare its estimates on candidate causal gene-trait pairs from literature.

Dissecting the genetic basis of parallel evolution, the independent evolution of similar phenotypes in similar environments among closely related lineages, allows evolutionary biologists to test whether evolution is predictable at the molecular level. Relatively little is still known about the genetics of parallel evolution in quantitative traits. Here we identify significant phenotypic and genomic parallel evolution in quantitative traits across two latitudinal transects of wild house mice in eastern and western North America. We find parallel evolution in thermally adaptive phenotypes (nest building behavior and body mass) and in genes involved in temperature-related traits such as body mass, metabolism, and temperature-sensing using population genomic scans for selection. We also find considerable divergent phenotypic and genomic evolution between eastern and western transects corresponding to known environmental differences between these transects. In this case, the evolution of quantitative traits across similar latitudinal transects involved a mixture of unique and shared responses to selection at the molecular level.

Background

Nine previous meta-analyses have been published to analyze the CYP1A1 T3801C and A2455G polymorphisms with BC risk. However, they did not assess the credibility of statistically significant associations. In addition, many new studies have been reported on the above themes. Hence, we conducted an updated systematic review and meta-analysis to further explore the above issues.

Background

Despite decades of extensive research, bovine respiratory disease (BRD) remains the most devastating disease in beef cattle production. Establishing a clinical diagnosis often relies upon visual detection of non-specific signs, leading to low diagnostic accuracy. Thus, post-weaned beef cattle are often metaphylactically administered antimicrobials at facility arrival, which poses concerns regarding antimicrobial stewardship and resistance. Additionally, there is a lack of high-quality research that addresses the gene-by-environment interactions that underlie why some cattle that develop BRD die while others survive. Therefore, it is necessary to decipher the underlying host genomic factors associated with BRD mortality versus survival to help determine BRD risk and severity. Using transcriptomic analysis of at-arrival whole blood samples from cattle that died of BRD, as compared to those that developed signs of BRD but lived (n = 3 DEAD, n = 3 ALIVE), we identified differentially expressed genes (DEGs) and associated pathways in cattle that died of BRD. Additionally, we evaluated unmapped reads, which are often overlooked within transcriptomic experiments.

Information about the external environment is processed by populations of sensory neurons using a combinatorial neural code comprised of spikes from multiple neurons. In many cases, this information has to be compressed before reaching downstream circuits due to limitations on axonal transmission properties and the metabolic cost of firing. A prominent theoretical framework known as efficient coding postulates that sensory populations transmit maximal information about sensory stimuli subject to metabolic constraints. Although used to successfully predict the shape of receptive fields in the insect and mammalian retina, this theory has often considered linear neurons or a single source of noise. Here, we derived a framework for information transfer in populations of noisy nonlinear neurons, specifically addressing the implications of the location of the noise source, and the way the sensory stimulus converges downstream, on the efficiency of information transfer. Our theoretical predictions support efficient coding in experimentally recorded populations of auditory nerve fibers coding for sound intensity.

Transcriptome-wide association studies (TWAS) can improve the statistical power of genetic association studies by leveraging the relationship between genetically predicted transcript expression levels and an outcome. We propose a new TWAS pipeline that integrates data on the genetic regulation of expression levels across multiple tissues. We generate cross-tissue expression features using sparse canonical correlation analysis and then combine evidence for expression-outcome association across cross- and single-tissue features using the aggregate Cauchy association test. We show that this approach has substantially higher power than traditional single-tissue TWAS methods. Application of these methods to publicly available summary statistics for ten complex traits also identifies associations missed by single-tissue methods.

While parents contribute equally to the DNA of offspring, some genes are imprinted, or differentially expressed depending on which parent the allele is passed from. In plants, imprinted genes are expressed in the endosperm, and prior studies have missed many imprinted genes. Here, we present a new method which identifies imprinted genes and transposable elements using all parental differences in sequence, including presence-absence variation between parents, rather than single nucleotide polymorphisms alone. We find that the majority of imprinted genes with maternal expression are genes and transposable elements that are present in only one genome and that are not shared with other grass species. These transcripts are often expressed primarily in the endosperm where epigenetic marks on the maternal alleles are distinct from the rest of the plant. We also find evidence of fused transcripts with transposable elements upstream of genes, implicating common regulation for imprinted transposable elements and some nearby genes.

Background

Leptospirosis is a zoonotic, bacterial disease, posing significant health risks to humans, livestock, and companion animals around the world. Symptoms range from asymptomatic to multi-organ failure in severe cases. Complex species-specific interactions exist between animal hosts and the infecting species, serovar, and strain of pathogen. Leptospira borgpetersenii serovar Hardjo strains HB203 and JB197 have a high level of genetic homology but cause different clinical presentation in the hamster model of infection; HB203 colonizes the kidney and presents with chronic shedding while JB197 causes severe organ failure and mortality. This study examines the transcriptome of L. borgpetersenii and characterizes differential gene expression profiles of strains HB203 and JB197 cultured at temperatures during routine laboratory conditions (29°C) and encountered during host infection (37°C).

Leptospirosis is a global zoonotic, neglected tropical disease. Interestingly, a high level of species specificity (both bacteria and host) plays a major role in the severity of disease presentation which can vary from asymptomatic to multi-organ failure. Pathogenic Leptospira colonize the kidneys of infected individuals and are shed in urine into the environment where they can survive until they are contracted by another host. This study looks at two strains of L. borgpetersenii, HB203 and JB197 which are genetically very similar, and identical by serotyping as serovar Hardjo, yet HB203 causes a chronic infection in the hamster while JB197 causes organ failure and mortality. To better characterize bacterial factors causing different disease outcomes, we examined the gene expression profile of these strains in the context of temperatures that would reflect natural Leptospira life cycles (environmentally similar 29°C and 37°C which is more indicative of host environment). We found vast differences in gene expression both between the strains and within strains between temperatures. Characterization of the transcriptome of L. borgpetersenii serovar Hardjo strains JB197 and HB203 provides insights into factors that can determine acute versus chronic disease in the hamster model of infection. Additionally, these studies highlight strain to strain variability within the same species, and serovar, at different growth temperatures, which needs to be considered when serovars are selected and propagated for use as bacterin vaccines used to immunize domestic animal species.

Biological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Due to the technical limitations, spatial transcriptomics data suffers from only sparsely measured mRNAs by in-situ capture and possibly missing spots in tissue regions that entirely failed fixing and permeabilizing RNAs. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information.

Pre-mRNA splicing is an important step in the processing of the genomic information during gene expression. During splicing, introns are excised from a gene transcript, and the remaining exons are ligated. Our work concerns one its particular subtype, which involves the so-called tandem alternative splice sites, a group of closely located exon borders that are used alternatively. We analyzed RNA-seq measurements of gene expression provided by the Genotype-Tissue Expression (GTEx) project, the largest to-date collection of such measurements in healthy human tissues, and constructed a detailed catalogue of tandem alternative splice sites. Within this catalogue, we characterized patterns of tissue-specific expression, regulation, impact on protein structure, and evolutionary selection acting on tandem alternative splice sites. In a number of genes, we predicted regulatory mechanisms that could be responsible for choosing one of many tandem alternative splice sites. The results of this study provide an invaluable resource for molecular biologists studying alternative splicing.

Background and purpose

The role of copy number variation (CNV) variation in stroke susceptibility and outcome has yet to be explored. The Copy Number Variation and Stroke (CaNVAS) Risk and Outcome study addresses this knowledge gap.

In HIV-infected individuals, a small fraction of the infected cells persist and divide. This reservoir persists during fully suppressive ART and can rekindle the infection if ART is discontinued. Because the number of possible sites of HIV DNA integration is very large, each infected cell, and all of its descendants, can be identified by the site where the provirus is integrated (IS). To understand the selective forces that determine the fates of infected cells in vivo, we compared the distribution of HIV IS in freshly-infected cells to cells from HIV-infected donors sampled both before and during ART. We found that, as previously reported, integration favors highly-expressed genes. However, over time, the fraction of cells with proviruses integrated in highly-expressed genes decreases, implying that they grow less well. There are exceptions to this broad negative selection. When a provirus is integrated in a specific region in one of seven genes, the proviruses affect the expression of the target gene, promoting growth and/or survival of the cell. Although this effect is striking, it is only a minor component of the forces that promote the growth and survival of the population of infected cells during ART.

Background

Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer.

Objective

The chest X-ray (CXR) is the most readily available and common imaging modality for the assessment of pneumonia. However, detecting pneumonia from chest radiography is a challenging task, even for experienced radiologists. An artificial intelligence (AI) model might help to diagnose pneumonia from CXR more quickly and accurately. We aim to develop an AI model for pneumonia from CXR images and to evaluate diagnostic performance with external dataset.

Cellular functions are carried out by a set of gene products. Mutation of a single gene is often sufficient to disrupt certain biological functions and promote tumorigenesis. Therefore, genes participating in the same function are less likely to mutate in the same sample. Such phenomenon is called “mutual exclusivity”. In this study, our algorithm (ORN) has utilized this property to identify gene-level mutations that affect similar biological functions. It also considers mutations’ impact on mRNA expression. Functional modules identified by ORN tends to be mutually exclusive while causing similar differential expression profiles. When we applied ORN to lower-grade glioma and liver cancer datasets, we have identified gene modules significantly related to patient survival. Furthermore, across different types of cancer, ORN has connected well-known cancer driver mutations with genes whose functions remain unclear. These connections, once validated, can generate novel hypothesis for biologist to further investigate cancer mechanism and develop targeted therapy.

Traditional Transcriptome-wide association studies (TWAS) tools make strong assumptions about the relationships among genetic variants, transcriptome, and phenotype that may be violated in practice, thereby substantially reducing the power. Here, we propose a Variance-Component TWAS method (VC-TWAS) that relaxes these assumptions and can be implemented with both individual-level and summary-level GWAS data, which is suitable for studying both continuous and dichotomous phenotypes. Our simulation studies showed that VC-TWAS achieved higher power compared to traditional TWAS methods based on a two-stage Burden test, when the underlying assumptions required by traditional TWAS tools were violated. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer’s dementia (AD). With individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. Interestingly, 5 of these genes were shown to possess significant pleiotropic effects on AD pathology phenotypes, revealing possible biological mechanisms. With summary-level data of a larger sample size, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. In conclusion, VC-TWAS provides an important analytic tool for identifying risk genes whose effects on phenotypes might be mediated through transcriptomes.

APOBEC3A (A3A) cytosine deaminase activity causes DNA damage and drives cancer mutagenesis. A genome-scale CRISPR/Cas9 screen identifies HMCES, a suicide enzyme that binds abasic sites, as a major suppressor of A3A-mediated damage in non-small cell lung cancer. Depletion of HMCES is synthetic lethal with A3A expression and p53 loss, suggesting its potential as a drug target.

Little is known about the host response to Venezuelan Equine Encephalitis Virus (VEEV) and the cellular factors this alphavirus hijacks to support effective replication or evade cellular immune responses. Monitoring dynamics of host and viral RNA (vRNA) during viral infection at a single-cell level can provide insight into the virus-host interplay at a high resolution. Here, a single-cell RNA sequencing technology that detects host and viral RNA was used to investigate the interactions between TC-83, the vaccine strain of VEEV, and the human host during the course of infection of U-87 MG cells (human astrocytoma). Virus abundance and host transcriptome were heterogeneous across cells from the same culture. Subsets of differentially expressed genes, positively or negatively correlating with vRNA abundance, were identified and subsequently in vitro validated as candidate proviral and antiviral factors, respectively, in TC-83 and/or virulent VEEV infections. In the first replication cycle, “superproducer” cells exhibited rapid increase in vRNA abundance and unique gene expression patterns. At later time points, cells with increased structural-to-nonstructural transcript ratio demonstrated upregulation of intracellular membrane trafficking genes. Lastly, comparing the VEEV dataset with published datasets on other RNA viruses revealed unique and overlapping responses across viral clades. Overall, this study improves the understanding of VEEV-host interactions, reveals candidate targets for antiviral approaches, and establishes a comparative single-cell approach to study the evolution of virus-host interactions.

Objective

Fecal microbiota transplantation (FMT) is a highly effective treatment for Clostridioides difficile infection (CDI). However, the fecal transplant’s causal components translating into clearance of the CDI are yet to be identified. The commensal bacteria Faecalibacterium prausnitzii may be of great interest in this context, since it is one of the most common species of the healthy gut microbiota and produces metabolites with anti-inflammatory properties. Although there is mounting evidence that F. prausnitzii is an important regulator of intestinal homeostasis, data about its role in CDI and FMT are relatively scarce.

Data sharing enhances understanding of research results beyond what is possible from any single study. Data pooling across multiple studies increases statistical power and allows exploration of between-study heterogeneity. But, considerations related to ethico-legal and intellectual/commercial value regularly prevent or impede physical data sharing. DataSHIELD is designed to circumvent this problem. However, despite the growing confidence users have been placing in DataSHIELD to perform privacy-protected analyses of data in cohort consortia, there are real challenges to federated analytics. They include considering the wide range of data formats, and big data sources used, for example, in ‘omics-based research. This article describes the development and implementation of the new “resources” architecture in DataSHIELD that overcomes this limitation. We illustrate its value with real world examples related to genomics and geographical data. We also demonstrate how genomic data sharing initiatives such as GA4GH and EGA can benefit directly from our development. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects.

The ribosome particles of a cell constantly synthesize fresh proteins. Some of these will be either integrated into organellar membranes or will be secreted from the cell. To this aim, they need to enter a channel protein complex termed translocon in the membrane of the endoplasmatic reticulum. The translocon needs to determine based on certain sequence features whether the peptide should be translocated into the endoplasmatic reticulum lumen or whether opening of a lateral gate will enable that its transmembrane domains slide laterally into the membrane. Whereas it is known that some peptides cannot be translocated without the involvement of accessory membrane proteins such as the Sec63 protein, the mechanistic details how Sec63 affects the conformation and/or dynamics of the translocon are so far not known. Here, we used molecular simulations, molecular docking and co-precipitation experiments to characterize protein contact residues and conformational shifts in the channel pore. Our simulations reveal that the plug moiety adopts different conformations in the channel in the presence, respectively the absence of Sec63, and we describe the influence of Sec63 on the conformation of the lateral gate. This study contributes to our understanding of the functionality of translocon and accessory proteins, and may eventually aid in characterizing and overcoming the effects of mutations related to protein translocation defects.

This study was the first to use shotgun metagenomics and in-depth bioanalysis of metagenomics data to understand the role of viral genomics, microorganism metagenomics and host transcriptomics in the severity of COVID-19. The study was performed using nasopharyngeal swabs collected from 104 patients with various severities of COVID-19 at the time of diagnosis. Neither the genetic characteristics of SARS-CoV-2, nor the detection of bacteria, viruses, fungi or parasites were associated with the severity of pulmonary disease. In contrast, host transcriptomic analyses showed that severe COVID-19 pneumonia is associated with overexpression of cytokine transcripts that activate the CXCR2 receptor pathway, whereas patients with benign disease present with a T helper “Th1-Th17” profile. The latter profile was associated with female gender and a lower mortality rate. These findings indicate that the most severe cases of COVID-19 are caused by excess neutrophil infiltration that enhances the inflammatory response and prolongs tissue damage. They suggest that CXCR2 antagonists, in particular IL-8 antagonists, could be promising candidates for the treatment of patients with severe COVID-19.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are progressive neurodegenerative diseases, whose most frequent genetic cause is hexanucleotide repeat (HR) expansion from normal 2 to 20 repeats to pathological hundreds of repeats within a non-coding region of the C9orf72 gene. Haplotype is a specific combination of multiple polymorphic sites along a chromosome that are inherited together in block. We characterized the single nucleotide polymorphism (SNP) signature and HR length of the major C9orf72 haplotypes in Caucasians to identify the allelic origin of C9orf72 transcripts per patient and determine the effects of expanded HR on C9orf72 gene expression and splicing. In C9orf72 ALS patients’ cells, the HR expanded allele, compared to non-R allele, was associated with decreased levels of downstream initiated transcript variant, increased levels of upstream initiated transcripts, accumulation of introns 1 and 2, and abnormal splicing at cryptic splice sites along intron 1. The C9orf72 haplotypes DNA signatures described here are valuable for studying C9-ALS/FTD pathogenesis and for developing allele-specific therapeutic strategies.

NOTCH1 deregulation can favor hematological malignancies. In addition to RBP-Jκ/NICD/MAML1, other regulators are required for the measured activation of NOTCH target genes. IKAROS is a known repressor of many NOTCH targets. Since it can also favor transcriptional activation and control gene expression levels, we questioned whether IKAROS could participate to the activation of specific NOTCH target genes. We are reporting that upon NOTCH induction, the absence of IKAROS impairs the measured activation of two groups of NOTCH target genes: (i) those overexpressed and characterized by an additive effect imposed by the absence of IKAROS and NOTCH induction; and (ii) those ‘desensitized’ and no more activated by NOTCH. At genes of both groups, IKAROS controls the timely recruitment of the chromatin remodelers CHD4 and BRG1. IKAROS then influences the activation of these genes through the organization of chromatin and poised transcription or through transcriptional elongation control. The importance of the IKAROS controlled and measured activation of genes is not limited to NOTCH signaling as it also characterizes Cdkn1a expression upon genotoxic stress. Thus, these results provide a new perspective on the importance of IKAROS for the adequate cellular response to stress, whether imposed by NOTCH or genotoxic insults.

As antimicrobial resistance is on the rise, the need for compounds with novel targets or mechanisms-of-action [MOA] are of the utmost importance from the standpoint of public health. A major bottleneck in drug discovery is the ability to rapidly screen candidate compounds for precise MOA activity as current approaches are expensive, time consuming, and are difficult to implement in high-throughput. To alleviate this bottleneck in drug discovery, we developed a human interpretable artificial intelligence classification framework that can be used to build highly accurate and flexible predictive models. In this study, we investigated antimicrobial MOA through the transcriptional responses of Escherichia coli challenged with 41 known antibiotic compounds, 9 crude extracts, and a recently discovered (circa 2019) compound, darobactin, with novel MOA activity. We implemented a highly stringent Leave Compound Out Cross-Validation procedure to stress-test our predictive models by simulating the scenario of observing novel compounds. Furthermore, we developed a versatile feature selection algorithm, Clairvoyance, that we apply to our hierarchical ensemble of classifiers framework to build high performance explainable machine-learning models. Although the methods in this study were developed and stress-tested to predict the primary MOA from transcriptomic responses in E. coli, we designed these methods for general application to any classification problem and open-sourced the implementations in our Soothsayer Python package. We further demonstrate the versatility of these methods by deploying them on recent Mycobacterium smegmatis metabolomic and E. coli transcriptomics datasets to predict MOA with high accuracy.

Chronic pain is a highly prevalent and debilitating condition, which is more common in women than in men. Sex differences in this condition may be a result of several factors, including differences between the sexes in genetic variation related to chronic pain and gene expression differences related to sex. To explore sex differences in chronic pain from a genetic perspective, we looked for genetic variants associated with chronic pain in men and women separately in a large general-population cohort, and compared the variants we identified between the sexes. We assessed the degree of overlap between genetic variants associated with chronic pain in each sex and those associated with a wide range of other traits, including major depression, body-mass index and suicidality. We also investigated gene expression patterns across a range of tissues for genes associated with chronic pain in each sex, in particular examining expression in neural and non-neural human and mouse tissues and assessing the degree of Dorsal Root Ganglion (DRG) enrichment, an important peripheral nervous system component involved in chronic pain. This work contributes to understanding of chronic pain as a trait and of sex differences in chronic pain at the levels of genetics and gene expression.

SARS-CoV-2 variants have appeared throughout the emergence of the current COVID-19 pandemic. In this study, characterization of a panel of infectious SARS-CoV-2 isolates in primary human respiratory cells allows the identification of important sequence features that contribute to virus replication.

During the breadmaking process, expanding gas bubbles cause the dough to increase volume. Gluten proteins act as an elastic scaffold in that process, allowing the wheat dough to grow more than other kinds of dough. Thus, explaining the unique viscoelastic properties of gluten at the molecular level may be of great interest to the baking industry. Assessing the structural properties of gluten is difficult because its proteins are disordered. We provide the first molecular dynamics model of gluten elasticity, that is able to distinguish gluten and proteins from different plants, like maize and rice. Our model shows the structural changes the gluten proteins undergo during their deformation, which mimics the mixing of dough during kneading. It also allows for a determination of the force required to extend gluten proteins, as during baking. The data confirms existing theories about gluten, but it also provides molecular-level information about the extraordinary elasticity of gluten.

We utilized the unprecedentedly large genotype and phenotype dataset in the UK Biobank to perform a genome-wide association study (GWAS) which accounts for the interplay between genotype and dietary intake. We examined the interaction effects of fish oil supplementation on levels of blood lipids (LDL-C, HDL-C, TAGs, and total cholesterol). Our findings were replicated in the Atherosclerosis Risk in Communities (ARIC) Study. We found that at the genetic variant rs112803755 (A>G), the minor allele (G) is associated with a decrease in TAGs among individuals with fish oil supplementation, but is associated with an increase in TAGs among those without supplementation. In other words, only individuals carrying the minor allele benefit from fish oil supplementation in reducing TAG levels. We further analyzed rs112803755 with functional genomics data from the Genotype-Tissue Expression (GTEx) project to identify potential target genes, and found a connexin coding gene which has been previously reported to respond to cellular omega-3 levels. This research suggests that inter-personal variation in TAG response to fish oil supplementation is in part explained by genotype, and that fish oil dose adjustment based on genotype should be investigated as a means to protect against cardiovascular disease risk.

We studied MCM9 variations in the genome of women with a Down syndrome child by stratifying the women based on MCM9 genotypes, meiotic error group, and their age of conception. We identified polymorphisms are associated with reduced recombination and nondisjunction of chromosome 21 at the meiosis I stage of oogenesis in a maternal age-independent manner. But these variants do not affect the position of chiasma formation. In Silico analyses revealed the presence of MCM9 variants that may cause alteration in protein function due to amino acid substitution. We also identified splice variants in MCM9. We hypothesize that the polymorphisms in MCM9 predispose women to experience reduced recombination on chromosome 21 in oocytes at meiosis I, which ultimately leads to the birth of a child with Down syndrome.

The membraneless organelles caused by liquid–liquid phase separation (LLPS) of IDPs/IDRs are involved in a wide range of biological functions such as RNA processing, ribosome biogenesis, and sequestration of mRNA, proteins, and compacted chromatin. In this study, we focus on the histone H2A-H2B binding partner, Swc5 and investigate the effects of the temperature, sequence length and number of charged residues on the LLPS behaviors. Moreover, we proposed three forms of swc conformation in ensemble according to the phase diagram. These characteristics of conformational changes (from LLPS to no LLPS, from condensed-phase to dilute-phase) are observed in the swc chains with different sequence length and charge pattern, which may be the general property for the IDP/IDR ensembles.

Rodent-borne Seoul orthohantavirus (SEOV) has provoked human cases from Asia to the Americas and Europe whereas most orthohantaviruses cause regional cases. Despite this, SEOV gets less attention than other orthohantaviruses. In Korea, 2019, a middle-aged man was initially diagnosed with Hantaan orthohantavirus (HTNV) and treated accordingly. However, next-generation sequencing identified SEOV, not HTNV, in the patient’s serum. Given its global outbreaks and public health threats, zoonotic SEOV should be diagnosed correctly on point of care to reduce unnecessary medical costs.

Aim

To identify differentially methylated positions (DMPs) and regions (DMRs) that predict response to Methotrexate (MTX) in early rheumatoid arthritis (RA) patients.

We randomly wired genes and cell behaviors into networks in EmbryoMaker. EmbryoMaker is a mathematical model of development that can simulate any gene network, all animal cell behaviors (division, adhesion, apoptosis, etc.), cell signaling, cell and tissues biophysics, and the regulation of those behaviors by gene products. Through EmbryoMaker we simulated how each random network regulates development and the resulting morphology (i.e. a specific distribution of cells and gene expression in 3D). This way we obtained a zoo of possible 3D morphologies. Real gene networks are not random, but a random search allows a relatively unbiased exploration of what is needed to develop complex robust morphologies. Compared to the networks leading to simple morphologies, the networks leading to complex morphologies have the following in common: 1) They are rarer; 2) They need to be finely tuned; 3) Mutations in them tend to decrease morphological complexity; 4) They are less robust to noise; and 5) They have more complex GPMs. These results imply that, when complexity evolves, it does so at a progressively decreasing rate over generations. This is because as morphological complexity increases, the likelihood of mutations increasing complexity decreases, morphologies become less robust to noise, and the GPM becomes more complex. We find some properties in common, but also some important differences, with non-developmental GPM models (e.g. RNA, protein and gene networks in single cells).

In this study we research the space of possible networks of gene and cell interactions in development using a computational model. This model includes gene networks, cell behaviors (such as cell division, apoptosis, adhesion, etc.) and realistic cell biophysics, which allows us to fully simulate animal development. We found that networks leading to complex morphologies have some things in common, which distinguish them from the networks leading to simple morphologies: 1) They are rarer; 2) They need to be finely tuned; 3) Mutations tend to decrease morphological complexity; 4) They are less robust; and 5) They have more complex genotype-phenotype maps. These results imply that, when complexity evolves, it does so at a progressively slower rate as mutations increasing complexity become rarer, morphologies become less robust to noise, and more complex genotype-phenotype maps evolve.

Despite major technological and computational advances, we remain unable to match human auditory perception using machines, or to restore normal-hearing communication for those with sensorineural hearing loss. An overarching reason for these limitations is that the neural correlates of auditory perception, particularly for complex everyday sounds, remain largely unknown. Although neural responses can be measured in humans noninvasively and compared with perception, these evoked responses lack the anatomical and physiological specificity required to reveal underlying neural mechanisms. Single-unit spike-train responses can be measured from preclinical animal models with well-specified pathology; however, the disparate response types (point-process versus continuous-valued signals) have limited application of the same advanced signal-processing analyses to single-unit and evoked responses required for direct comparison. Here, we fill this gap with a unifying framework for analyzing both spike-train and evoked neural responses using advanced spectral analyses of both the slow and rapid response components that are known to be perceptually relevant for speech and music, particularly in challenging listening environments. Numerous benefits of this framework are demonstrated here, which support its potential to advance the translation of spike-train data from animal models to improve clinical diagnostics and technological development for real-world listening.

Leishmaniasis is a disease caused by parasites of the Leishmania genus. Leishmania panamensis is a species that causes cutaneous leishmaniasis and potentially the more severe mucocutaneous leishmaniasis characterized by disfiguring lesions in nose and mouth. The C57BL/6 and BALB/c mouse strains are among the models commonly used for studying Leishmania infection and evaluating the effect of novel drug candidates. Previous research shows that the response of these two strains to Leishmania infection differs by factors that are related to both mouse strain genetic background and the infecting Leishmania species. In this study we characterized the differences in gene expression in macrophages of both mouse strains using next generation sequencing technologies. We identified differences in the expression of genes associated with the immune response that suggest that C57BL/6 macrophages display a classical pattern of activation associated with resolution of the disease, while BALB/c macrophages show an intermediate pattern of activation that favors parasite persistence and chronicity of the disease. The intermediate pattern observed in BALB/c macrophages resembles that observed in human infections with L. panamensis and supports the use of BALB/c as the preferred model for studying L. panamensis infection.

Dengue virus consists of four serotypes (DENV1-4) and there are different strains within a serotype. DENV can have smooth or bumpy surface morphologies at physiological body temperature of 37°C, depending on the strain. We have determined the cryoEM structures of a cross-reactive neutralizing human monoclonal antibody (HMAb) 1C19 in complex with strains of DENV1 and DENV2 that form either smooth or bumpy surface morphologies. We have mapped the epitope of HMAb 1C19 to E protein domain II and the epitope is likely partially hidden on the virus surface. We showed that the antibody has high affinity for binding to recombinant DENV1 E protein than to DENV2 E protein. This explains the strong neutralization activity for all DENV1 strains tested regardless of their morphologies at physiological temperature, whereas it can only neutralize DENV2 strain that exposes the epitope on the bumpy surface particles. These results suggest that high-affinity therapeutic antibodies could neutralize diverse dengue virus morphologies.

Campylobacter is the leading cause of bacterial gastroenteritis worldwide and an emerging and neglected pathogen in South America. In this study, we performed an in-depth analysis of the genome sequences of 69 C. jejuni and 12 C. coli clinical strains isolated from Chile, which account for over a third of the sequences from clinical strains available from South America. We identified a high genetic diversity among C. jejuni strains and the unexpected identification of clade 3 C. coli strains, which are infrequently isolated from humans in other regions of the world. Most strains harbored the virulence factors described for Campylobacter. While ~40% of strains harbored mutation in the gyrA gene described to confer fluoroquinolone resistance, very few strains encoded the determinants linked to macrolide resistance, currently used for the treatment of campylobacteriosis. Our study contributes to our knowledge of this important foodborne pathogen providing valuable data from South America.

Background

Considering the high invasiveness and mortality of glioma as well as the unclear key genes and signaling pathways involved in the development of gliomas, there is a strong need to find potential gene biomarkers and available drugs.

The genomes of plant pathogens often exhibit an architecture that facilitates high rates of dynamic rearrangements and genetic diversification in virulence associated regions. These regions, which tend to be gene sparse and repeat rich, are thought to serve as a cradle for adaptive evolution. Supernumerary chromosomes, i.e. chromosomes that are only present in some but not all individuals of a species, are a special type of structural variation that have been observed in plants, animals, and fungi. Here we identified and studied supernumerary mini-chromosomes in the blast fungus Magnaporthe oryzae, a pathogen that causes some of the most destructive plant diseases. We found that rice, foxtail millet and goosegrass isolates of this pathogen contain mini-chromosomes with distinct sequence composition. All mini-chromosomes are rich in repetitive genetic elements and have lower gene densities than core-chromosomes. Further, we identified virulence-related genes on the mini-chromosome of the rice isolate. We observed large-scale genomic rearrangements around these loci, indicative of a role of mini-chromosomes in facilitating genome dynamics. Taken together, our results indicate that mini-chromosomes contribute to genome rearrangements and possibly adaptive evolution of the blast fungus.

Background

Melioidosis is an endemic disease in southeast Asia and northern Australia caused by the saprophytic bacteria Burkholderia pseudomallei, with a high mortality rate. The clinical presentation is multifaceted, with symptoms ranging from acute septicemia to multiple chronic abscesses. Here, we report a chronic case of melioidosis in a patient who lived in Malaysia in the 70s and was suspected of contracting tuberculosis. Approximately 40 years later, in 2014, he was diagnosed with pauci-symptomatic melioidosis during a routine examination. Four strains were isolated from a single sample. They showed divergent morphotypes and divergent antibiotic susceptibility, with some strains showing resistance to trimethoprim-sulfamethoxazole and fluoroquinolones. In 2016, clinical samples were still positive for B. pseudomallei, and only one type of strain, showing atypical resistance to meropenem, was isolated.

B. pseudomallei is a Gram-negative bacterium that causes melioidosis, a tropical disease. The mortality rate is high, the treatment long and harsh, and the therapeutic arsenal is limited due to the natural resistance of the bacteria to antibiotics. Eleven percent of melioidosis cases are chronic. Here, we studied a chronic melioidosis case in a French male patient who lived in Malaysia in the 70s. B. pseudomallei was identified in 2014 and in a relapse in 2016. Analysis revealed several strains from the same clinical sample with different morphotypes and divergent antibiotic-resistance profiles. Two atypical multidrug resistance profiles were observed for two strains: one possessed multiple resistance to trimethoprim-sulfamethoxazole, fluoroquinolones, and chloramphenicol and the other multiple resistance to fluoroquinolones and meropenem. Trimethoprim-sulfamethoxazole or meropenem resistance have rarely been described in clinical cases and are probably underdiagnosed. Here, we show that trimethoprim-sulfamethoxazole resistance can be transient in clinical strains and easily lost in the laboratory after sub-culture during identification, resulting in an underestimation of trimethoprim-sulfamethoxazole resistance and therapeutic failure. We also identified a mutation in the AmrAB-OprA efflux pump regulator, leading to high level meropenem resistance, but this resistance is also transient.

Ion channels are proteins that control the flow of ions into the cell. The family of ion channels known as the pentameric ligand gated ion channels (pLGICS) open in response to the binding of a neurotransmitter, moving the channel from a resting state to an open state. The glycine receptor is a pLGIC whose structure has been resolved in different functional states. It is also known that the response of pLGICs can also be modified by different types of lipids found within the membrane itself but exactly how is unclear. Here, we used a realistic model of a neuronal membrane and performed molecular dynamics simulations to show various lipid-protein interactions that are dependent on the channel state. Our work also reveals previously unconsidered protein-lipid interactions at a key junction of the channel known to be critical for the transmission of the opening process. We also demonstrate that cholesterol interacts with the protein at a site already known to bind to another compound that modulates the channel, called ivermectin. The work should be useful for future design of allosteric modulators.

The hippocampus is a potential neuroimaging endophenotype for Alzheimer’s disease (AD). This study identifies genes whose expression in hippocampal tissue is associated with AD and establishes the pathways from hippocampal gene expression to hippocampal volume to AD. We demonstrate that 24 genes are associated with AD in hippocampal tissue, and these genes are enriched for AD-related biological processes of amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. We further show that hippocampal volume mediates the effects of the hippocampal gene expression of QPCTL and ERCC2 on AD. These findings improve our understanding of the genetic and neural mechanisms of AD.

Background

Vitamin D deficiency has been associated with type 1 diabetes in observational studies, but evidence from randomized controlled trials (RCTs) is lacking. The aim of this study was to test whether genetically decreased vitamin D levels are causally associated with type 1 diabetes using Mendelian randomization (MR).

Despoina Manousaki and co-workers investigate vitamin D levels and risk of type I diabetes.

The voltage-dependent anion channel (VDAC) is a membrane protein playing a pivotal role in the transport of ions or ATP across the mitochondrial outer membrane as well as in the induction of apoptosis. At high enough membrane potential, VDAC is known to transition from an open state to multiple closed states, reducing the flow of ions through the channel and blocking the passage of large metabolites. While the structure of the open state was resolved more than a decade ago, a molecular description of the gating mechanism of the channel is still missing. Here we show that the N-terminus of VDAC is an intrinsically disordered region and that such a property has a profound impact on its dynamics either as a free peptide or as part of the channel. By taking disordered properties of the N-terminus into account, we managed to generate long-lived closed conformations of the channel at experimental values of the membrane potential. Our results provide new insights into the molecular mechanism driving the gating of VDAC.

RiboViz is a package to extract biological insight from ribosome profiling data to help advance understanding of protein synthesis. At the heart of RiboViz is an analysis workflow, implemented in a Python script. To conform to best practices for scientific computing which recommend the use of build tools to automate workflows and to reuse code instead of rewriting it, the authors reimplemented this workflow within a workflow management system. To select a workflow management system, a rapid survey of available systems was undertaken, and candidates were shortlisted: Snakemake, cwltool, Toil, and Nextflow. Each candidate was evaluated by quickly prototyping a subset of the RiboViz workflow, and Nextflow was chosen. The selection process took 10 person-days, a small cost for the assurance that Nextflow satisfied the authors’ requirements. The use of prototyping can offer a low-cost way of making a more informed selection of software to use within projects, rather than relying solely upon reviews and recommendations by others.

Data analysis involves many steps, as data are wrangled, processed, and analysed using a succession of unrelated software packages. Running the right steps, in the right order, and putting the right outputs in the right places, is a major source of frustration. Workflow management systems require that each data analysis step be “wrapped” in a structured way, describing its inputs, parameters, and outputs. By writing these wrappers, the scientist can focus on the meaning of each step, and how they fit together, which is the interesting part. The system uses these wrappers to decide what steps to run and how to run these and takes charge of running the steps, including reporting on errors. This makes it much easier to repeatedly run the analysis and to run it transparently upon different computers. To select a workflow management system, we surveyed available tools and chose 4 in which we developed prototype implementations to evaluate their suitability for our project. We conclude that many similar multistep data analysis workflows can be rewritten in a workflow management system, and we advocate prototyping as a low-cost (both time and effort) way of making an informed selection of software for use within a research project.

The cell’s most important pieces of machinery are large complexes of proteins often along with nucleic acids. From the ribosome, to CRISPR-Cas9, to transcription factors and DNA-wrangling proteins like the SMC-Kleisins, these complexes allow organisms to replicate and enable cells to respond to environmental cues. Computer simulation is a key technology that can be used to connect physical theories with biological reality. Unfortunately, the time and length scales accessible to molecular simulation have not kept pace with our ambition to study the cell’s molecular factories. Many simulation codes also unfortunately remain effectively locked away from the user community who need to modify them as more of the underlying physics is learned. In this paper, we present OpenAWSEM and Open3SPN2, two new easy-to-use and easy to modify implementations of efficient and accurate coarse-grained protein and DNA simulation forcefields that can now be run hundreds of times faster than before, thereby making studies of large biomolecular machines more facile.

Objective

The current study investigated the mechanism underlying the therapeutic effects of berberine in the vasculature in hypertension.

Currently, RNA massive sequencing RNA-seq is the most extensively used technique for gene expression profiling in a single assay. The output of RNA-seq experiments contains millions of sequences, generated from cDNA libraries produced by the retro-transcription of RNA samples, that need to be processed by computational methods to be transformed into meaningful biological information. Thus, a number of bioinformatic workflows and pipelines have been proposed to produce different types of gene expression measurements, including in some cases, functional annotations to facilitate biological interpretation. While most pipelines focus exclusively on transcriptional data, the ultimate activity of the resulting gene product also depends critically on its integrity. Although traditional hybridization-based transcriptomics methodologies (microarrays) miss this information, RNA-seq data also contains information on variants present in the transcripts that can affect the function of the gene product, which is systematically ignored by current RNA-seq pipelines. MIGNON is the first workflow able to perform an integrative analysis of transcriptomic and genomic data in the proper functional context, provided by a mechanistic model of signaling pathway activity, making thus the most of the information contained in RNA-Seq data. MIGNON is easy to use and to deploy and may become a valuable asset in fields such as personalized medicine.

This work demonstrates a practical approach to the joint-analysis of multiple miRNA expression profiling datasets acquired with different measurement technologies. The use of different platforms in miRNA profiling can lead to major differences in results. In particular, some miRNA species are less amenable to detection and quantification by certain platforms or designs. Our approach, termed AQuN, combines quantile normalization with special attention to missing entities, to normalize miRNA expression across datasets, technologies, designs and platforms. As we show, our proposed approach uncovers patterns of interest that would not have emerged as statistically significant when analyzing the datasets individually or with other standard-practice normalization methods.

Amongst our findings, we noted a previously undocumented miRNA that is significantly over-expressed in samples from estrogen-receptor positive breast cancer patients as compared to samples from estrogen-receptor negative patients. We further investigated this miRNA, hsa-miR-193b-5p, and experimentally show, in cell lines, that its expression level impacts the viability of tumor cells. AQuN is available to the community in the form of Python and Matlab packages. The joint-processed data is also made available for further investigation.

Determining the entire DNA of environmental samples (sequencing) is a fundamental approach to gain deep insights into complex bacterial communities and their functions. However, this approach produces enormous amounts of data, which makes analysis time intense and complicated. We developed the Software “MUFFIN,” which effortlessly untangle the complex sequencing data to reconstruct individual bacterial species and determine their functions. Our software is performing multiple complicated steps in parallel, automatically allowing everyone with only basic informatics skills to analyze complex microbial communities.

For this, we combine two sequencing technologies: "long-sequences" (nanopore, better reconstruction) and "short-sequences" (Illumina, higher accuracy). After the reconstruction, we group the fragments that belong together ("binning") via multiple approaches and refinement steps while also utilizing the information from other bacterial communities ("differential binning"). This process creates hundreds of "bins" whereas each represents a different bacterial species with a unique function. We automatically determine their species, assess each genome’s completeness, and attribute their biological functions and activity ("transcriptomics and pathways"). Our Software is entirely freely available to everyone and runs on a good computer, compute cluster, or via cloud.

Understanding deregulations of metabolism based on isolated measures of gene expression or protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for a given data and reconstruction method, there are usually multiple solutions that satisfy the same constraints and with the same quality, but only one solution is returned by the commonly used reconstruction methods. Here, we formalize this problem and we propose and analyze different methods to obtain diverse samples of metabolic sub-networks. We evaluate them by performing an extensive comparison and we show how the different sets of optimal networks discovered by the different methods are biological meaningful by constructing ensembles of networks to improve the prediction of essential genes in Saccharomyces cerevisiae and to detect enriched metabolic pathways in four different human cancer cell lines.

Introduction

Gait deficits are debilitating in people with Parkinson’s disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical features and progression of the disease.

Hybridization is a fascinating evolutionary phenomenon that raises the question of how species maintain their integrity. Inter-species hybridization occurs between certain Schistosoma species that can cause important public health and veterinary issues. In particular hybrids between Schistosoma haematobium and S. bovis associated with humans and animals respectively are frequently identified in Africa. Recent genomic evidence indicates that some S. haematobium populations show signatures of genomic introgression from S. bovis. Here we conducted a comparative genomic study to assess the genomic diversity within S. haematobium and S. bovis species and genetic differentation at the genome scale between these two sister species over the African continent. We also investigated traces of possible ancient introgression from one species to another. We found that S. haematobium display low genetic diversity compared to S. bovis. We also found that most S. haematobium samples harbor signature of past introgression with S. bovis at some genomic positions. Our results strongly suggest that introgression occurred long time ago and that such introgression is unidirectional from S. bovis within S. haematobium. Such introgresssion event(s) result in diverging patterns of genomic introgression across S. haematobium lineages.

Mathematical models of population dynamics are an important tool to advance our understanding of ecosystems, which can be relevant for environmental, clinical, and industrial applications. One sanity check for such models is to virtually split a population into two with identical properties – allegorically, we paint half the individuals of the population in a different color. As we do not change the ecological situation, the outcome of the model should not change either; we call this feature clone consistency. We investigated the mathematical properties of clone-consistent models and deduced simple rules for their form. These rules allow to easily check clone consistency in existing models and ensure it when building new ones. The resulting framework can guide researchers in building models for specific ecosystems and in investigating general properties of ecosystems. We showcase our approach by applying it to models for bacterial communities causing urinary-tract infections. We further discuss that clone inconsistency, which occurs in several prominent models, reflects strong, often implicit, assumptions and it is important to check whether these are justified. Such assumptions may diminish the applicability of these models and the generality of results obtained with them.

Genome-wide association and replication study for skin pigmentation was performed in African Americans, and then the implication of the skin pigmentation genes in serum vitamin D variation was assessed. A variant, rs2675345, near SLC24A5 showed the strongest associations with skin pigmentation. A Genetic Score calculated using the top variants from 3 genomic regions, SLC24A5, SLC45A2 and OCA2, and West African genomic ancestry together account for a large proportion of skin pigmentation variation. The pattern of association between the Genetic Score and skin pigmentation was different between men and women suggesting an interaction between sex and genetic variation. The Genetic Score from the same 3 skin pigmentation gene variants was also associated with severe vitamin D deficiency, defined as serum 25(OH)D levels <12 ng/mL. However, skin pigmentation and vitamin D pathway gene variants account for a small but significant proportion of serum vitamin D variation. The results suggest that genomic variations strongly control skin pigmentation and also influence serum vitamin D levels.

The present work describes the transcriptome profile of salivary glands of O. moubata and assesses the gene expression dynamics along the trophogonic cycle using Illumina sequencing.

De novo transcriptome assembling resulted in 71,194 transcript clusters and 41,011 annotated transcripts, which represent 57.6% of the annotation success. Most salivary gene expression takes place during the first 7 days after feeding (6,287 upregulated transcripts), while a minority of genes (203 upregulated transcripts) are differentially expressed between 7 and 14 days after feeding. The functional protein groups more abundantly overrepresented after blood feeding were lipocalins, proteases (especially metalloproteases), protease inhibitors including the Kunitz/BPTI-family, proteins with phospholipase A2 activity, acid tail proteins, basic tail proteins, vitellogenins, the 7DB family and proteins involved in tick immunity and defence. The complexity and functional redundancy observed in the sialotranscriptome of O. moubata are comparable to those of the sialomes of other argasid and ixodid ticks.

This transcriptome provides a valuable reference database for ongoing proteomics studies of the salivary glands and saliva of O. moubata aimed at confirming and expanding previous data on the O. moubata sialoproteome.

The soft tick Ornithodoros moubata constitutes an important medical and veterinary problem in Africa because, in addition to being the vector of African swine fever, it transmits human relapsing fever (HRF), a hyper-endemic and lethal, but still neglected, tick-borne disease. Effective control of HRF requires eradicating its vector tick from domestic environments. As chemical acaricide application is ineffective against this tick, development of anti-tick vaccines seems the most promising method for tick control. Salivary proteins play essential functions for tick feeding and survival, which convert them in potential antigen targets for the development of tick vaccines. To know which these proteins are, we obtained the salivary transcriptome of O. moubata females and established, for the first time in a soft tick, the salivary gene transcription dynamics along its trophogonic cycle. Thereby, we have identified numerous genes encoding bioactive proteins essential for tick feeding. This information is essential to drive the selection of candidate antigens for anti-tick vaccine development and evaluate its protective potential in animal immunization trials. These data significantly enlarge the current repertory of known protein-coding sequences from soft tick salivary glands and establish a valuable reference database to improve our knowledge of the O. moubata salivary proteome.

Rising carbon dioxide levels driving climate change prompts us to embrace sustainable resources, such as autotrophic microbes that produce biomass or chemicals by consuming carbon dioxide. As genetic engineering of natural autotrophs is challenging, it is of interest to engineer autotrophy in more pliable microbial species, such as Escherichia coli. We contrasted 1,020 genomes of microbes carrying the most widespread carbon dioxide fixation pathway, the Calvin cycle, to genomes of closest relatives lacking this pathway. This comparison identified and ranked genetic adaptations that may enable Calvin cycle operation. This list of adaptations sheds light on the evolution of autotrophy and represents a recipe for an autotrophic microbe, which can aid genetic engineers in improving autotrophs or creating them from scratch.

It is widely assumed that only a large group of neurons can encode a stimulus or control behavior. This tenet of neuroscience has been challenged by experiments in which stimulating a single cortical neuron has had a measurable effect on an animal’s behavior. Recently, theoretical studies have explored how a single-neuron stimulation could be detected in a large recurrent network. However, these studies missed essential biological mechanisms of cortical networks and are unable to explain more recent experiments in the barrel cortex. Here, to describe the stimulated brain area, we propose and study a network model endowed with many important biological features of the barrel cortex. Importantly, we also investigate different readout mechanisms, i.e. ways in which the stimulation effects can propagate to other brain areas. We show that a readout network which tracks rapid variations in the local network activity is in agreement with the experiments. Our model demonstrates a possible mechanism for how the stimulation of a single neuron translates into a signal at the population level, which is taken as a proxy of the animal’s response. Our results illustrate the power of spiking neural networks to properly describe the effects of a single neuron’s activity.

A major goal of neuroscience is to understand how entire populations of neurons process sensory stimuli. This understanding is limited because current experimental techniques do not allow to record all relevant neurons of a sensory structure. A possible strategy to overcome this issue, is to use sequential recordings from cells recorded in multiple experiments to reconstruct how the entire population will respond. Yet this approach can not account for collective behaviour and synchronous activity within the population. Here we address this issue in the retina and propose a method to infer the activity of an entire population of neurons of the same type from sequential recordings. Our method assumes that we have access to many single cell recordings gathered from different experiments, and additionally to a few synchronous recordings of pairs of neurons of the same type. By combining copula distributions and maximum entropy modeling, we used these data to reconstruct the collective activity of a large population of neurons and to show how synchrony grows with the number of neurons.

S. epidermidis is a substantial component of the human skin microbiota, but also a major cause of nosocomial infections related to implanted medical devices. While phenotypic and genotypic determinants supporting invasion were identified, none appears to be necessary. By analysis of S. epidermidis from prosthetic joint infections, we here show that adaptive events are of importance during the transition from commensalism to infection. Adaptation to the infectious lifestyle is characterised by the development of intra-clonal heterogeneity, increased biofilm formation and enhanced growth in iron-free and nutrient-poor media, as well as reduced production of hemolysins. Importantly, during infection subpopulations emerge that carry mutations in a number of genes, most importantly the acetate kinase (ackA) and the β-subunit of the RNA polymerase (rpoB), have deleted larger chromosomal fragments (e.g. within the SCCmec element) or IS insertions in AgrC, a component of the master quorum sensing system in S. epidermidis. These results shed light on the multifactorial processes of infection adaptation and demonstrate how S. epidermidis is able to flexibly repurpose and edit factors important for colonization to facilitate survival under hostile infection conditions. While mobilome associated factors are important for S. epidermidis invasive potential, the species possesses a multi-layered and complex ability for adaptation to hostile environments, supporting the progression to chronic implant-associated infections.

Adaptation to changing environments is vital to bacteria and is enabled by sophisticated signal transduction systems. While signal transduction by two-component systems is well studied, the signal transduction of membrane-integrated one-component systems, where one protein performs both sensing and response regulation, are insufficiently understood. How can a membrane-integrated protein bind to specific sites on the genome to regulate transcription? Here, we study the kinetics of this process, which involves both protein diffusion within the membrane and conformational fluctuations of the genomic DNA. A well-suited model system for this question is CadC, the signaling protein of the E. coli Cad system involved in pH stress response. Fluorescently labeled CadC forms visible spots in single cells upon stable DNA-binding, marking the end of the protein-DNA search process. Moreover, the start of the search is triggered by a medium shift exposing cells to pH stress. We probe the underlying mechanism by varying the number and position of DNA target sites. We combine these experiments with mathematical analysis and kinetic Monte Carlo simulations of lattice models for the search process. Our results suggest that CadC diffusion in the membrane is pivotal for this search, while the DNA target site is just mobile enough to reach the membrane.

We designed a developmental architecture inspired by the cortico-basal system for early vocal learning. Our neural system explores, evaluates and strengthens the motor primitives that match the best the sound repertoire created also dynamically. After a babbling process in which the network tests and aligns pronounced sound and motor vocal tracks, it is used for listening to novel voices, solving the correspondence problem.

Cells adapt to diverse environments by regulating gene expression. Genome-wide measurements of gene expression levels have exponentially increased in recent years, but successful integration and analysis of these datasets are limited. Recently, we showed that independent component analysis (ICA), a signal deconvolution algorithm, can separate a large bacterial gene expression dataset into groups of co-regulated genes. This previous study focused on data generated by a standardized pipeline and did not address whether ICA extracts the same quantitative co-expression signals across expression profiling platforms. In this study, we show that ICA finds similar co-regulation patterns underlying multiple gene expression datasets and can be used as a tool to integrate and interpret diverse datasets. Using a dataset containing over 3,000 expression profiles, we predicted three new regulons and characterized their activities. Since large, standardized expression datasets only exist for a few bacterial strains, these results broaden the possible applications of this tool to better understand transcriptional regulation across a wide range of microbes.

Background

FLT3 mutation is present in 25–30% of all acute myeloid leukemias (AML), and it is associated with adverse outcome. FLT3 inhibitors have shown improved survival results in AML both as upfront treatment and in relapsed/refractory disease. Curiously, a variable proportion of wild-type FLT3 patients also responded to these drugs.

The visual cortex has a prominent role in the processing of visual information by the brain. Previous work has segmented the mouse visual cortex into different areas based on the organization of retinotopic maps. Here, we collect responses of the visual cortex to various types of stimuli and ask if we could discover unique clusters from this dataset using machine learning methods. The retinotopy based area borders are used as ground truth to compare the performance of our clustering algorithms. We show our results on two datasets, one collected by the authors using wide-field imaging and another a publicly available dataset collected using two-photon imaging. The proposed supervised approach is able to predict the area labels accurately using neuronal responses to various visual stimuli. Following up on these results using visual stimuli, we hypothesized that each area of the mouse brain has unique responses that can be used to classify the area independently of stimuli. Experiments using resting state responses, without any overt stimulus, confirm this hypothesis. Such activity-based segmentation of the mouse visual cortex suggests that large-scale imaging combined with a machine learning algorithm may enable new insights into the functional organization of the visual cortex in mice and other species.

Autism spectrum disorder is a neurodevelopmental disorder with complex genetic etiology. Mutational variant studies link damaging and typically rare variants in protein-coding genes with disease outcomes, while genome-wide association studies identify genetic variations that are common in the human population associated with autism risk. Interestingly, studies targeting common and rare variants have implicated distinct risk pathways for autism. Here, we introduce a novel statistical framework for risk gene mapping, i.e., TITANS, to better analyze common genetic variants from parent-proband trios. TITANS integrates transmission disequilibrium information with tissue-specific regulatory annotations of multiple linked variants to infer risk genes. We pinpoint a novel autism gene POU3F2, which encodes a key transcription factor regulating multiple autism risk genes implicated in exome sequencing studies. Our findings provide a novel connection between rare and common variants, whereby autism genes affected by rare mutations are regulated by an unlinked transcription factor affected by common genetic variations.

Molecular dating using phylogenetics allows us to estimate the date of an infection from time-stamped within-host sequences alone. There are large datasets of HIV-1 sequences, but genome and gene analyses are not often performed in parallel and rarely with the possibility to compare results against a known narrow window of infection. We showed that all but the longest genes are near-clonal in acute infection, with little information for dating purposes. For infections with single founders, we estimated the eclipse phase—the time between HIV-1 exposure and the first positive diagnostic test—to last between one and two weeks using env, gag, pol and near full-length genomes. This approach could be used to narrow the date of suspected infection in ongoing clinical trials for the prevention of HIV-1 infection.

Mutations can now be introduced into genes that code for RNAs and proteins almost at will. Yet why one mutation compromises the function of the molecule while another does not often remains unclear. This is, in part, because our main signposts for understanding the molecular basis of differential mutational effects—crystal structures–provide only very partial guidance. RNAs in particular are highly dynamic and defects can arise during multiple conformations that the RNA assumes during normal function. A single crystal structure might represent but a snapshot of all the important conformations in a large ensemble. Here we show that deep mutational scanning–a technique to generate a large library of mutated versions of the original molecule–can simultaneously capture the impact of mutations that exert their effect in one of several conformations the molecule assumes during its life cycle. Deep mutational scanning can therefore be used, in principle, to study conformations that are transient or hard to observe and to better understand why and when mutations are harmful.

In order to optimize their behavior, animals must continuously represent the uncertainty associated with their beliefs. Understanding the neural code for this uncertainty is a pressing and critical issue in neuroscience. Following a long tradition, some studies have investigated this code by measuring how average statistics of neural responses (like the tuning curves) correlate with uncertainty as stimulus characteristics are varied. We show that this approach can be very misleading. An alternative consists in decoding the neuronal responses to recover the posterior distribution over the encoded sensory variables and using the variance of this distribution as the measure of uncertainty. We demonstrate that this decoding approach can indeed avoid the pitfalls of the traditional approach, while leading to more accurate estimates of uncertainty.

We can better understand human health and disease by studying the state of cells and how environmental dysregulation affects cell state. Cellular assays, when collected across time, can show us how genes in cells respond to stimuli. These time-series assays provide an opportunity to identify causal relationships among thousands of genes without performing hundreds of thousands of experiments. However, inferring causal relationships from these time-series data needs to be fast, robust, and accurate. We present a method, BETS, that infers causal gene networks from gene expression time series. BETS runs quickly because it is parallelized, allowing even data sets with thousands of genes to be analyzed. We demonstrate the performance of BETS compared to 22 other state-of-the-art inference methods on benchmark data. We then use BETS to build causal networks from gene expression responses to the widely-prescribed drug dexamethasone. We replicate the estimated causal relationships using gene expression data from the Genotype-Tissue Expression (GTEx) project and from additional experiments with dexamethasone. We release our software so that BETS can be used to accurately and effectively infer causal relationships from gene expression time-series assays.

Many pathogens vary their surface antigenic profile in order to establish and maintain infections in the face of host immune responses. Although antigenic variation has been extensively documented in extracellular pathogens and is crucial in these cases to pathogen evasion of host antibody responses, there is scant understanding of the role that antigenic variation plays in immunity to intracellular pathogens, where cell-mediated immune responses are key to infection control and where a low frequency of switching from one predominant surface antigen to a new variant would be expected to have little impact on immune recognition. Herein we use comparative genome analysis to reveal the details of and mechanisms behind how the intracellular parasite Trypanosoma cruzi, agent of human Chagas disease, maintains a vast and varying array of antigens that are the targets of host immune responses. The process of diversification is so efficient that two isolates share not a single identical gene among the thousands of antigenic variants in their genomes, thus making the likelihood of generating protective vaccines, low. This genome flexibility also ensnares genes whose products are not targets of immune responses, thus further driving the isolate-specific biological diversity that characterizes this species.

EBV immortalization recapitulates aspects of natural B-cell development from germinal center reaction to memory B-cell differentiation. Here, we provide an integrated multi-omic approach to investigating the EBV-induced changes in B-cell transcriptome using RNA-seq, epigenome by ATAC-seq, and metabolome by mass spectrometry of polar metabolites. We identify purine metabolism as a major pathway that is significantly altered by EBV in each data set. We focus on EBNA1 and find that it binds directly to enhancer elements in a subset of genes, including key regulators of purine metabolism such as adenosine deaminase (ADA) and adenylate kinase 4 (AK4). EBV immortalized B-cells are strongly dependent on ADA for proliferation and inhibitors of ADA prevent B-cell immortalization by EBV. These findings identify purine metabolism as a major pathway targeted by EBV during primary infection and suggest that EBNA1 functions as a pioneering transcription factor that reprograms key regulatory genes in purine metabolism pathway.

Epidemic response in the form of social contact reduction, such as has been utilized during the ongoing COVID-19 pandemic, presents inherent tradeoffs between the economic costs of reducing social contacts and the public health costs of neglecting to do so. Such tradeoffs introduce an interactive, iterative mechanism that adds complexity to an infectious disease system. Consequently, infectious disease modeling typically has not included dynamic behavior change that must address such a tradeoff. Here, we develop a theoretical strategic model that introduces lost or gained economic and public health utility through the adjustment of social contact rates with delayed information. This model produces an equilibrium, a point of indifference where the tradeoff is neutral, and at which a disease will be endemic for a long period of time. Under small perturbations, this model exhibits complex dynamic regimes, including oscillatory behavior, runaway exponential growth, and eradication. These dynamics suggest that for epidemic responses that rely on social contact reduction, secondary waves and surges with accompanied business and school re-closures and shutdowns may be expected, and that accurate projection under such circumstances is unlikely.

The level of cholesterol circulating in the blood in low-density lipoproteins (LDL) is an important determinant of overall risk for cardiovascular diseases, including heart attack and stroke. This level is regulated by the removal of LDL from circulation into liver cells. While many molecules involved in LDL uptake have been characterized, we hypothesized that other currently unrecognized genetic interactions are also involved in this process. We therefore applied CRISPR-mediated genome editing to systematically test the contribution of every gene in the human genome to the uptake of LDL by a liver-derived cell line. We synthesized a secondary CRISPR library targeting the top candidate genes from this initial genome-wide screen to confirm their role in LDL uptake and to test their influence on other cellular functions. Our findings confirm the role of genes previously known to participate in LDL uptake and also provide novel insight into the overall regulation of this process.

Genome-scale reconstructions of metabolism appeared shortly after the first genome sequences became available. Constraint-based models are widely used to compute steady state properties of such reconstructions, but the attainment of dynamic models has remained elusive. We thus developed the MASSpy software package, a framework that enables the construction, simulation, and visualization of dynamic metabolic models. MASSpy is based on the mass action kinetics for each elementary step in an enzymatic reaction mechanism. MASSpy seamlessly unites existing software packages within its framework to provide the user with various modeling tools in one package. MASSpy integrates community standards to facilitate the exchange of models, giving modelers the freedom to use the software for different aspects of their own modeling workflows. Furthermore, MASSpy contains methods for generating and simulating ensembles of models, and for explicitly accounting for biological uncertainty. MASSpy has already demonstrated success in a classroom setting. We anticipate that the suite of modeling tools incorporated into MASSpy will enhance the ability of the modeling community to construct and interrogate complex dynamic models of metabolism.

Ae. albopictus is an important vector of arboviruses such as dengue and Zika viruses. Studies on virus-host interaction at gene expression and molecular level are crucial especially in devising methods to inhibit virus replication in Aedes mosquitoes. Previous reports have shown that, besides protein-coding genes, noncoding RNAs such as lncRNAs are also involved in virus-host interaction. In this study, we report a comprehensive catalog of novel lncRNA transcripts in the genome of Ae. albopictus. We also show that the expression of lncRNAs was altered upon infection with dengue and Zika. Additionally, depletion of certain lncRNAs resulted in increased replication of dengue and Zika; hence, suggesting potential association of lncRNAs in virus infection. Results of this study provide a new avenue to the investigation of mosquito-virus interactions, especially in the aspect of noncoding genes.

In plants, conserved microRNAs tend to be encoded by gene families with multiple members. This Primer explores the implications of two recent studies that interrogated the functions of the five-member MIR172 family in Arabidopsis, revealing complexities and intricacies of the gene regulatory networks underlying floral transition.

Motivation

A comprehensive characterization of the humoral response towards a specific antigen requires quantification of the B-cell receptor repertoire by next-generation sequencing (BCR-Seq), as well as the analysis of serum antibodies against this antigen, using proteomics. The proteomic analysis is challenging since it necessitates the mapping of antigen-specific peptides to individual B-cell clones.

The proteomics of serum antibodies (Ig-Seq) is based on a two-arm foundation: the generation of BCR-Seq (using next-generation sequencing of B cells) and high-resolution mass-spectrometry (LC-MS/MS) of serum antibodies. The BCR-Seq data are used for the interpretation of mass-spectra and result in the identification of antibody derived peptides thus, the identification of V genes of the serum antibodies. To provide accessibility to non-expert users we introduce the PASA (Proteomic Analysis of Serum Antibodies) web server that provides a robust computational platform for the analysis and integration of data obtained from proteomics of serum antibodies and enables its integration with BCR-Seq data as obtained from ASAP (that provides bioinformatics support for BCR-Seq analysis and also provides as output a reference database that can be used in proteomic analysis of serum antibodies. ASAP is available at http://asap.tau.ac.il. PASA has a user-friendly interface, while it keeps the ability of expert users to tune their computations towards their special needs. We provide a GALLERY section that demonstrates selected outputs of PASA and example data to allow a quick ramp up for new users. Our graphical outputs promote quantitative analyses and visualization of the immune repertoires.

Barley powdery mildew caused by the fungus Blumeria graminis forma specialis hordei (Bgh) can result in annual yield losses of 15% of this cereal crop. Bgh promotes virulence in plants through the secretion of diverse effector molecules, small proteins of which a subset enters into and modifies the immune status and physiology of the host leaf. In response, the host has evolved a multitude of disease resistance genes. The Mildew locus a (Mla) resistance gene stands out because diversification in the host population has generated numerous Mla variants encoding multi-domain receptors, each of which can directly recognize an isolate-specific Bgh effector, designated as avirulence (AVR_A) effectors. Recognition of AVR_A effectors by MLA triggers plant immune responses, a phenomenon known as isolate-specific resistance, which invariably results in localized host cell death. Here, we identify the powdery mildew effector AVR_A6 and validate its specific interaction with its matching receptor MLA6. Furthermore, through the use of hybrid receptors constructed from MLA1 and MLA6 as well as MLA10 and MLA22 receptors, we provide insights into the specific domains and amino acid residues generally important for AVR_A recognition by MLA receptors. We find that sequence variation in the leucine-rich repeats (LRRs) of multi-allelic MLA receptors determines specific recognition of AVR_A effectors. These effectors are sequence-unrelated, but our analysis indicates that they may be structurally related. This data may assist in the future generation of synthetic immune receptors with pre-defined recognition specificities.

Using methods to visualize mRNA in living zebrafish larvae, this study identifies candidate motifs 3’ UTRs that were sufficient to localize mRNA to myelin sheaths; one such motif is highly enriched in the myelin transcriptome, suggesting that this sequence is a global regulator of mRNA localization during developmental myelination.

Background

To investigate the prognostic value of gene variants and copy number variations (CNVs) in patients with newly diagnosed multiple myeloma (NDMM), an integrative genomic analysis was performed.

Patients affected by osteoporosis frequently present with decreased BMD and increased fracture risk. Genes are known to control the onset and progression of bone diseases such as osteoporosis. Therefore, we aimed to identify osteoporosis-related genes using BMD measures obtained from a large pool of mutant mice genetically modified for deletion of individual genes (knockout mice). In a collaborative endeavor involving several research sites world-wide, we generated and phenotyped 3,823 knockout mice and identified 200 genes which regulated BMD. Of the 200 BMD genes, 141 genes were previously not known to affect BMD. The discovery and study of novel BMD genes will help to better understand the causes and therapeutic options for patients with low BMD. In the long run, this will improve the clinical management of osteoporosis.

This Essay discusses how complex traits may evolve under selection to maintain trait values in the face of changing environments; this scenario has implications for interpreting genetic differences between groups and signals of adaptation.

Background

Observational studies have reported either null or weak protective associations for coffee consumption and risk of breast cancer.

Recombinant proteins have dominated recent blockbuster therapeutic drugs, accounting for 11 of the top 15 drugs by sales. Chinese hamster ovary (CHO) cells are the most widely used expression system for biomanufacturing of many of these biotherapies. Thus, there is increasing interest in leveraging omics technologies for CHO cell line development, bioprocess optimization, and biotherapeutic product quality assessment. However, CHO cells have been largely ignored in the development of publicly available tools to facilitate comprehensive omics data analysis and management, despite being a ubiquitous research tool and biotherapeutic production host. To address the gap, we have recently developed a web-based tool, named “CHOmics”, for the integrative and interactive data analysis and visualization specifically designed for CHO. This novel tool provides all-in-one solutions from raw data processing to pathway and gene analysis and offers considerable flexibility to customize analysis and visualization. It further allows for other omics data inputs and thereby enables multi-omics comparison. The open-source tool is freely available at http://www.chomics.org.

When part of the heat shock protein DnaJB1 is fused to the catalytic subunit of cAMP-dependent protein kinase (PKA), this fusion protein drives the development of fibrolamellar hepatocellular carcinoma. This study of the asymmetric structure and dynamics of the PKA RIIβ holoenzyme with the oncogenic DnaJB1-PKAc fusion protein reveals disrupted PKA allostery.

The accurate description of the structure of a protein is pivotal to fully understand its biological function. A large fraction of eukaryotic proteins is intrinsically disordered or consists of multiple folded domains connected by disordered regions. The structure of these proteins is highly flexible and can only be described by large ensembles of conformations. The characterization of these ensembles can be achieved by integrating in silico molecular modelling and simulations with experiments. Here, we present DEER-PREdict, an open-source software program to conveniently and efficiently calculate the observables of two biophysical methods, namely double electron-electron resonance (DEER) and paramagnetic relaxation enhancement (PRE) nuclear magnetic resonance. Both techniques provide distance information for highly dynamic systems and involve labelling proteins at one or more sites with flexible probe molecules. The DEER-PREdict package combines previously developed and validated methods for placing multiple conformations of a nitroxide molecule at the protein sites with the rapid calculation of DEER and PRE observables from large ensembles of protein structures. Through examples, we illustrate the use of DEER-PREdict as a tool for interpreting experimental results, validating molecular models of flexible proteins as well as designing experiments.

Inferring familial relationships has a wide range of applications. Family-based genome-wide association studies and population-based GWAS both require genetic relationships. Inferring relationship is essential for unknown familial structures and can be used to correct pedigree information due to false paternity, sample switches, or unregistered adoption. Current approaches for inferring relationships are not scalable for large cohorts comprising millions of individuals. Here, we present a fast and flexible method, called RAFFI, using Identical by Descent (IBD) segments. IBD segments are uninterrupted DNA segments inherited from a common ancestor. Relationships are usually inferred by computing the kinship coefficients and the genome-wide probability of zero IBD sharing among all pairs of individuals. In the first step, we search for IBD segments using RaPID which avoids a pairwise comparison of all individuals in a haplotype panel. In the second step, we compute the kinship coefficients to infer the relationships. To make our method robust against genotyping and phasing error, the thresholds of kinship coefficients for different degrees of relatedness are adjusted. As a result, the lower detection power of IBD segments due to phasing errors or misspecification of the genotyping error rate will not comprise the inference of relationships.

The rate at which plants grow is a major functional trait in plant ecology. However, little is known about its genetic variation in natural populations. Here, we investigate genetic and environmental factors shaping variation in the growth rate of Arabidopsis thaliana and ask whether genetic variation in plant growth contributes to adaptation to local environmental conditions. We grew plants under two light regimes that mimic latitudinal differences in the intensity of natural light radiation, and measured plant diameter as it grew over time. When the light supply was decreased, plant diameter grew more slowly but reached a markedly larger final size, confirming that plants can adjust their growth to prevailing light conditions. Yet, we also detected significant levels of genetic variation both in growth rate and in how the growth dynamics is adjusted to the light conditions. We show that this variation is encoded by many loci of small effect that are hard to locate in the genome but overall significantly enriched among genes associated with growth and stress reactions. We further observe that Spanish genotypes tended to reach, on average, a significantly larger rosette size than Northern European genotypes. Tests of adaptive divergence indicate that these differences may reflect adaptation to local environmental conditions.

Objective

Cardiac sarcoidosis is difficult to diagnose, often requiring expensive and inconvenient advanced imaging techniques. Circulating exosomes contain genetic material, such as microRNA (miRNA), that are derived from diseased tissues and may serve as potential disease-specific biomarkers. We thus sought to determine whether circulating exosome-derived miRNA expression patterns would distinguish cardiac sarcoidosis (CS) from acute myocardial infarction (AMI).

The coronavirus that causes COVID-19 controls the production of key viral proteins through a process known as programmed ribosomal frameshifting. Frameshifting is triggered by a particular structure in the viral RNA, a pseudoknot, which is a promising drug target. Here we model the structure of this pseudoknot through atomistic molecular dynamics simulations. Surprisingly, we find that the pseudoknot can take on distinct fold topologies, two of which involve unusual threading of a single strand of RNA through helical junctions, something not seen before in frameshifting pseudoknots. All of the folds are generally consistent with previous experimental studies of the closely-related SARS coronavirus pseudoknot. These results should assist in the analysis and interpretation of future experimental studies of the pseudoknot structure, and support structure-based drug-discovery efforts.

A combination of biomolecular simulations and experiments reveals that the disc-like oligomeric lipodystrophy protein seipin interacts with and traps triglycerides in the endoplasmic reticulum, thus facilitating the formation and growth of lipid droplets.

One often compares biological vision to a camera-like system where an image would be processed according to a sequence of successive transformations. In particular, this “feedforward” view is prevalent in models of visual processing such as deep learning. However, neuroscientists have long stressed that more complex information flow is necessary to reach natural vision efficiency. In particular, recurrent and feedback connections in the visual cortex allow to integrate contextual information in our representation of visual stimuli. These modulations have been observed both at the low-level of neural activity and at the higher level of perception. In this study, we present an architecture that describes biological vision at both levels of analysis. It suggests that the brain uses feedforward and feedback connections to compare the sensory stimulus with its own internal representation. In contrast to classical deep learning approaches, we show that our model learns interpretable features. Moreover, we demonstrate that feedback signals modulate neural activity to promote good continuity of contours. Finally, the same model can disambiguate images corrupted by noise. To the best of our knowledge, this is the first time that the same model describes the effect of recurrent and feedback modulations at both neural and representational levels.

Rhesus macaque CMV (RhCMV) is an important model for human CMV (HCMV) pathogenesis and vaccine development. Therefore, it is important to understand the similarities and differences in infectivity and interaction of these viruses with their host species. In contrast to the strict species-specificity of HCMV, RhCMV is able to cross species barriers to replicate in human cells. We know from past work that a component of this broader host range is RhCMV’s ability to counteract both the rhesus and human versions of a key antiviral factor. Here we delve further into the mechanisms by which RhCMV can adapt to counteract human cellular defenses. We find that RhCMV appears to be poised to undergo a specific genomic rearrangement that facilitates increased replication efficiency in human cells. Besides providing insights into CMV species-specificity and host barriers to cross-species transmission, this work also provides more generalized clues about viral adaptative mechanisms.

The rust fungus Puccinia coronata f. sp. avenae (Pca), which causes crown rust disease, decimates oat (Avena sativa) production in many countries of the world. While the use of genetic resistance in crop breeding programs is the most sustainable disease management strategy to control plant disease, the release of oat varieties that display genetic resistance to Pca infection is hindered by rapid evolution of this pathogen. This study aims to determine demography and determinants of adaptive evolution in Pca to minimize the risk of disease outbreaks and enhance resistance gene stewardship. We recently published two high quality genome references of P. coronata f. sp. avenae. Here, we used these resources to direct a population genomics-based study of two temporally distant sets of pathogen collections to study genotypic changes that may explain the most recent oat crown rust epidemics across the continental US. We found that the population of Pca in 1990 is significantly different to that collected in 2015 at both genotypic and phenotypic levels. Our findings are consistent with the role of sexual and asexual reproduction in the Pca population diversity. Importantly, our work identifies genomic regions and genes that may be involved in local host adaptation which in the future may assist in the development of molecular markers and diagnosis of virulence.

Streptococcus pyogenes is a human specific pathogen causing both mild and invasive infections. It employs sophisticated mechanisms to evade and circumvent parts of the host’s immune defenses, in part via its major surface associated virulence factor, the family of M proteins. Of these, the M1 protein is the most prevalent serotype. The M1 protein creates a dense coat-like structure with multiple host proteins on the bacterial surface to disguise itself from opsonizing antibodies. It specifically interacts in a non-immune way with human immunoglobulin G (IgG) Fc-domains to disarm their receptor binding site. The molecular level details of this interaction have not been characterized. Here, we describe these interactions from minimally perturbed samples of human plasma adsorbed onto living bacteria using an integrative structural biology approach including cross-linking mass spectrometry, molecular modeling, and molecular dynamics simulations. We identify two distinct M1-peptides that bind IgGs and reveal the stability of these interactions. We show that both peptides block the Fc-receptor binding sites through capturing IgGs via their Fc-domains. These results highlight the importance of describing novel pathogen-derived peptides mediating host immune evasion as potential vaccine targets in future studies.

Kinship coefficients and F_ST, which measure relatedness and population structure, respectively, are important quantities needed to accurately perform various analyses on genetic data, including genome-wide association studies and heritability estimation. However, existing estimators require restrictive assumptions of independence that are not met by real human and other datasets. In this work we find that existing estimators can be severely biased under reasonable scenarios, first by theoretically determining their properties, and then using an admixture simulation to illustrate our findings. In particular, we find that existing F_ST estimators are downwardly biased, and that existing kinship matrix estimators have related biases that are on average downward and of similar magnitude but vary for every pair of individuals. These insights led us to a new estimation framework for kinship and F_ST that is practically unbiased for any population structure, as demonstrated by theory and simulations. Our new approaches—available as open-source R packages—are easy to use and are more widely applicable than existing approaches, and they are likely to improve downstream analyses that require accurate kinship and F_ST estimates.

Schistosomiasis is a neglected tropical disease caused by helminths within the genus Schistosoma, which affects over 200 million people globally. Here, we demonstrate the ability to obtain whole genome sequences from single S. japonicum miracidia that have been archived for years, and the promise of how these data may be used to answer major questions to understand and eventually help control schistosomiasis. To demonstrate the approach, we generated a moderate-sized dataset of 22 individual S. japonicum genomes and use these data to infer fine-scale patterns of relatedness among parasites from human hosts from two villages in Sichuan, China. We highlight how this type of genomic information may be useful for understanding the genetic variation among individual parasites with high precision, which could ultimately provide high resolution for understanding transmission pathways and inform control efforts with larger sample sizes. We also show that it is possible to use less data per individual than we obtained here, and still maintain high levels of accuracy; this would enable greater sample sizes by lowering individual sample cost. Collectively our results demonstrate that sampling genomes of individual schistosome parasites is now feasible on a population-level scale, opening new avenues for studying relatedness, tracing parasite spread, and studying parasite biology.

Background

It has been reported that genetic factors are associated with risk factors and onset of lifestyle-related diseases, but this finding is still the subject of much debate.

Background

Osteoporosis is an asymptomatic disease of high prevalence and incidence, leading to bone fractures burdened by high mortality and disability, mainly when several subsequent fractures occur. A fragility fracture predictive model, Artificial Intelligence-based, to identify dual X-ray absorptiometry (DXA) variables able to characterise those patients who are prone to further fractures called Bone Strain Index, was evaluated in this study.

Single cell sequencing produces gene expression data which has many individual observations, but each individual cell is noisy and sparsely sampled. Existing denoising and imputation methods are of varying complexity, and it is difficult to determine if an output is optimally denoised. There are often no general criteria by which to choose model hyperparameters and users may need to supply unknown parameters such as noise distributions. Neighbor graphs are common in single cell expression analysis pipelines and are frequently used in denoising applications. Data is averaged within a connected neighborhood of the k-nearest neighbors for each observation to reduce noise. Denoising without a clear objective criteria can result in data with too much averaging and where biological information is lost. Many existing methods lack such an objective criteria and tend to overly smooth data. We have developed and evaluated an objective function that can be reliably minimized for optimally denoising single cell data on a graph, DEWÄKSS. The DEWÄKSS objective function is derived from self supervised learning principles and requires optimization over only a few parameters. DEWÄKSS performs robustly compared to more complex algorithms and state of the art graph denoising methods.

In the biomedical field there is ever increasing availability of data from sequencing-based methods, such as whole-genome or whole-exome sequencing, that can greatly help elucidate the role of rare genetic variants in the aetiology of common diseases. However, state-of-the-art rare variant association methods are vastly underpowered in small to medium-sized studies and therefore novel methodologies are needed to leverage these datasets while integrating information from different genomic sources. To this end we present NETPAGE, a gene-based association testing method that models how the effect of rare deleterious variants spreads over gene interaction networks. NETPAGE is robust and flexible, and can be applied to different diseases, sample sizes, and types of traits (binary or continuous). We demonstrate the successful application of NETPAGE to two Alzheimer’s disease cohorts of different sizes and sequencing methods, identifying connected hub genes and communities underlying biological processes and pathways involved in Alzheimer’s and other neurodegenerative diseases, and that could be considered as potential drug targets.

Background

An effective syphilis vaccine should elicit antibodies to Treponema pallidum subsp. pallidum (T. p. pallidum) surface antigens to induce pathogen clearance through opsonophagocytosis. Although the combination of bioinformatics, structural, and functional analyses of T. p. pallidum genes to identify putative outer membrane proteins (OMPs) resulted in a list of potential vaccine candidates, still very little is known about whether and how transcription of these genes is regulated during infection. This knowledge gap is a limitation to vaccine design, as immunity generated to an antigen that can be down-regulated or even silenced at the transcriptional level without affecting virulence would not induce clearance of the pathogen, hence allowing disease progression.

Syphilis is still an endemic disease in many low- and middle-income countries and has been resurgent in high-income nations for almost two decades now. In endemic areas, syphilis still causes significant morbidity and mortality in patients, particularly when its causative agent, the bacterium Treponema pallidum subsp. pallidum is transmitted to the fetus during pregnancy. Although there are significant ongoing efforts to identify an effective syphilis vaccine to bring into clinical trials within the decade in the U.S., such efforts are partially hindered by the lack of knowledge on transcriptional regulation of many genes encoding vaccine candidates. Here, we start addressing this knowledge gap for the putative outer membrane protein (OMP) and vaccine candidates TprL, encoded by the tp1031 gene. As we previously reported for other putative OMP-encoding genes of the syphilis agent, tprL transcription level appears to be affected by the length of a homopolymeric sequence of guanosines (Gs) located within the gene promoter. This is a mechanism known as phase variation and often involved in altering the surface antigenic profile of a bacterial pathogen to facilitate immune evasion and/or adaptation to the host milieu.

A curative therapy for HIV-1 infection will at least require the eradication of a small pool of CD4+ helper T cells in which the virus can persist in an inactive, latent state, even after years of successful antiretroviral therapy. It has been assumed that activation of these viral reservoir T cells will also reactivate the latent virus, which is a prerequisite for the destruction of these cells. Remarkably, this is not always the case and following application of even the most potent stimuli that activate normal T cells through their T cell receptor, a large portion of the latent virus pool remains in a dormant state. Herein we demonstrate that a large part of latent HIV-1 infection events reside in T cells that have been rendered activation inert. We provide a systemwide, biomolecular description of the changes that render latently HIV-1 infected T cells activation inert and using this description, devise pharmacologic interference strategies that render initially activation inert T cells responsive to stimulation. This in turn allows for efficient triggering of HIV-1 reactivation in a large part of the otherwise unresponsive latently HIV-1 infected T cell reservoir.

Background

Personalized medicine requires finding relationships between variables that influence a patient’s phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis. Thus, our aim was to develop a method to find the relationships between microbiome and host transcriptome data and the relevant clinical variables in a complex disease, such as Crohn’s disease.

Dravet syndrome (DS) is a neurological disorder affecting approximately 1:15,700 Americans that causes generalized epilepsy and associated complications. While most patients have a mutation in the SCN1A gene that encodes the Na_v1.1 voltage-gated sodium channel, about 20% do not have a mutation identified by exome or targeted sequencing. Recently, we identified variants in intron 20, a noncoding region of SCN1A, in some DS patients. We hypothesized that these variants alter SCN1A transcript processing, decrease Na_v1.1 function, and lead to DS pathophysiology via inclusion of exon 20N, a “poison” exon that leads to a premature stop codon. In this study, we generated a knock-in mouse model, Scn1a+/KI, of one of these variants, which resides in a genomic region that is extremely conserved across vertebrate species. We found that Scn1a+/KI mice have reduced levels of Scn1a transcript and Na_v1.1 protein and develop DS-related phenotypes. We find that transcripts from brains of Scn1a+/KI mice show elevated rates of Scn1a exon 20N inclusion. We also explored the relationship between exon 20N inclusion and Scn1a expression during development, and found that, during brain development when Scn1a expression is low, exon 20N inclusion is high; postnatally, as Scn1a expression increases, there is a corresponding decrease in exon 20N usage. Expression of another voltage-gated sodium channel transcript, Scn8a (Na_v1.6), was similarly regulated. Together, these data demonstrate that poison exon inclusion is a conserved mechanism to control sodium channel expression in the brain, and that an intronic mutation that disrupts the normal developmental regulation of poison exon inclusion leads to reduced Na_v1.1 and generalized epilepsy.

Objective

One of the greatest challenges in clinical trial design is dealing with the subjectivity and variability introduced by human raters when measuring clinical end-points. We hypothesized that robotic measures that capture the kinematics of human movements collected longitudinally in patients after stroke would bear a significant relationship to the ordinal clinical scales and potentially lead to the development of more sensitive motor biomarkers that could improve the efficiency and cost of clinical trials.

Nucleosome positioning plays a key role in the genome’s function by regulating the accessibility of protein binding sites as well as higher-order chromatin organization. Though significant progress has been made towards studying the role of DNA sequence in positioning the nucleosomes, our understanding on the impact of transcription lags behind. Our study uses kinetic simulations to explore the role of DNA sequence specificity, transcription factor binding, enzyme remodeling, and Pol II elongation in positioning nucleosomes. It suggests that the differences in nucleosome density profiles observed at various transcription levels in yeast and mouse embryonic stem cells can be understood from a tug-of-war between two types of remodeling enzymes.

Background

Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models.

Generative neural networks have been effectively used in many different domains in the last decade, including machine dreamt photo-realistic imagery. In our work, we apply a similar concept to genetic data to automatically learn its structure and, for the first time, produce high quality realistic genomes. These novel genomes are distinct from the original ones used for training the generative networks. We show that artificial genomes, as we name them, retain many complex characteristics of real genomes and the heterogeneous relationships between individuals. They can be used in intricate analyses such as imputation of missing data as we demonstrated. We believe they have a high potential to become alternatives for many genome databases which are not publicly available or require long application procedures or collaborations and remove an important accessibility barrier in genomic research in particular for underrepresented populations.

The endo-lysosomal cation channel TPC2 is implicated in numerous human diseases ranging from metabolic disease, Parkinson’s disease, cancer and pigmentation defects, to infectious diseases such as Ebola, Covid-19, and bacterial infections. Here, we present a functional analysis of several polymorphisms occurring in the human TPC2 protein in distinct populations. By evaluating several human genome databases, we identified a large number of single nucleotide polymorphisms in TPC2. We electrophysiologically characterized the most common polymorphisms by applying the endo-lysosomal patch-clamp technique. We thereby identified several novel gain-of-function variants and found the TPC2 variation L564P to be a prerequisite for the previously described M484L gain-of-function effect associated with blond hair. In addition, publicly available genome-wide association study databases were assessed, and linked traits of the investigated TPC2 polymorphisms interrogated. Considering that different human populations have a different likelihood of carrying the identified gain-of-function variations, these findings appear highly relevant for further assessment of TPC2 as a pharmacological drug target.

Applied to single-cell RNA-seq data, RNA velocity analysis provides a way to estimate the rate of change of the gene expression levels in individual cells. This, in turn, enables estimation of what the gene expression profile of each cell will look like a short time into the future and lets researchers infer likely developmental relationships among different types of cells in a tissue. An important first step in this type of analysis consists of estimating the expression levels of unspliced pre-mRNA as well as mature mRNA in each cell. Several methods are available for this purpose, and in this study we perform a comparison of these tools and highlight respective advantages and disadvantages. We envision that the results will be informative for researchers performing RNA velocity analysis, as well as to guide future developments in the evaluated and newly proposed quantification methods.

Studies in model animals have shown that asymmetrically localized maternal transcripts play important roles in axial patterning and cell fate specification in early embryos. However, comprehensive analyses of the maternal transcriptomes with spatial information are limited to a handful of organisms. Here, we use a PCR-based single-cell RNA sequencing approach on separated blastomeres from the cleavage stage embryos of amphioxus, an early-branching chordate. We generate a spatial transcriptomic map and demonstrate the mosaic property of the early amphioxus embryo, contrasting to the general notion that its embryogenesis is highly regulative. Our cross-species experiments further provide evidence to support that some of the identified factors play functions in early axial patterning. Our results provide a global transcriptomic blueprint that can serve as a rich platform for guiding future characterization of molecular players in early amphioxus development and for comparative studies on testing conservation and divergence of developmental programs during chordate evolution.

Motivation and background

The patient’s immune system plays an important role in cancer pathogenesis, prognosis and susceptibility to treatment. Recent work introduced an immune related breast cancer. This subtyping is based on the expression profiles of the tumor samples. Specifically, one study showed that analyzing 658 genes can lead to a signature for subtyping tumors. Furthermore, this classification is independent of other known molecular and clinical breast cancer subtyping. Finally, that study shows that the suggested subtyping has significant prognostic implications.

The polymerase chain reaction is an extensively applied technique that helps to identify bacterial and viral pathogens, germline and somatic mutations. And primers are one of the most important parts of each PCR-assay, defining its sensitivity and specificity. Many tools have been developed to design primers automatically, however, none of them let someone develop multiplex PCR assays requiring to design from two to thousands of primers with one-two commands. Here we present such a tool called NGS-PrimerPlex that was initially developed to create new targeted NGS-panels. This approach has become widespread since it allows targeting only the regions of interest of a genome reducing the analysis cost. We showed that NGS-PrimerPlex can be applied for the multiplex pathogen detection and sequencing exons of the LRRK2 gene in Parkinson’s disease patients. The primers designed were validated on 354 DNA samples for which the LRRK2 coding sequences were successfully studied on the MiniSeq Illumina platform.

This study shows that decRiPPter, an innovative algorithmic approach using pan-genomics and machine learning, can discover novel types of ribosomally synthesized peptide (RIPP) natural products, including a new class of lanthipeptides.

Herpes simplex virus type 1 (HSV-1) infects and destroys the outer layer of skin cells, producing lesions known as cold sores. Although these lesions heal, the virus persists in the host for the lifetime and can reactivate to cause new lesions. This is possible because the virus enters the axons of neurones in the skin and moves to their cell bodies located in spinal or cranial nerve bundles called ganglia, where the virus becomes dormant (latent). The most abundant viral RNAs expressed during this state are the latency associated transcripts (LATs), which have been considered a hallmark of HSV-1 latency. Here, we studied HSV-1 infection and spread in human skin. Unexpectedly, we found that the LAT locus is necessary for lesion formation in skin. HSV-1 viruses that were genetically mutated to delete the start of the locus could not spread in skin, whereas viruses with many other genetic mutations had this capacity. Our results suggest that an antiviral drug that inhibits transcripts from this region of the viral genome could block viral spread in skin, or a vaccine could possibly be produced by genetically modifying the virus at the LAT locus and by doing so, limit the virus’ ability become latent in neurones.

Parasitic filarial nematodes affect over 100 million individuals in tropical areas where they cause debilitating diseases. Almost all species infecting humans live in mutualism with Wolbachia endosymbionts. The Wolbachia wBm of Brugia malayi, a nematode responsible for human elephantiasis, show a typical localisation in the hypodermis of the adult worms, and in the female germline in order to be vertically transmitted to the next generation through the eggs. Analyses of Wolbachia-depleted filarial nematodes have revealed that these α-proteobacteria contribute to the long lifespan of the nematode host. In addition, they clearly support the maintenance of the female germline, indicating a developmental symbiosis between B. malayi and its associated wBm endosymbiont. To understand the mutualism between wBm and the B. malayi host, we performed transcriptomics analyses at an unprecedented tissue-specific resolution, during oogenesis and in the soma, and we characterized the adaptation of wBm to specific cellular environments such as the hypodermis. Additional comparative germline transcriptomic analyses between B. malayi and C. elegans as well as a B. malayi orthology map revealed peculiarities in the filarial oogenesis, likely resulting from evolutionary innovations specific to the parasitic lifestyle.

Background

Despite the benefits offered by an abundance of health applications promoted on app marketplaces (e.g., Google Play Store), the wide adoption of mobile health and e-health apps is yet to come.

Neurons, even “at rest”, require a constant supply of energy to function. They cannot sustain high-frequency activity over long periods because of regulatory mechanisms, such as adaptation or sodium channels inactivation, and metabolic limitations. These limitations are especially severe in many neuronal disorders, where energy can become insufficient and make the neuronal response change drastically, leading to increased burstiness, network oscillations, or seizures. Capturing such behaviors and impact of energy constraints on them is an essential prerequisite to study disorders such as Parkinson’s disease and epilepsy. However, energy and spiking constraints are not present in any of the standard neuronal models used in computational neuroscience. Here we introduce models that provide a simple and scalable way to account for these features, enabling large-scale theoretical and computational studies of neurological disorders and activity patterns that could not be captured by previously used models. These models provide a way to study energy-associated disorders over the whole time-course of disease progression, and they enable a better assessment of energy-centered approaches to improve therapies.

Background

As a mosquito-borne infectious disease, dengue fever (DF) has spread through tropical and subtropical regions worldwide in recent decades. Dengue forecasting is essential for enhancing the effectiveness of preventive measures. Current studies have been primarily conducted at national, sub-national, and city levels, while an intra-urban dengue forecasting at a fine spatial resolution still remains a challenging feat. As viruses spread rapidly because of a highly dynamic population flow, integrating spatial interactions of human movements between regions would be potentially beneficial for intra-urban dengue forecasting.

Dengue fever, a mosquito-borne infectious disease, has become a serious public health problem in many tropical and subtropical regions worldwide, such as Southeast Asian countries and the Guangdong Province in China. In the absence of an effective vaccine at present, disease surveillance and mosquito control remain the primary means of controlling the spread of the disease. At an intra-urban setting, it is important to predict the spatial distribution of future patients, which can help government agencies to establish precise and targeted prevention measures beforehand. Considering the fast virus spread within a city because of a highly dynamic population flow, we proposed a novel approach to enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions. First, using a graph-embedding model called Node2Vec, the embeddings of the regions were learned from their population interaction network so that strongly interacted regions would have more similar embeddings. Secondly, serving as interaction features, the embeddings were combined with the commonly used features as inputs of the forecasting models. The experimental results indicated that the performance of the models can be improved by incorporating the interaction features, confirming the effectiveness of our proposed strategy in enhancing fine-grained intra-urban dengue forecasting.

Tuberculosis is one of the deadliest infectious diseases, being responsible for more than one million deaths per year. The causing bacteria are becoming increasingly drug-resistant, which is hampering disease control. At the same time, an unprecedented amount of bacterial whole-genome sequencing is increasingly informing clinical practice. In order to detect the genetic alterations responsible for developing drug resistance and predict resistance status from genomic data, bio-statistical methods and machine learning models have been employed. However, due to strongly overlapping drug resistance phenotypes and genotypes in multidrug-resistant datasets, the results of these correlation-based approaches frequently also contain mutations related to resistance against other drugs. In the past, this issue has often been ignored or partially resolved by either restricting the input data or in post-analysis screening—with both strategies relying on prior information. Here we present a heuristic algorithm for finding resistance-associated variants and demonstrate that it is considerably more robust towards co-occurrent resistance compared to traditional techniques. The software is available at https://github.com/julibeg/HHS.

Purpose

Accurate and timely diagnosis of breast carcinoma is very crucial because of its high incidence and high morbidity. Screening can improve overall prognosis by detecting the disease early. Biopsy remains as the gold standard for pathological confirmation of malignancy and tumour grading. The development of diagnostic imaging techniques as an alternative for the rapid and accurate characterization of breast masses is necessitated. Quantitative ultrasound (QUS) spectroscopy is a modality well suited for this purpose. This study was carried out to evaluate different texture analysis methods applied on QUS spectral parametric images for the characterization of breast lesions.

Backgrounds

Sevoflurane is a most frequently used volatile anesthetics, but its molecular mechanisms of action remain unclear. We hypothesized that specific genes play regulatory roles in brain exposed to sevoflurane. Thus, we aimed to evaluate the effects of sevoflurane inhalation and identify potential regulatory genes by RNA-seq analysis.

This study describes a novel single-cell RNA-seq technology called SCAN-seq which can capture the full-length transcripts in single cells based on the third-generation Nanopore sequencing platform, and demonstrates its performance on mouse preimplantation embryos.

An intracellular signaling pathway is often represented by a set of nonlinear ordinary differential equations, which translate our current knowledge about the signaling pathway into a testable mathematical model. However, predictions from such models are often subject to high uncertainty since many signaling pathways are only partially known beforehand. In this study, we propose a systematic approach to develop a hybrid model to improve model accuracy by combining machine learning and the first-principle modeling. Specifically, model correction terms are learned from discrepancy between model predictions and measurements, and these terms are added to the first-principle model to enhance the prediction accuracy. Once these correction terms are learned from the data, an artificial neural network (ANN) model is developed to find an empirical relation between the model and the correction terms so that the developed ANN can be used to posses improved predictive capabilities even in new operating conditions (i.e., generalizability). The final hybrid model is then constructed by coupling the first-principle model with the developed ANN.

Brucella spp. are facultative intracellular pathogens that can persistently colonize host cells and cause the zoonosis- brucellosis. The WHO recommended a treatment for brucellosis that involves a combination of doxycycline, rifampicin, or streptomycin. Rifampicin-resistance related genes were screened by transcriptomic analysis and gene recombination method at low rifampicin concentrations and the major rifampicin- resistance pathways in Brucella spp were predicted. The results showed that the VirB operon, β-resistance genes, ABC transporters, quorum-sensing genes, DNA repair- and replication -related genes were associated with rifampicin resistance when no variations of the in rpoB were detected. Among the VirB operons, VirB7-11 may play a central role in rifampicin resistance.

Gene-based association tests are statistical methods used in genome-wide association studies (GWAS) to identify genes that affect heritable traits. Gene-based tests are formed by aggregating genotypes across multiple genetic variants for each gene, often including only variants that are likely to affect gene function or regulation. In this work, we present a unified framework to integrate heterogeneous classes of functional variants in gene-based association analysis. This approach enables us to simultaneously assess multiple distinct biological mechanisms underlying GWAS association signals, and to construct powerful omnibus tests by aggregating across functional classes for each gene. We evaluated the performance of gene-based association test methods and strategies to identify causal genes by conducting extensive simulation studies, and by analyzing 128 human traits from the UK Biobank and comparing our results against lists of high-confidence putative causal genes. Our analysis suggests that incorporating heterogeneous functional variants in gene-based association tests increases power to detect gene-based association and helps identify causal genes.

Currently, a novel coronavirus called SARS-COV-2 is spreading across many parts of the world. Unfortunately, there is still a lack of effective drugs against the virus. Additionally, it usually takes much longer time to develop a new drug using traditional methods. Thus, it is now better to rely on some alternative methods to develop drugs that can treat such a disease effectively. In this paper, we have proposed a deep learning and molecular dynamics simulation based hybrid drug screening procedure for identifying potential drug candidates targeting RdRp from 1906 market available drugs. Our screening have successfully identified a FDA-approved drug called Pralatrexate that strongly inhibits the replication of 2019-nCoV in vitro with EC50 values of 0.008μM. This work demonstrated the feasibility of accurate virtual drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Conjunctival melanoma is an extremely rare form of cancer of the eye that arises from melanocytes–the cells producing the protective pigment melanin–in the outmost layer of the eye: the conjunctiva. This tissue, similarly to the skin, can also be exposed to UV light radiation from the sun. We investigated the genetic background of this rare form of cancer in samples from fourteen patients, by global DNA and RNA sequencing. Our results showed that conjunctival melanoma is genetically very similar to cutaneous melanoma. More precisely, in tumor DNA we detected signs of damage caused by UV light, as well as mutations in the genes BRAF, NF1 and NRAS/HRAS, previously described to be involved in cutaneous melanoma. Analysis of tumor gene expression also revealed similarities between these two types of cancer, some of which could be used as prognostic factors or as indicators of a patients’ response to therapy. In addition, we identified frequent somatic mutations in ACSS3, a gene not yet associated with either conjunctival or cutaneous melanoma, which represents a potential key player in oncogenesis of conjunctival melanoma.

Background

Proteins with low complexity regions (LCRs) have atypical sequence and structural features. Their amino acid composition varies from the expected, determined proteome-wise, and they do not follow the rules of structural folding that prevail in globular regions. One way to characterize these regions is by assessing the repeatability of a sequence, that is, calculating the local propensity of a region to be part of a repeat.

Background

A zoonotic association has been suggested for several PCR ribotypes (RTs) of Clostridioides difficile. In central parts of Sweden, RT046 was found dominant in neonatal pigs at the same time as a RT046 hospital C. difficile infection (CDI) outbreak occurred in the southern parts of the country.

Rift valley fever (RVF) is an emerging zoonotic disease and it is caused by RVFV. This virus is commonly transmitted in endemic areas between wild ruminants and mosquitoes, mainly by mosquitoes of Culex and Aedes genus. Starting from the year 2000, several outbreaks have been reported outside Sub Saharan Africa, in countries facing the Mediterranean Sea (Egypt), or Yemen and Saudi Arabia. Available vaccines for ruminants present limited efficacy or residual pathogenic effects. Consequently, new strategies are urgently required to limit the expansion of this zoonotic virus. The main objective of this work is to investigate transcriptional alterations of Cx. pipiens to RVFV focusing mainly on genes implicated in conventional innate immunity pathways, RNAi mechanisms and the apoptotic process in order to evaluate the involvement of these genes in viral infection. The immune altered genes here described could be potential targets to control RVFV infection in mosquitoes. Some of the genes related to the immune defense response were previously described in others mosquito-arbovirus models, as well as in Drosophila and human. To our knowledge, this study highlights for the first time the Cx. pipiens-RVFV interactions in terms of defense infection-response and provides information for developing in the future new approaches to prevent and control the expansion of the virus.

Confluent motion of cells in tissues plays a crucial role in wound healing, tissue repair, development, morphogenesis and in numerous pathological processes such as tumor invasion and metastatic cancer. For such complex processes, controlled experiments help clarifying the roles of chemical, mechanical and biological cues. Among them, spreading of cellular tissues on an empty space and antagonistic migration assays between cancerous and normal cells are quite revealing. The interfaces between confluent cellular aggregates uncover properties thereof when a combination of modeling, numerical simulation and data analysis is used. Here we have modified an active vertex model with a dynamics that includes inertia, friction and active forces that tend to align cells based on interaction with its immediate neighborhood. Selecting appropriately junction tensions among cells and using the SAMoS software, we have succeed in simulating assays of cellular tissue spreading on an empty space and the invasion of healthy tissue by cancerous one. We have introduced topological data analysis to characterize, track and compare in an automatic manner the interfaces of the tissue both in numerical simulations and from experimental data of normal and Ras modified precancerous Human Embryonic Kidney cells. We find good agreement when normal cells are solidlike and modified cells are liquidlike according to their shape parameters. In addition, cell variability means that a fraction of randomly distributed cells favor mixing, the others segregation. Topological data analysis techniques are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant in ascertaining how the biophysical features of materials may affect tissue and organ regeneration.

Coronaviral genomes code for several proteins, with the large 1a/1ab being expressed directly from genomic (g)RNA. For the expression of other viral proteins, a set of subgenomic mRNAs is produced during replication. It includes mRNAs for structural (S-E-M-N) and accessory proteins. While the function of structural proteins is well described, the function of the latter ones is under debate. Some of them are required for replication, while others are dispensable in vitro but essential in vivo. Initially, 10 open reading frames (ORFs) were annotated in the SARS-CoV-2 genome, amongst which ORF10 is the most peculiar, as it does not share sequence homology with any known protein. Shortly after the genomic sequences became available, speculations on this protein's role in pathogenesis and innate immunity breaching started. Here, we identified two patients infected with SARS-CoV-2 variants with the ORF10 gene prematurely terminated. The disease was not attenuated, and the transmissibility was maintained. The in vitro study showed that the ORF10 is also not essential for replication. Consequently, ORF10 should not be treated as the protein-coding gene, and the genome annotations should be amended.

Chromosomal inversions are a type of structural variant commonly found to underlie local adaptation and speciation. However, in most cases we do not know what genes or genetic variants inside an inversion are important, and how any such variants regulate the associated adaptive traits. Fine-mapping genetic variants using forward genetics inside inversions is difficult because recombination between heterokayotypes are suppressed. In this study, we used gene expression as a tool to study the genetics of a large chromosomal inversion found in highland maize populations in Mexico called Inv4m. Gene expression analyses in 9 tissues across two environmental conditions identified a large number of seemingly independent developmental and physiological processes regulated by Inv4m, suggesting that this locus contributes to local adaptation of maize to highland environments in multiple ways.

Background

Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue.

Dengue virus infects millions of people annually and is associated with a high mortality rate. When outbreaks occur, hospitals are often overcrowded with patients. Thus, novel approaches are required to accelerate patient triage for hospitalization, or further intensive care. Machine learning is being widely applied for resolving various problems, including medical diagnosis and outcome prediction. Here, we combined information from patients, including age, sex, and rapid virus test results, to develop a machine learning model for severe outcome prediction. The developed machine learning model displayed good performance for severe dengue disease prediction, and all information required for the model could be easily obtained. We also found that patients who were over 60 years old, who had detectable nonstructural protein-1 from dengue virus, or who had both detectable anti-dengue IgM and IgG antibodies in their sera, had a greater risk of progression to severe dengue. This study established a new approach to predict dengue disease outcomes by applying machine learning and defined the risk factors for severity prediction.

We propose a new approach PLACO that uses aggregate-level genotype-phenotype association statistics—commonly referred to as GWAS summary statistics—to identify genetic variants that influence risk of two traits or diseases. It allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. We demonstrate that PLACO can achieve major power gain over alternative methods that are typically used. We applied PLACO to Type 2 Diabetes and Prostate Cancer summary data from two large case-control studies. Many previous studies have reported an inverse association of these two chronic diseases suggesting shared risk factors; however, shared genetic mechanisms underlying this association is poorly understood. PLACO identified a number of novel shared genetic regions that are not detected by individual trait analysis. Many of the loci implicated by PLACO increase risk for one disease while decreasing risk for the other. PLACO can similarly be used on other traits to shed light on shared genetic risk factors.