Gene–Environment Interactions at Nucleotide Resolution

Interactions among genes and the environment are a common source of phenotypic variation. To characterize the interplay between genetics and the environment at single nucleotide resolution, we quantified the genetic and environmental interactions of four quantitative trait nucleotides (QTN) that govern yeast sporulation efficiency. We first constructed a panel of strains that together carry all 32 possible combinations of the 4 QTN genotypes in 2 distinct genetic backgrounds. We then measured the sporulation efficiencies of these 32 strains across 8 controlled environments. This dataset shows that variation in sporulation efficiency is shaped largely by genetic and environmental interactions. We find clear examples of QTN:environment, QTN: background, and environment:background interactions. However, we find no QTN:QTN interactions that occur consistently across the entire dataset. Instead, interactions between QTN only occur under specific combinations of environment and genetic background. Thus, what might appear to be a QTN:QTN interaction in one background and environment becomes a more complex QTN:QTN:environment:background interaction when we consider the entire dataset as a whole. As a result, the phenotypic impact of a set of QTN alleles cannot be predicted from genotype alone. Our results instead demonstrate that the effects of QTN and their interactions are inextricably linked both to genetic background and to environmental variation

ATTR amyloidosis during the COVID-19 pandemic: insights from a global medical roundtable

BACKGROUND: The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing the ongoing coronavirus disease 2019 (COVID-19) pandemic has raised serious concern for patients with chronic disease. A correlation has been identified between the severity of COVID-19 and a patient's preexisting comorbidities. Although COVID-19 primarily involves the respiratory system, dysfunction in multiple organ systems is common, particularly in the cardiovascular, gastrointestinal, immune, renal, and nervous systems. Patients with amyloid transthyretin (ATTR) amyloidosis represent a population particularly vulnerable to COVID-19 morbidity due to the multisystem nature of ATTR amyloidosis. MAIN BODY: ATTR amyloidosis is a clinically heterogeneous progressive disease, resulting from the accumulation of amyloid fibrils in various organs and tissues. Amyloid deposition causes multisystem clinical manifestations, including cardiomyopathy and polyneuropathy, along with gastrointestinal symptoms and renal dysfunction. Given the potential for exacerbation of organ dysfunction, physicians note possible unique challenges in the management of patients with ATTR amyloidosis who develop multiorgan complications from COVID-19. While the interplay between COVID-19 and ATTR amyloidosis is still being evaluated, physicians should consider that the heightened susceptibility of patients with ATTR amyloidosis to multiorgan complications might increase their risk for poor outcomes with COVID-19. CONCLUSION: Patients with ATTR amyloidosis are suspected to have a higher risk of morbidity and mortality due to age and underlying ATTR amyloidosis-related organ dysfunction. While further research is needed to characterize this risk and management implications, ATTR amyloidosis patients might require specialized management if they develop COVID-19. The risks of delaying diagnosis or interrupting treatment for patients with ATTR amyloidosis should be balanced with the risk of exposure in the health care setting. Both physicians and patients must adapt to a new construct for care during and possibly after the pandemic to ensure optimal health for patients with ATTR amyloidosis, minimizing treatment interruptions

Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices

BACKGROUND: Protein-protein interactions are critical for cellular functions. Recently developed computational approaches for predicting protein-protein interactions utilize co-evolutionary information of the interacting partners, e.g., correlations between distance matrices, where each matrix stores the pairwise distances between a protein and its orthologs from a group of reference genomes. RESULTS: We proposed a novel, simple method to account for some of the intra-matrix correlations in improving the prediction accuracy. Specifically, the phylogenetic species tree of the reference genomes is used as a guide tree for hierarchical clustering of the orthologous proteins. The distances between these clusters, derived from the original pairwise distance matrix using the Neighbor Joining algorithm, form intermediate distance matrices, which are then transformed and concatenated into a super phylogenetic vector. A support vector machine is trained and tested on pairs of proteins, represented as super phylogenetic vectors, whose interactions are known. The performance, measured as ROC score in cross validation experiments, shows significant improvement of our method (ROC score 0.8446) over that of using Pearson correlations (0.6587). CONCLUSION: We have shown that the phylogenetic tree can be used as a guide to extract intra-matrix correlations in the distance matrices of orthologous proteins, where these correlations are represented as intermediate distance matrices of the ancestral orthologous proteins. Both the unsupervised and supervised learning paradigms benefit from the explicit inclusion of these intermediate distance matrices, and particularly so in the latter case, which offers a better balance between sensitivity and specificity in the prediction of protein-protein interactions

Redeployment-based drug screening identifies the anti-helminthic niclosamide as anti-myeloma therapy that also reduces free light chain production

Despite recent therapeutic advancements, multiple myeloma (MM) remains incurable and new therapies are needed, especially for the treatment of elderly and relapsed/refractory patients. We have screened a panel of 100 off-patent licensed oral drugs for anti-myeloma activity and identified niclosamide, an anti-helminthic. Niclosamide, at clinically achievable non-toxic concentrations, killed MM cell lines and primary MM cells as efficiently as or better than standard chemotherapy and existing anti-myeloma drugs individually or in combinations, with little impact on normal donor cells. Cell death was associated with markers of both apoptosis and autophagy. Importantly, niclosamide rapidly reduced free light chain (FLC) production by MM cell lines and primary MM. FLCs are a major cause of renal impairment in MM patients and light chain amyloid and FLC reduction is associated with reversal of tissue damage. Our data indicate that niclosamides anti-MM activity was mediated through the mitochondria with rapid loss of mitochondrial membrane potential, uncoupling of oxidative phosphorylation and production of mitochondrial superoxide. Niclosamide also modulated the nuclear factor-κB and STAT3 pathways in MM cells. In conclusion, our data indicate that MM cells can be selectively targeted using niclosamide while also reducing FLC secretion. Importantly, niclosamide is widely used at these concentrations with minimal toxicity

Evolutionary Mirages: Selection on Binding Site Composition Creates the Illusion of Conserved Grammars in Drosophila Enhancers

The clustering of transcription factor binding sites in developmental enhancers and the apparent preferential conservation of clustered sites have been widely interpreted as proof that spatially constrained physical interactions between transcription factors are required for regulatory function. However, we show here that selection on the composition of enhancers alone, and not their internal structure, leads to the accumulation of clustered sites with evolutionary dynamics that suggest they are preferentially conserved. We simulated the evolution of idealized enhancers from Drosophila melanogaster constrained to contain only a minimum number of binding sites for one or more factors. Under this constraint, mutations that destroy an existing binding site are tolerated only if a compensating site has emerged elsewhere in the enhancer. Overlapping sites, such as those frequently observed for the activator Bicoid and repressor Krüppel, had significantly longer evolutionary half-lives than isolated sites for the same factors. This leads to a substantially higher density of overlapping sites than expected by chance and the appearance that such sites are preferentially conserved. Because D. melanogaster (like many other species) has a bias for deletions over insertions, sites tended to become closer together over time, leading to an overall clustering of sites in the absence of any selection for clustered sites. Since this effect is strongest for the oldest sites, clustered sites also incorrectly appear to be preferentially conserved. Following speciation, sites tend to be closer together in all descendent species than in their common ancestors, violating the common assumption that shared features of species' genomes reflect their ancestral state. Finally, we show that selection on binding site composition alone recapitulates the observed number of overlapping and closely neighboring sites in real D. melanogaster enhancers. Thus, this study calls into question the common practice of inferring “cis-regulatory grammars” from the organization and evolutionary dynamics of developmental enhancers

Identifying Cognate Binding Pairs among a Large Set of Paralogs: The Case of PE/PPE Proteins of Mycobacterium tuberculosis

We consider the problem of how to detect cognate pairs of proteins that bind when each belongs to a large family of paralogs. To illustrate the problem, we have undertaken a genomewide analysis of interactions of members of the PE and PPE protein families of Mycobacterium tuberculosis. Our computational method uses structural information, operon organization, and protein coevolution to infer the interaction of PE and PPE proteins. Some 289 PE/PPE complexes were predicted out of a possible 5,590 PE/PPE pairs genomewide. Thirty-five of these predicted complexes were also found to have correlated mRNA expression, providing additional evidence for these interactions. We show that our method is applicable to other protein families, by analyzing interactions of the Esx family of proteins. Our resulting set of predictions is a starting point for genomewide experimental interaction screens of the PE and PPE families, and our method may be generally useful for detecting interactions of proteins within families having many paralogs

Molecular purging of multiple myeloma cells by ex-vivo culture and retroviral transduction of mobilized-blood CD34+ cells

<p>Abstract</p> <p>Background</p> <p>Tumor cell contamination of the apheresis in multiple myeloma is likely to affect disease-free and overall survival after autografting.</p> <p>Objective</p> <p>To purge myeloma aphereses from tumor contaminants with a novel culture-based purging method.</p> <p>Methods</p> <p>We cultured myeloma-positive CD34⁺PB samples in conditions that retained multipotency of hematopoietic stem cells, but were unfavourable to survival of plasma cells. Moreover, we exploited the resistance of myeloma plasma cells to retroviral transduction by targeting the hematopoietic CD34⁺cell population with a retroviral vector carrying a selectable marker (the truncated form of the human receptor for nerve growth factor, ΔNGFR). We performed therefore a further myeloma purging step by selecting the transduced cells at the end of the culture.</p> <p>Results</p> <p>Overall recovery of CD34⁺cells after culture was 128.5%; ΔNGFR transduction rate was 28.8% for CD34⁺cells and 0% for CD138-selected primary myeloma cells, respectively. Recovery of CD34⁺cells after ΔNGFR selection was 22.3%. By patient-specific Ig-gene rearrangements, we assessed a decrease of 0.7–1.4 logs in tumor load after the CD34⁺cell selection, and up to 2.3 logs after culture and ΔNGFR selection.</p> <p>Conclusion</p> <p>We conclude that <it>ex-vivo </it>culture and retroviral-mediated transduction of myeloma leukaphereses provide an efficient tumor cell purging.</p

The Drosophila Gap Gene Network Is Composed of Two Parallel Toggle Switches

Drosophila “gap” genes provide the first response to maternal gradients in the early fly embryo. Gap genes are expressed in a series of broad bands across the embryo during first hours of development. The gene network controlling the gap gene expression patterns includes inputs from maternal gradients and mutual repression between the gap genes themselves. In this study we propose a modular design for the gap gene network, involving two relatively independent network domains. The core of each network domain includes a toggle switch corresponding to a pair of mutually repressive gap genes, operated in space by maternal inputs. The toggle switches present in the gap network are evocative of the phage lambda switch, but they are operated positionally (in space) by the maternal gradients, so the synthesis rates for the competing components change along the embryo anterior-posterior axis. Dynamic model, constructed based on the proposed principle, with elements of fractional site occupancy, required 5–7 parameters to fit quantitative spatial expression data for gap gradients. The identified model solutions (parameter combinations) reproduced major dynamic features of the gap gradient system and explained gap expression in a variety of segmentation mutants