COVID-19 research risks ignoring important host genes due to pre-established research patterns

A graph theoretical approach to experimental prioritization in genome-scale investigations

The goal of systems biology is to gain a network level understanding of how gene interactions influence biological states, and ultimately inform upon human disease. Given the scale and scope of systems biology studies, resource constraints often limit researchers when validating genome-wide phenomena and potentially lead to an incomplete understanding of the underlying mechanisms. Further, prioritization strategies are often biased towards known entities (e.g. previously studied genes/proteins with commercially available reagents), and other technical issues that limit experimental breadth. Here, heterogeneous biological information is modeled as an association graph to which a high-performance minimum dominating set solver is applied to maximize coverage across the graph, and thus increase the breadth of experimentation. First, we tested our model on retrieval of existing gene functional annotations and demonstrated that minimum dominating set returns more diverse terms when compared to other computational methods. Next, we utilized our heterogenous network and minimum dominating set solver to assist in the process of identifying understudied genes to be interrogated by the International Mouse Phenotyping Consortium. Using an unbiased algorithmic strategy, poorly studied genes are prioritized from the remaining thousands of genes yet to be characterized. This method is tunable and extensible with the potential to incorporate additional user-defined prioritizing information. The minimum dominating set approach can be applied to any biological network in order to identify a tractable subset of features to test experimentally or to assist in prioritizing candidate genes associated with human disease.

Funding priorities and health outcomes in Danish medical research

External research funding is an essential component of the infrastructure of modern, academic research. Priorities in funding decisions drive what knowledge is generated, and how scientists' careers are shaped. For health research, it can ultimately have implications for health outcomes. The aim of this paper is to illustrate how funding information can be used to track priorities in health research, linking them to disease burdens and research outputs. Furthermore, funding concentrations are analysed from both researcher and disease perspectives, to estimate the influence of personal Matthew-effects on the distribution of health research funding. Denmark is used as the case, including funding information from all major public and private research foundations in the period 2004-2016. Grant information is linked to research outputs and disability-adjusted life-years (DALY rates), for 34,160 publications linked to 2630 grants, receiving DKK 4.8 billion in funding. Data show poor correlation between funding priorities, research activity and disease burdens, with several diseases receiving disproportionate amounts of funding. A research opportunity index is calculated to identify diseases with the highest potential for future investments from a burden-centred point of view. Funding is highly concentrated, both on people and on specific diseases. High funding concentrations on researchers can be a driving factor behind the observed funding-to-burden imbalances, and may risk knowledge stagnation through monopolisation of the market place of ideas. Results indicate that funders of clinical and translational research, as well as some types of biomedical research, need to supplement traditional considerations of scientific excellence with measures of societal challenges and relevance.

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

The Interleukin-6 gene variants may protect against SARS-CoV-2 infection and the severity of COVID-19: a case-control study in a Moroccan population

The symptoms of SARS-CoV-2 infection vary widely, ranging from asymptomatic cases to severe forms marked by acute respiratory distress syndrome, multi-organ damage, and fatalities. Studies indicate a correlation between specific genes and susceptibility to SARS-CoV-2 infection and disease severity, particularly involving variants in genes linked to inflammation and immune responses. The objective of this study is to investigate the association between rs1800795 (- 174 G > C) and rs1800797 (- 597 A > G) variants in the interleukin-6 (IL-6) promoter region and susceptibility to SARS-CoV-2 infection. Additionally, we aim to explore their correlation with COVID-19 severity in a Moroccan population. In this case-control study, we enrolled 270 unvaccinated COVID-19 patients, consisting of 132 with severe COVID-19 and 138 with asymptomatic-moderate COVID-19. Additionally, we included 339 SARS-CoV-2-negative group. Genotyping of rs1800795 and rs1800797 polymorphisms of the IL-6 gene was performed using predesigned TaqMan SNP genotyping. The median age of SARS-CoV-2-negative controls was 50 years, while severe COVID-19 cases exhibited a median age of 61 years. Additionally, individuals with asymptomatic to moderate COVID-19 had a median age of 36 years. We observed a significant age difference between severe and mild COVID-19 patients (p < 0.0001), and an association was noted between gender and the severity of COVID-19 (p = 0.011). The allele and genotype frequencies of the IL-6 - 597G > A and - 174G > C variants did not show significant associations with susceptibility to SARS-CoV-2 infection (p > 0.05). However, further analysis revealed that the linkage disequilibrium between rs1800797 and rs1800795 indicated that individuals with the GC* haplotype (OR = 0.04, 95% CI 0.01-0.30, p = 0.001) and AG* haplotype (OR = 0.11, 95% CI 0.03-0.46, p = 0.002) were significantly associated with protection against SARS-CoV-2 infection. Moreover, in the overdominant model, the IL-6 - 174 G/C genotype was found to be protective against the development of severe disease compared to those with the G/G-C/C genotypes (p = 0.03; OR = 0.41, 95% CI 0.18-0.96). However, correlations between complete blood count markers, hematological markers, D-dimer, C-reactive protein, and ferritin levels according to - 597 A > G and - 174G > C genotypes showed no significant differences (all p > 0.05). Our findings provide valuable insights into the pathogenesis of COVID-19, suggesting that genetic variations at the IL-6 gene may contribute to the susceptibility to severe SARS-CoV-2 infection within the Moroccan population.

Meta-Research: Understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

Meta-Research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of - omics studies. To promote the investigation of understudied genes we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

Did the early full genome sequencing of yeast boost gene function discovery?

BACKGROUND

Although the genome of Saccharomyces cerevisiae (S. cerevisiae) was the first one of a eukaryote organism that was fully sequenced (in 1996), a complete understanding of the potential of encoded biomolecular mechanisms has not yet been achieved. Here, we wish to quantify how far the goal of a full list of S. cerevisiae gene functions still is.

RESULTS

The scientific literature about S. cerevisiae protein-coding genes has been mapped onto the yeast genome via the mentioning of names for genomic regions in scientific publications. The match was quantified with the ratio of a given gene name's occurrences to those of any gene names in the article. We find that ~ 230 elite genes with ≥ 75 full publication equivalents (FPEs, FPE = 1 is an idealized publication referring to just a single gene) command ~ 45% of all literature. At the same time, about two thirds of the genes (each with less than 10 FPEs) are described in just 12% of the literature (in average each such gene has just ~ 1.5% of the literature of an elite gene). About 600 genes have not been mentioned in any dedicated article. Compared with other groups of genes, the literature growth rates were highest for uncharacterized or understudied genes until late nineties of the twentieth century. Yet, these growth rates deteriorated and became negative thereafter. Thus, yeast function discovery for previously uncharacterized genes has returned to the level of ~ 1980. At the same time, literature for anyhow well-studied genes (with a threshold T10 (≥ 10 FPEs) and higher) remains steadily growing.

CONCLUSIONS

Did the early full genome sequencing of yeast boost gene function discovery? The data proves that the moment of publishing the full genome in reality coincides with the onset of decline of gene function discovery for previously uncharacterized genes. If the current status of literature about yeast molecular mechanisms can be extrapolated into the future, it will take about another ~ 50 years to complete the yeast gene function list. We found that a small group of scientific journals contributed extraordinarily to publishing early reports relevant to yeast gene function discoveries.

Ageing restructures the transcriptome of the hypothalamic supraoptic nucleus and alters the response to dehydration

Ageing is associated with altered neuroendocrine function. In the context of the hypothalamic supraoptic nucleus, which makes the antidiuretic hormone vasopressin, ageing alters acute responses to hyperosmotic cues, rendering the elderly more susceptible to dehydration. Chronically, vasopressin has been associated with numerous diseases of old age, including type 2 diabetes and metabolic syndrome. Bulk RNAseq transcriptome analysis has been used to catalogue the polyadenylated supraoptic nucleus transcriptomes of adult (3 months) and aged (18 months) rats in basal euhydrated and stimulated dehydrated conditions. Gene ontology and Weighted Correlation Network Analysis revealed that ageing is associated with alterations in the expression of extracellular matrix genes. Interestingly, whilst the transcriptomic response to dehydration is overall blunted in aged animals compared to adults, there is a specific enrichment of differentially expressed genes related to neurodegenerative processes in the aged cohort, suggesting that dehydration itself may provoke degenerative consequences in aged rats.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Protection of the human gene research literature from contract cheating organizations known as research paper mills

Human gene research generates new biology insights with translational potential, yet few studies have considered the health of the human gene literature. The accessibility of human genes for targeted research, combined with unreasonable publication pressures and recent developments in scholarly publishing, may have created a market for low-quality or fraudulent human gene research articles, including articles produced by contract cheating organizations known as paper mills. This review summarises the evidence that paper mills contribute to the human gene research literature at scale and outlines why targeted gene research may be particularly vulnerable to systematic research fraud. To raise awareness of targeted gene research from paper mills, we highlight features of problematic manuscripts and publications that can be detected by gene researchers and/or journal staff. As improved awareness and detection could drive the further evolution of paper mill-supported publications, we also propose changes to academic publishing to more effectively deter and correct problematic publications at scale. In summary, the threat of paper mill-supported gene research highlights the need for all researchers to approach the literature with a more critical mindset, and demand publications that are underpinned by plausible research justifications, rigorous experiments and fully transparent reporting.

COVID-19: impact on Public Health and hypothesis-driven investigations on genetic susceptibility and severity

COVID-19 is a new complex multisystem disease caused by the novel coronavirus SARS-CoV-2. In slightly over 2 years, it infected nearly 500 million and killed 6 million human beings worldwide, causing an unprecedented coronavirus pandemic. Currently, the international scientific community is engaged in elucidating the molecular mechanisms of the pathophysiology of SARS-CoV-2 infection as a basis of scientific developments for the future control of COVID-19. Global exome and genome analysis efforts work to define the human genetics of protective immunity to SARS-CoV-2 infection. Here, we review the current knowledge regarding the SARS-CoV-2 infection, the implications of COVID-19 to Public Health and discuss genotype to phenotype association approaches that could be exploited through the selection of candidate genes to identify the genetic determinants of severe COVID-19.

The characteristics of early-stage research into human genes are substantially different from subsequent research

Throughout the last 2 decades, several scholars observed that present day research into human genes rarely turns toward genes that had not already been extensively investigated in the past. Guided by hypotheses derived from studies of science and innovation, we present here a literature-wide data-driven meta-analysis to identify the specific scientific and organizational contexts that coincided with early-stage research into human genes throughout the past half century. We demonstrate that early-stage research into human genes differs in team size, citation impact, funding mechanisms, and publication outlet, but that generalized insights derived from studies of science and innovation only partially apply to early-stage research into human genes. Further, we demonstrate that, presently, genome biology accounts for most of the initial early-stage research, while subsequent early-stage research can engage other life sciences fields. We therefore anticipate that the specificity of our findings will enable scientists and policymakers to better promote early-stage research into human genes and increase overall innovation within the life sciences.

Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2

BACKGROUND

Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction.

RESULTS

We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents.

CONCLUSIONS

We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.

Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks

The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene’s popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear computational approaches for estimating gene popularity to then compare their relative importance. We find that roughly 25% of the studies are the result of a historical positive feedback, which we may think of as social reinforcement. Of the remaining features, gene family membership is the most indicative followed by disease relevance and finally regulatory pathway association. Disease relevance has been an important driver until the 1990s, after which the focus shifted to exploring every single gene. We also present a resource that allows one to study the impact of reinforcement, which may guide our research toward genes that have not yet received proportional attention.

HOW GENETICISTS CONTRIBUTE TO UNDERSTANDING OF COVID-19 DISEASE PATHOGENICITY

Human populations are faced to the COVID-19 pandemic due to the emerging SARS-CoV-2 coronavirus originating from Wuhan (China) and with dramatic Public Health consequences. Despite periods of panic, the scientific community demonstrated an incredible innovation potential and energy ending up in one year with new vaccines to be used in population. Researchers are interrogating on how individual genetic differences contribute to the diversity of clinical manifestations or ethnic and geographic disparities of COVID-19. While efforts were spent to understand mechanistically the infectious potential of the virus, recent progresses in molecular genetics and bioinformatics allowed the characterization of viral sequence and construction of phylogeographical maps of viral dispersion worldwide. These data will help understanding epidemiological disparities among continents and ethnic populations. Much effort was also spent in analyzing host genetics by studying individual genes involved in innate and immune responses or explaining pathogenesis of comorbidities that complicate the fate of elderly patients. Several international consortia launched already Genome wide Association Studies (GWAS) and whole genome sequencing strategies to identify genetic markers with immediate application in patients at risk of respiratory failure. These new genetic data are important not only for understanding susceptibility factors for COVID-19 but they also contain an important message of hope for mankind warranting our survival and health.