Reese A. K. Richardson

The condition of having a healthy, functional proteome is known as protein homeostasis, or proteostasis. Maintaining proteostasis is the province of the proteostasis network, which consists of the cellular components that regulate protein synthesis, folding, localization, and degradation. The proteostasis network is a fundamental functional unit in biology with relevance to many diseases. Despite its importance, the human proteostasis network has until now lacked a precise definition, limiting our ability to interrogate proteostasis, integrate multiomic datasets, compare results across studies, or model proteostasis dysfunction in disease. To address this limitation, we have operationally defined the human proteostasis network by providing a comprehensive, annotated, and manually curated list of its components classified into nine Branches based on their function and localization - cytonuclear proteostasis, ER proteostasis, mitochondrial proteostasis, nuclear proteostasis, proteostasis network regulation, translation, extracellular proteostasis, the autophagy-lysosome pathway, and the ubiquitin-proteasome system - which we present in a series of three manuscripts of which this is the first. Overall, the proteostasis network annotation encompasses 3,151 components described by 4,043 annotations. Here, we present the 1,190 components that make up the first seven of these Branches, which comprise the compartment-based proteostasis network (cytonuclear, ER, mitochondrial, nuclear, and extracellular proteostasis), proteostasis network regulation, and translation. These Branches include chaperones, folding enzymes, systems for trafficking proteins, organelle-specific degradation systems, and the cell's translation machinery. This work provides a framework that will facilitate insights into how proteostasis is preserved in health, lost in aging and disease, and how it could be restored through pharmacological intervention.

Investigating the integrity of published scientific papers is key to the scientific process, but the necessary knowledge is in short supply. We present COSIG, an open collection of meta-scientific guides enabling anyone to perform forensic peer review.

The combination of open-access datasets, machine learning workflows, increased computing capacity, and generative artificial intelligence has effectively removed many of the rate-limiting steps in manuscript production. This has created an industry of click-data science and a flood of low-quality manuscripts based on large health datasets such as the US National Health and Nutrition Examination Survey, the UK Biobank, and the US FDA Adverse Event Reporting System. These papers often employ statistically appropriate methods and real data, but introduce misleading results and false discoveries to the literature. Here, we offer suggestions for editors on how to identify such manuscripts and reject them at the point of submission, reducing the burden on the publishing process.

Science is characterized by collaboration and cooperation, but also by uncertainty, competition, and inequality. While there has always been some concern that these pressures may compel some to defect from the scientific research ethos—i.e., fail to make genuine contributions to the production of knowledge or to the training of an expert workforce—the focus has largely been on the actions of lone individuals. Recently, however, reports of coordinated scientific fraud activities have increased. Some suggest that the ease of communication provided by the internet and open-access publishing have created the conditions for the emergence of entities—paper mills (i.e., sellers of mass-produced low quality and fabricated research), brokers (i.e., conduits between producers and publishers of fraudulent research), predatory journals, who do not conduct any quality controls on submissions—that facilitate systematic scientific fraud. Here, we demonstrate through case studies that i) individuals have cooperated to publish papers that were eventually retracted in a number of journals, ii) brokers have enabled publication in targeted journals at scale, and iii), within a field of science, not all subfields are equally targeted for scientific fraud. Our results reveal some of the strategies that enable the entities promoting scientific fraud to evade interventions. Our final analysis suggests that this ability to evade interventions is enabling the number of fraudulent publications to grow at a rate far outpacing that of legitimate science.

Materials science and engineering (MSE) research has, for the most part, escaped the doubts raised about the reliability of the scientific literature by recent large-scale replication studies in psychology and cancer biology. However, users on post-publication peer review sites have recently identified dozens of articles where the make and model of the scanning electron microscope (SEM) listed in the text of the paper does not match the instrument’s metadata visible in the images in the published article. In order to systematically investigate this potential risk to the MSE literature, we develop a semi-automated approach to scan published figures for this metadata and check it against the SEM instrument identified in the text. Starting from an exhaustive set of 1,067,108 articles published since 2010 in 50 journals with impact factors ranging from 2 to 24, we identify 11,314 articles for which SEM manufacturer and model can be identified in an image’s metadata. For 21.2% of those articles, the image metadata does not match the SEM manufacturer or model listed in the text and, for another 24.7%, at least some of the instruments used in the study are not reported. We find that articles with SEM misidentification are more likely to have existing observations of improprieties made on post-publication peer review site PubPeer than other MSE articles and that a subset of these articles within the subfield of electrochemistry are more likely to incorrectly estimate the optical band gap if the article features SEM misidentification. This suggests that SEM misidentification may be a tractable signature for flagging problematic MSE articles. Unexplained patterns common to many of these articles suggest the involvement of paper mills, organizations that mass-produce, sell authorship on, and publish fraudulent scientific manuscripts at scale.

Materials science and engineering (MSE) research has, for the most part, escaped the doubtsraised about the reliability of the scientific literature by recent large-scale replication studies inpsychology and cancer biology. However, users on post-publication peer review sites have re-cently identified dozens of articles where the make and model of the scanning electron micro-scope (SEM) listed in the text of the paper does not match the instrument’s metadata visible inthe images in the published article. In order to systematically investigate this potential risk to theMSE literature, we develop a semi-automated approach to scan published figures for this meta-data and check it against the SEM instrument identified in the text. Starting from an exhaustiveset of 1,067,102 articles published since 2010 in 50 journals with impact factors ranging from 2to 24, we identify 11,314 articles for which SEM make and model can be identified in an image’smetadata. For 21.2% of those articles, the image metadata does not match the SEM manufactureror model listed in the text and, for another 24.7%, at least some of the instruments used in thestudy are not reported.

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.