Comprehensive Assessment of Toxicity in ChatGPT

Moderating offensive, hateful, and toxic language has always been an important but challenging topic in the domain of safe use in NLP. The emerging large language models (LLMs), such as ChatGPT, can potentially further accentuate this threat. Previous works have discovered that ChatGPT can generate toxic responses using carefully crafted inputs. However, limited research has been done to systematically examine when ChatGPT generates toxic responses. In this paper, we comprehensively evaluate the toxicity in ChatGPT by utilizing instruction-tuning datasets that closely align with real-world scenarios. Our results show that ChatGPT's toxicity varies based on different properties and settings of the prompts, including tasks, domains, length, and languages. Notably, prompts in creative writing tasks can be 2x more likely than others to elicit toxic responses. Prompting in German and Portuguese can also double the response toxicity. Additionally, we discover that certain deliberately toxic prompts, designed in earlier studies, no longer yield harmful responses. We hope our discoveries can guide model developers to better regulate these AI systems and the users to avoid undesirable outputs

Mechanisms underlying divergent responses of genetically distinct macrophages to IL-4

Mechanisms by which noncoding genetic variation influences gene expression remain only partially understood but are considered to be major determinants of phenotypic diversity and disease risk. Here, we evaluated effects of >50 million single-nucleotide polymorphisms and short insertions/deletions provided by five inbred strains of mice on the responses of macrophages to interleukin-4 (IL-4), a cytokine that plays pleiotropic roles in immunity and tissue homeostasis. Of >600 genes induced >2-fold by IL-4 across the five strains, only 26 genes reached this threshold in all strains. By applying deep learning and motif mutation analyses to epigenetic data for macrophages from each strain, we identified the dominant combinations of lineage-determining and signal-dependent transcription factors driving IL-4 enhancer activation. These studies further revealed mechanisms by which noncoding genetic variation influences absolute levels of enhancer activity and their dynamic responses to IL-4, thereby contributing to strain-differential patterns of gene expression and phenotypic diversity

Diverse motif ensembles specify non-redundant DNA binding activities of AP-1 family members in macrophages

Mechanisms by which members of the AP-1 family of transcription factors play non-redundant biological roles despite recognizing the same DNA sequence remain poorly understood. To address this question, here we investigate the molecular functions and genome-wide DNA binding patterns of AP-1 family members in primary and immortalized mouse macrophages. ChIP-sequencing shows overlapping and distinct binding profiles for each factor that were remodeled following TLR4 ligation. Development of a machine learning approach that jointly weighs hundreds of DNA recognition elements yields dozens of motifs predicted to drive factor-specific binding profiles. Machine learning-based predictions are confirmed by analysis of the effects of mutations in genetically diverse mice and by loss of function experiments. These findings provide evidence that non-redundant genomic locations of different AP-1 family members in macrophages largely result from collaborative interactions with diverse, locus-specific ensembles of transcription factors and suggest a general mechanism for encoding functional specificities of their common recognition motif

SALL1 enforces microglia-specific DNA binding and function of SMADs to establish microglia identity

Spalt-like transcription factor 1 (SALL1) is a critical regulator of organogenesis and microglia identity. Here we demonstrate that disruption of a conserved microglia-specific super-enhancer interacting with the Sall1 promoter results in complete and specific loss of Sall1 expression in microglia. By determining the genomic binding sites of SALL1 and leveraging Sall1 enhancer knockout mice, we provide evidence for functional interactions between SALL1 and SMAD4 required for microglia-specific gene expression. SMAD4 binds directly to the Sall1 super-enhancer and is required for Sall1 expression, consistent with an evolutionarily conserved requirement of the TGFβ and SMAD homologs Dpp and Mad for cell-specific expression of Spalt in the Drosophila wing. Unexpectedly, SALL1 in turn promotes binding and function of SMAD4 at microglia-specific enhancers while simultaneously suppressing binding of SMAD4 to enhancers of genes that become inappropriately activated in enhancer knockout microglia, thereby enforcing microglia-specific functions of the TGFβ–SMAD signaling axis.</p

The Epigenetic State of IL-4-Polarized Macrophages Enables Inflammatory Cistromic Expansion and Extended Synergistic Response to TLR Ligands

Prior exposure to microenvironmental signals could fundamentally change the response of macrophages to subsequent stimuli. It is believed that T helper-2 (Th2)-cell-type cytokine interleukin-4 (IL-4) and Toll-like receptor (TLR) ligand-activated transcriptional programs mutually antagonize each other, and no remarkable convergence has been identified between them. In contrast, here, we show that IL-4-polarized macrophages established a hyperinflammatory gene expression program upon lipopolysaccharide (LPS) exposure. This phenomenon, which we termed extended synergy, was supported by IL-4-directed epigenomic remodeling, LPS-activated NF-κB-p65 cistrome expansion, and increased enhancer activity. The EGR2 transcription factor contributed to the extended synergy in a macrophage-subtype-specific manner. Consequently, the previously alternatively polarized macrophages produced increased amounts of immune-modulatory factors both in vitro and in vivo in a murine Th2 cell-type airway inflammation model upon LPS exposure. Our findings establish that IL-4-induced epigenetic reprogramming is responsible for the development of inflammatory hyperresponsiveness to TLR activation and contributes to lung pathologies

Investigating the Effects of Genetic Variation on Transcriptional Regulation

Thousands of genetic variants have been found to increase disease risk based on genome-wide association studies. Many of these variants are located outside of protein-coding regions, suggesting their regulatory effects on gene transcription. However, it is not fully understood the effects of non-coding genetic variation on transcriptional regulation. One way of interpreting these variants is to link with the specific DNA sequences recognized by transcription factors (TFs), which are also called motifs. I developed MAGGIE, a bioinformatic approach to identify functional motifs that mediate TF binding and function. Unlike many other motif analysis tools, MAGGIE associates motif mutations caused by non-coding variants with the changes in TF binding or regulatory function to provide more direct insights into the regulatory effects of genetic variation. I showed the outstanding performance of MAGGIE in various applications, including its ability to distinguish the divergent functions of distinct NF-kB factors in pro-inflammatory macrophages. As a detailed case study of the effects of non-coding variants, I applied MAGGIE to identify functional motifs for anti-inflammatory macrophages and discovered dominant TFs driving the anti-inflammatory response, which are also the frequent targets of genetic variation to influence such response. In combination with an integrative analysis of transcriptomic and epigenomic data, I revealed quantitative variations in motif affinity underlying the divergent anti-inflammatory responses observed in genetically different mouse strains. By leveraging deep learning approaches, I pinpointed functional variants altering functional motifs and provided strong evidence supporting the promise of using deep learning to identify functional variants. Finally, I went beyond motifs to systematically analyze the spacing between motifs and investigated its significance in the context of variant interpretation. I found most collaborative TFs do not require a constrained spacing but allow a relaxed range of spacing in between. Based on synthetic genetic variations from mutagenesis experiments and millions of naturally occurring variations, I showed that spacing alterations are generally tolerated by TF binding and regulatory function at TF binding sites. Collectively, these findings advance our understanding of how non-coding genetic variation influences gene transcription and phenotypic diversity

Evidence for volcanism and weathering during the Permian-Triassic mass extinction from Meishan (South China) osmium isotope record

The Permian–Triassic mass extinction event is the most severe biotic crisis during the Phanerozoic. The trigger of this event has been widely linked with massive volcanic activity associated with the Siberian Traps Large Igneous Province. However, the direct link is still lacking to fully understand the event. In this study, we apply osmium isotope (187Os/188Os, or Osi) stratigraphy across the Permian–Triassic boundary interval in the Meishan section of South China. The Os isotope stratigraphy reveals multiple shifts to more unradiogenic 187Os/188Os composition that are interpreted to reflect pulses of volcanism across the mass extinction interval. Additionally, a shift to a more radiogenic 187Os/188Os composition is also found immediately above the mass extinction interval, which is taken to reflect the enhanced weathering of the continental crust in response to greenhouse gas release into the atmosphere and the associated hyperthermal