Co-regulation Patterns Research Articles

Dynamic respiratory sinus arrhythmia self-regulation and coregulation in response to caregiving challenges in at-risk mother-child and father-child dyads.

We examined how mother-preschooler and father-preschooler dyads differed in dynamic self-regulation and time-lagged coregulation of respiratory sinus arrhythmia (RSA) when having to transition from play into a challenging disciplinary context, and how individual and dyadic regulatory responses to this challenge varied by parenting risk. Participants included 78 mother-preschooler and 51 father-preschooler dyads (53% female, 63.3% non-Hispanic White) oversampled for familial risk. At 2½ years, parents self-reported harsh parenting. At 3 years, parent-child RSA was collected during free play and cleanup tasks. Multilevel models of time-lagged RSA (i.e., parent RSA predicting child RSA in the next time unit and vice versa) were conducted. In response to a task with increased challenge and parenting demands, mothers and children showed expected individual RSA decreases (indicating active regulation), whereas fathers showed increases in RSA (suggesting decreasing arousal or disengagement). Mother-driven negative time-lagged RSA coregulation and father-driven positive time-lagged RSA coregulation were observed during play, but not during cleanup. Harsh parenting was associated with altered RSA responses to challenge: During cleanup, harsher mothers showed no active regulation, suggesting disengagement, harsher fathers showed more stability in RSA self-regulation, and child-driven negative RSA coregulation with harsher fathers was observed. Findings suggest that during preschool, (a) parents are the typical drivers of RSA coregulation, (b) challenging contexts and parenting risk alter dynamic RSA self-regulation and time-lagged RSA coregulation, and (c) typical and atypical RSA self-regulation and time-lagged coregulation patterns differ between mother-child and father-child dyads. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Binge Eating and Obesity Differentially Alter the Mesolimbic Endocannabinoid System in Rats

Binge eating disorder (BED) is characterized by the rapid overconsumption of palatable food in a short amount of time, often leading to obesity. The endocannabinoid system (ECS), a system involved in palatable food intake, is highly expressed in reward-related brain regions and is involved in both obesity and BED. This study investigated differences in ECS expression between these conditions using male Wistar rats exposed to specific regimen over six weeks: a non-access group (NA) with a standard diet, a continuous access group (CA) with free-choice high-fat high-sugar (fcHFHS) diet modeling obesity, and an intermittent access group (IA) with intermittent fcHFHS access modeling BED. Food intake was measured, and brain tissues from the nucleus accumbens (NAc), dorsal striatum (DS), ventral tegmental area (VTA), and rostromedial tegmental nucleus (RMTg) were analyzed for ECS expression using qPCR and mass spectrometry. We identified differential ECS expression across palatable food access groups, with variations depending on the brain region (striatal or mesencephalic). Correlation analyses revealed ECS dysregulations dependent on the type (fat or sucrose) and quantity of palatable food consumed. Comparative network analysis revealed co-regulation patterns of ECS-related genes with specific signatures associated with each eating pattern, highlighting RMTg as a key region for future research in eating behavior.

Meta-Analysis of Transcriptomic Studies of Blood and Six Brain Regions Identifies a Consensus of 15 Cross-Tissue Mechanisms in Alzheimer's Disease and Suggests an Origin of Cross-Study Heterogeneity.

The comprehensive genome-wide nature of transcriptome studies in Alzheimer's disease (AD) should provide a reliable description of disease molecular states. However, the genes and molecular systems nominated by transcriptomic studies do not always overlap. Even when results do align, it is not clear if those observations represent true consensus across many studies. A couple of sources of variation have been proposed to explain this variability, including tissue-of-origin and cohort type, but its basis remains uncertain. To address this variability and extract reliable results, we utilized all publicly available blood or brain transcriptomic datasets of AD, comprised of 24 brain studies with 4007 samples from six different brain regions, and eight blood studies with 1566 samples. We identified a consensus of AD-associated genes across brain regions and AD-associated gene-sets across blood and brain, generalizable machine learning and linear scoring classifiers, and significant contributors to biological diversity in AD datasets. While AD-associated genes did not significantly overlap between blood and brain, our findings highlighted 15 dysregulated processes shared across blood and brain in AD. The top five most significantly dysregulated processes were DNA replication, metabolism of proteins, protein localization, cell cycle, and programmed cell death. Conversely, addressing the discord across studies, we found that large-scale gene co-regulation patterns can account for a significant fraction of variability in AD datasets. Overall, this study ranked and characterized a compilation of genes and molecular systems consistently identified across a large assembly of AD transcriptome studies in blood and brain, providing potential candidate biomarkers and therapeutic targets.

Dual RNA-seq reveals distinct families of co-regulated and structurally conserved effectors in Botrytis cinerea infection of Arabidopsis thaliana

BackgroundBotrytis cinerea is a broad-host-range pathogen causing gray mold disease and significant yield losses of numerous crops. However, the mechanisms underlying its rapid invasion and efficient killing of plant cells remain unclear.ResultsIn this study, we elucidated the dynamics of B. cinerea infection in Arabidopsis thaliana by live cell imaging and dual RNA sequencing. We found extensive transcriptional reprogramming events in both the pathogen and the host, which involved metabolic pathways, signaling cascades, and transcriptional regulation. For the pathogen, we identified 591 candidate effector proteins (CEPs) and comprehensively analyzed their co-expression, sequence similarity, and structural conservation. The results revealed temporal co-regulation patterns of these CEPs, indicating coordinated deployment of effectors during B. cinerea infection. Through functional screening of 48 selected CEPs in Nicotiana benthamiana, we identified 11 cell death-inducing proteins (CDIPs) in B. cinerea.ConclusionsThe findings provide important insights into the transcriptional dynamics and effector biology driving B. cinerea pathogenesis. The rapid infection of this pathogen involves the temporal co-regulation of CEPs and the prominent role of CDIPs in host cell death. This work highlights significant changes in gene expression associated with gray mold disease, underscoring the importance of a diverse repertoire of effectors crucial for successful infection.

How blue hat co-regulate pre-service teachers' collective reflection based upon the six-hat thinking technique: A lag sequential analysis

Collective reflection is essential for teachers' professional growth which involves multiple individuals participating in reflective exercises. However, collective reflection often faces problems such as disorganization and inadequate criticism. This study utilized the six-hat thinking framework to structure collective reflection among pre-service teachers during project-based instructional design learning. Despite its known collaboration-boosting effects, the impact of six-hat thinking technique on pre-service teachers' collective reflection, particularly within co-regulation, is under-explored. Focusing on Blue Hat's coordinating role on monitoring and planning, this study employed co-regulation coding and Lag Sequential Analysis (LSA) to analyze collective reflection behaviors. Findings uncovered co-regulation patterns within the six-hat-enhanced reflection, emphasizing Blue Hat's significance. This study distinguished regulation patterns between high and low-level understanding on grasping the six-hat thinking technique. High-understanding groups demonstrated co-regulation centered on regulation-evaluation, fostering self-assessment of instructional design projects and refinement planning. Conversely, low-understanding groups exhibited a pattern focused on task verification, neglecting group work regulation or evaluation. This study also delved into LSA-derived empirical insights on communication dynamics underlying co-regulation in six-hat-integrated collective reflection.

Co-regulation, relationship quality, and infant distress vocalizations observed during mother-infant interactions: Influences of maternal depression and different contexts.

Mother-infant interactions are co-regulated and provide the foundation for mother-infant relationship quality. The implications of maternal depression and contextual demands (i.e., reinstating the interaction following maternal unavailability and vocalized infant distress) on observationally coded co-regulation in mother-infant dyads (n=40) at 4-months was investigated. Associations among co-regulation patterns and mother-infant relationship quality was also examined. Dyads participated in Still-Face (SF) and Separation (SP) procedures, with periods of maternal emotional and physical unavailability. Co-regulation was captured using the Revised Relational Coding System. Relationship quality was examined using the Emotional Availability Scales. Dyads in the depressed group had significantly more unilateral exchanges than the non-depressed group following the SF and SP perturbations. The depressed group also had significantly more distress vocalizations during the SP perturbation than the non-depressed group. Co-regulation in the depressed group was less disrupted by the SF perturbation. Positive relationship quality dimensions (maternal sensitivity, structuring, and infant responsiveness) were associated with more symmetrical and less unilateral co-regulation regardless of the interaction period. There were also context-specific results pertaining to patterns of co-regulation and associated maternal hostility and infant responsiveness. Results highlight co-regulatory differences in depressed mothers and their infants and how these differences are exacerbated by contextual demands.

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

BackgroundThe genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.ResultsWe studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.ConclusionWe show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Abstract PR009: Exploring the functional consequences of APOBEC3-induced non-coding hotspot mutations in bladder cancer using massively parallel reporter assays and CRISPR-mediated base editing

Abstract Cancer research has traditionally focused on protein-coding genes, but approximately 98% of the human genome is non-coding, comprising gene regulatory elements such as enhancers, silencers, and promoters. Mutations within these regions can foster tumorigenesis by modulating the expression of oncogenes. Recent whole-genome sequencing studies have revealed several recurrently mutated non-coding genomic regions, including in bladder cancer, but their role in tumorigenesis remains unknown. In this study, we delved into whole-genome sequencing data from the bladder cancer TCGA cohort and an independent set of bladder tumors, uncovering hundreds of recurrent mutations. Mutational signature analysis identified over 65% of these mutations as APOBEC3-induced. We substantiated the involvement of APOBEC3 enzymes by using conventional deamination assays and novel approach dubbed as APOBEC3-mediated mutation capture assay. Predominantly, these mutations occurred in the hairpin loop, a favored motif for APOBEC3 enzymes. To systematically assess which of the mutations are functional and prioritize them for biological characterization, we developed Massively Parallel Reporter Assays (MPRA). Two types of MPRA libraries were constructed, one for evaluating promoter activity and the other for assessing enhancer activity of the mutations. Each library construction entailed 36,000 oligonucleotides, comprehensively representing all mutation hotspots and their corresponding wild-type alleles. The application of MPRA resulted in the identification of a substantial number of functionally relevant mutations. Zooming in on the top 20 genomic regions, we employed CRISPR-based approaches to pinpoint target genes and elucidate their functional consequences. Most of these regions co-localized with enhancers, as determined by analysis of ATAC-seq, ChIP-seq, and Hi-C data from bladder cancer cell lines. Perturbation of hotspots regions resulted in dysregulation of multiple genes. The co-regulation patterns of genes were further supported by strong correlation of their mRNA expression in RNA-seq datasets (TCGA, SRA, etc.). Notable examples included twin hotspot mutations upstream of the LEPROTL1 gene, elevating the expression of LEPROTL1, DCTN6 and SARAF, resulting in aggressive tumor growth in in vitro functional experiments. These three genes emerged as novel oncogenes in cancer. Another functional hotspot influenced the expression of two known long non-coding RNAs, NEAT1 and MALAT1, previously characterized as cancer genes. Additionally, some recurrently mutated regions or their target genes were associated with worse overall survival in the TCGA cohort. In conclusion, our findings propose that non-coding mutations likely drive tumorigenesis, contributing to tumor heterogeneity. Employing a combination of functional, multi-omics, and in vivo approaches, we aim to comprehensively characterize the role of hotspot non-coding mutations in bladder cancer and assess their clinical significance. Citation Format: Rouf Banday, Bilal Lone, Kelly Butler, Dhanusha Yesudhas, Alexandra Dobbins, Arup Chakraborty. Exploring the functional consequences of APOBEC3-induced non-coding hotspot mutations in bladder cancer using massively parallel reporter assays and CRISPR-mediated base editing [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr PR009.

Identification and coregulation pattern analysis of long noncoding RNAs in the mouse brain after Angiostrongylus cantonensis infection

BackgroundAngiostrongyliasis is a highly dangerous infectious disease. Angiostrongylus cantonensis larvae migrate to the mouse brain and cause symptoms, such as brain swelling and bleeding. Noncoding RNAs (ncRNAs) are novel targets for the control of parasitic infections. However, the role of these molecules in A. cantonensis infection has not been fully clarified.MethodsIn total, 32 BALB/c mice were randomly divided into four groups, and the infection groups were inoculated with 40 A. cantonensis larvae by gavage. Hematoxylin and eosin (H&E) staining and RNA library construction were performed on brain tissues from infected mice. Differential expression of long noncoding RNAs (lncRNAs) and mRNAs in brain tissues was identified by high-throughput sequencing. The pathways and functions of the differentially expressed lncRNAs were determined by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. The functions of the differentially expressed lncRNAs were further characterized by lncRNA‒microRNA (miRNA) target interactions. The potential host lncRNAs involved in larval infection of the brain were validated by quantitative real-time polymerase chain reaction (qRT‒PCR).ResultsThe pathological results showed that the degree of brain tissue damage increased with the duration of infection. The transcriptome results showed that 859 lncRNAs and 1895 mRNAs were differentially expressed compared with those in the control group, and several lncRNAs were highly expressed in the middle–late stages of mouse infection. GO and KEGG pathway analyses revealed that the differentially expressed target genes were enriched mainly in immune system processes and inflammatory response, among others, and several potential regulatory networks were constructed.ConclusionsThis study revealed the expression profiles of lncRNAs in the brains of mice after infection with A. cantonensis. The lncRNAs H19, F630028O10Rik, Lockd, AI662270, AU020206, and Mexis were shown to play important roles in the infection of mice with A. cantonensis infection.Graphical

In the heat of connection: using infrared thermal imaging to shed new light into early parent-infant co-regulation patterns.

In the heat of connection: using infrared thermal imaging to shed new light into early parent-infant co-regulation patterns.

Abstract 1472: APOBEC-induced non-coding hotspot mutations near LEPROTL1 contribute to the tumorigenicity of bladder cancer via deregulation of LEPROTL1, DCTN6 and SARAF

Abstract In bladder cancer, APOBEC3 deaminases are major drivers of genomic mutagenesis, with most of these mutations occurring in synonymous or non-coding regions of the genome. The impact of these mutations on tumorigenesis has not been explored. This study aimed to investigate two non-coding hotspot mutations, likely induced by APOBEC3A near LEPROTL1—chr8: 29,952,919 G>A and chr8: 29,952,921 C>T—in bladder cancer. Using dual-luciferase reporter assays, we found that these mutations enhance both promoter and enhancer activities. Employing multi-omics approaches, including RNA-seq, ChiP-seq, and Hi-C, identified LEPROTL1, DCTN6, and SARAF as target genes co-regulated by a common enhancer where these hotspot non-coding mutations manifest. Isogenic cell lines, generated through CRISPR-mediated base editing to introduce these mutations, exhibited upregulation of target genes, and displayed oncogenic characteristics, such as increased cell growth, proliferation, invasiveness, and resistance to cisplatin. Moreover, these mutations exert their effect on cell proliferation by promoting cell cycle progression. These results suggest that recurrent non-coding somatic mutations upstream of LEPROTL1 disrupt the coregulation pattern of the three genes, thereby promoting bladder tumorigenesis. Citation Format: Bilal Lone, Kelly Butler, Alexandra Dobbins, Dhanusha Yesudhas, Arup Chakraborty, Evan Hickman, Salah Boudjadi, Andrea Apolo, Rouf Banday. APOBEC-induced non-coding hotspot mutations near LEPROTL1 contribute to the tumorigenicity of bladder cancer via deregulation of LEPROTL1, DCTN6 and SARAF [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1472.

REPRESENTATION LEARNING OF PROTEOME DYNAMICS TO CHARACTERIZE ORGANISMIC COMMUNICATION AND INTRINSIC HEALTH

Abstract Co-regulation and interactions among organ systems are crucial to maintain health. Therefore, understanding patterns of such organismic communication using proteome dynamics could be an innovative and effective strategy for quantifying intrinsic human health. In this preliminary study, we conducted a case-control study involving six participants with severe genetic mitochondrial disease and six age- and sex-matched healthy subjects. We measured and recorded the salivary proteome of each participant (n=2922 proteins) at 0-, 30-, and 45-minutes post-awakening as well as bedtime (pm), over two days, representing a natural physiological challenge known to involve systems-wide physiological recalibrations. We then analyzed the changes in protein levels across successive time points using a high-dimensional two-sample testing approach (Cai, Liu, and Xia 2013). The protein co-regulation pattern (captured by the covariance matrix of their changes in expression) was significantly different between the healthy and mitochondrial disease participants (p=0.01), thereby linking proteome dynamics with health status. We then implemented Weighted Correlation Network Analysis to identify representative features showing a significant correlation with the subject’s health status between pairs of successive time points (0-30, 30-45, 45-pm). The correlation between the extracted feature and the self-rated health was as high as 0.39 (p-value=0.03) for the 30-45 time period. This finding reveals an association between health status and salivary protein co-regulation patterns, and suggests a promising representation learning strategy to quantify the manifestation of health into accessible proteome dynamics.

Discovering Dynamic Plant Enzyme Complexes in Yeast for Kratom Alkaloid Pathway Identification.

Discovering natural product biosynthetic pathways of medicinal plants is challenging and laborious. Capturing the coregulation patterns of pathway enzymes, particularly transcriptomic regulation, has proven an effective method to accelerate pathway identification. In this study, we developed a yeast-based screening method to capture the protein-protein interactions (PPI) between plant enzymes, which is another useful pattern to complement the prevalent approach. Combining this method with plant multiomics analysis, we discovered four enzyme complexes and their organized pathways from kratom, an alkaloid-producing plant. The four pathway branches involved six enzymes, including a strictosidine synthase, a strictosidine β-D-glucosidase (MsSGD), and four medium-chain dehydrogenase/reductases (MsMDRs). PPI screening selected six MsMDRs interacting with MsSGD from 20 candidates predicted by multiomics analysis. Four of the six MsMDRs were then characterized as functional, indicating the high selectivity of the PPI screening method. This study highlights the opportunity of leveraging post-translational regulation features to discover novel plant natural product biosynthetic pathways.

Discovering Dynamic Plant Enzyme Complexes in Yeast for Kratom Alkaloid Pathway Identification**

AbstractDiscovering natural product biosynthetic pathways of medicinal plants is challenging and laborious. Capturing the coregulation patterns of pathway enzymes, particularly transcriptomic regulation, has proven an effective method to accelerate pathway identification. In this study, we developed a yeast‐based screening method to capture the protein‐protein interactions (PPI) between plant enzymes, which is another useful pattern to complement the prevalent approach. Combining this method with plant multiomics analysis, we discovered four enzyme complexes and their organized pathways from kratom, an alkaloid‐producing plant. The four pathway branches involved six enzymes, including a strictosidine synthase, a strictosidine β‐D‐glucosidase (MsSGD), and four medium‐chain dehydrogenase/reductases (MsMDRs). PPI screening selected six MsMDRs interacting with MsSGD from 20 candidates predicted by multiomics analysis. Four of the six MsMDRs were then characterized as functional, indicating the high selectivity of the PPI screening method. This study highlights the opportunity of leveraging post‐translational regulation features to discover novel plant natural product biosynthetic pathways.

Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine.

The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial-mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics.

Physiological coregulation during social support discussions.

Close relationships are proposed to function as dynamic regulatory systems, whereby partners jointly regulate each other's emotions and physiology to maintain an equilibrium level of responding--a process known as coregulation. Little is known, however, regarding when coregulation emerges. We hypothesized that because social support interactions involve explicit interpersonal emotion regulation attempts, they might be especially likely to engender stabilizing patterns of coregulation that entrain partners' responses toward an equilibrium level. We conducted a dyadic laboratory experiment in which romantic couples engaged in social support and control discussions as cardiovascular responses were measured. To assess dyadic coregulation, we used dynamical systems modeling with Bayesian estimation to capture the frequency of oscillations around an equilibrium level and changes in amplitudes over time. Results indicated there was coregulation across discussions as a whole, as well as differences in coregulation by discussion type and gender. Stabilizing coregulatory dynamics-indicated by patterns of oscillations around an equilibrium level and decreases in amplitudes as the discussions progressed-were more pronounced during social support (vs. control) discussions, especially when the male partner received support. There was also substantial between-dyad heterogeneity in couples' coregulation trajectories, whereby some couples showed pronounced coregulatory dynamics whereas other did not. Overall, this work suggests that social support interactions may be a key context when coregulation emerges and thereby offers novel insights into how relationships might contribute to well-being. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Evidence for cultural differences in affect during mother–infant interactions

Maternal care is considered a universal and even cross-species set of typical behaviors, which are necessary to determine the social development of children. In humans, most research on mother–infant bonding is based on Western cultures and conducted in European and American countries. Thus, it is still unknown which aspects of mother–infant behaviors are universal and which vary with culture. Here we test whether typical mother–infant behaviors of affect-communication and affect-regulation are equally represented during spontaneous interaction in Palestinian-Arab and Jewish cultures. 30 Palestinian-Arab and 43 Jewish mother–infant dyads were recruited and videotaped. Using AffectRegulation Coding System (ARCS), we behaviorally analyzed the second-by-second display of valence and arousal in each participant and calculated the dynamic patterns of affect co-regulation. The results show that Palestinian-Arab infants express more positive valence than Jewish infants and that Palestinian-Arab mothers express higher arousal compared to Jewish mothers. Moreover, we found culturally-distinct strategies to regulate the infant: increased arousal in Palestinian-Arab dyads and increased mutual affective match in Jewish dyads. Such cross-cultural differences in affect indicate that basic features of emotion that are often considered universal are differentially represented in different cultures. Affect communication and regulation patterns can be transmitted across generations in early-life socialization with caregivers.

Towards a structurally resolved human protein interaction network

Cellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology.

Uncovering common pathobiological processes between COVID-19 and pulmonary arterial hypertension by integrating Omics data.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to the current pandemic. Many factors, including age and comorbidities, influence the severity and mortality of COVID-19. SARS-CoV-2 infection can cause pulmonary vascular dysfunction. The COVID-19 case-fatality rate in patients with pulmonary arterial hypertension (PAH) is higher in comparison with the general population. In this study, we aimed to identify pathobiological processes common to COVID-19 and PAH by utilizing the human protein-protein interactome and whole-genome transcription data from peripheral blood mononuclear cells (PBMCs) and from lung tissue. We found that there are significantly more interactions between SARS-CoV-2 targets and PAH disease proteins than expected by chance, suggesting that the PAH disease module is in the neighborhood of SARS-CoV-2 targets in the human interactome. In addition, SARS-CoV-2 infection-induced changes in gene expression significantly overlap with PAH-induced gene expression changes in both tissues, indicating SARS-CoV-2 and PAH may share common transcriptional regulators. We identified many upregulated genes and downregulated genes common to COVID-19 and PAH. Interestingly, we observed different co-regulation patterns and dysfunctional signaling pathways in PBMCs versus lung tissue. Endophenotype enrichment analysis revealed that genes regulating fibrosis, inflammation, hypoxia, oxidative stress, immune response, and thromboembolism are significantly enriched in the COVID-19-PAH co-expression modules. We examined the network proximity of the targets of repositioned drugs for COVID-19 to the co-expression modules in PBMCs and lung tissue, and identified 42 drugs that can be potentially used for COVID-19 patients with PAH as a comorbidity. The uncovered common pathobiological pathways arecrucial for discovering therapeutic targets and designing tailored treatments for COVID-19 patients who also have PAH.

Altered Serum Bile Acid Profile Associated with Chronic Allograft Dysfunction in Kidney Transplant Recipients.

BACKGROUND Chronic allograft dysfunction (CAD) is the leading cause of graft loss among kidney transplant recipients (KTRs). Bile acids (BAs) play an important role in regulating inflammatory process, which is the major contributor to the development of CAD. The aim of this study was to evaluate the association between BAs metabolic dysregulation and CAD in KTRs. MATERIAL AND METHODS Fifteen serum BA species were determined in 43 healthy controls (HCs) and 131 KTRs by UPLC-MS/MS. KTRs were grouped into stable renal function (STA) and CAD1 and CAD2 groups based on eGFR levels. Circulating CYP7A1, CYP7B1, CYP27A1, and SLCO2B1 mRNA levels were determined by RT-PCR. RESULTS Total BA concentrations were comparable among the 4 groups. However, KTRs showed significantly different BAs profiling compared to HCs. KTRs with severe CAD (CAD2) had significantly lower unconjugated BAs and secondary BAs (SBAs) compared to the other 3 groups. KTRs had significantly lower SBAs/primary BAs (PBAs) ratios than HCs, which were comparable among the 3 KTR groups. Conjugated/unconjugated BAs ratios increased significantly with the deterioration of allograft function, which was further confirmed by correlation analysis. Differential correlation network analysis revealed that perturbations in intraclass and interclass BA coregulation existed during CAD progression. Moreover, relative gene expressions of CYP7B1 and CYP27A1 were positively correlated with eGFR. CONCLUSIONS BA species profiling, but not total BA concentrations, was significantly altered in KTRs with CAD. The shifts from unconjugated BAs toward conjugated BAs, SBAs toward PBAs, and distinct pairwise BAs coregulation patterns were the main characteristics of KTRs with CAD.