Single-cell analysis reveals lasting immunological consequences of influenza infection and respiratory immunization in the pig lung.

The pig is a natural host for influenza viruses and integrally involved in virus evolution through interspecies transmissions between humans and swine. Swine have many physiological, anatomical, and immunological similarities to humans, and are an excellent model for human influenza. Here, we employed single cell RNA-sequencing (scRNA-seq) and flow cytometry to characterize the major leukocyte subsets in bronchoalveolar lavage (BAL), twenty-one days after H1N1pdm09 infection or respiratory immunization with an adenoviral vector vaccine expressing hemagglutinin and nucleoprotein with or without IL-1ß. Mapping scRNA-seq clusters from BAL onto those previously described in peripheral blood facilitated annotation and highlighted differences between tissue resident and circulating immune cells. ScRNA-seq data and functional assays revealed lasting impacts of immune challenge on BAL populations. First, mucosal administration of IL-1ß reduced the number of functionally active Treg cells. Second, influenza infection upregulated IFI6 in BAL cells and decreased their susceptibility to virus replication in vitro. Our data provide a reference map of porcine BAL cells and reveal lasting immunological consequences of influenza infection and respiratory immunization in a highly relevant large animal model for respiratory virus infection.

A primer on correlation-based dimension reduction methods for multi-omics analysis.

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

Transcriptome analysis reveals tumor microenvironment changes in glioblastoma.

A better understanding of transcriptional evolution of IDH-wild-type glioblastoma may be crucial for treatment optimization. Here, we perform RNA sequencing (RNA-seq) (n = 322 test, n = 245 validation) on paired primary-recurrent glioblastoma resections of patients treated with the current standard of care. Transcriptional subtypes form an interconnected continuum in a two-dimensional space. Recurrent tumors show preferential mesenchymal progression. Over time, hallmark glioblastoma genes are not significantly altered. Instead, tumor purity decreases over time and is accompanied by co-increases in neuron and oligodendrocyte marker genes and, independently, tumor-associated macrophages. A decrease is observed in endothelial marker genes. These composition changes are confirmed by single-cell RNA-seq and immunohistochemistry. An extracellular matrix-associated gene set increases at recurrence and bulk, single-cell RNA, and immunohistochemistry indicate it is expressed mainly by pericytes. This signature is associated with significantly worse survival at recurrence. Our data demonstrate that glioblastomas evolve mainly by microenvironment (re-)organization rather than molecular evolution of tumor cells.

mSep: investigating physiological and immune-metabolic biomarkers in septic and healthy pregnant women to predict feto-maternal immune health - a prospective observational cohort study protocol.

INTRODUCTION: Maternal sepsis remains a leading cause of death in pregnancy. Physiological adaptations to pregnancy obscure early signs of sepsis and can result in delays in recognition and treatment. Identifying biomarkers that can reliably diagnose sepsis will reduce morbidity and mortality and antibiotic overuse. We have previously identified an immune-metabolic biomarker network comprising three pathways with a >99% accuracy for detecting bacterial neonatal sepsis. In this prospective study, we will describe physiological parameters and novel biomarkers in two cohorts-healthy pregnant women and pregnant women with suspected sepsis-with the aim of mapping pathophysiological drivers and evaluating predictive biomarkers for diagnosing maternal sepsis. METHODS AND ANALYSIS: Women aged over 18 with an ultrasound-confirmed pregnancy will be recruited to a pilot and two main study cohorts. The pilot will involve blood sample collection from 30 pregnant women undergoing an elective caesarean section. Cohort A will follow 100 healthy pregnant women throughout their pregnancy journey, with collection of blood samples from participants at routine time points in their pregnancy: week 12 'booking', week 28 and during labour. Cohort B will follow 100 pregnant women who present with suspected sepsis in pregnancy or labour and will have at least two blood samples taken during their care pathway. Study blood samples will be collected during routine clinical blood sampling. Detailed medical history and physiological parameters at the time of blood sampling will be recorded, along with the results of routine biochemical tests, including C reactive protein, lactate and white blood cell count. In addition, study blood samples will be processed and analysed for transcriptomic, lipidomic and metabolomic analyses and both qualitative and functional immunophenotyping. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Wales Research Ethics Committee 2 (SPON1752-19, 30 October 2019). TRIAL REGISTRATION NUMBER: NCT05023954.

Bayesian networks elucidate complex genomic landscapes in cancer.

Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they can be constructed from observed data and can provide a guiding, graphical tool in exploring such relations. Here we propose BNs for elucidating the relations between driver events in large cancer genomic datasets. We present a methodology that is specifically tailored to biologists and clinicians as they are the main producers of such datasets. We achieve this by using an optimal BN learning algorithm based on well established likelihood functions and by utilising just two tuning parameters, both of which are easy to set and have intuitive readings. To enhance value to clinicians, we introduce (a) the use of heatmaps for families in each network, and (b) visualising pairwise co-occurrence statistics on the network. For binary data, an optional step of fitting logic gates can be employed. We show how our methodology enhances pairwise testing and how biologists and clinicians can use BNs for discussing the main relations among driver events in large genomic cohorts. We demonstrate the utility of our methodology by applying it to 5 cancer datasets revealing complex genomic landscapes. Our networks identify central patterns in all datasets including a central 4-way mutual exclusivity between HDR, t(4,14), t(11,14) and t(14,16) in myeloma, and a 3-way mutual exclusivity of three major players: CALR, JAK2 and MPL, in myeloproliferative neoplasms. These analyses demonstrate that our methodology can play a central role in the study of large genomic cancer datasets.

mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies.

Mutational signatures have emerged as powerful biomarkers in cancer patients, with prognostic and therapeutic implications. Wider clinical utility requires access to reproducible algorithms, which allow characterization of mutational signatures in a given tumor sample. Here, we show how mutational signature fitting can be applied to hematological cancer genomes to identify biologically and clinically important mutational processes, highlighting the importance of careful interpretation in light of biological knowledge. Our newly released R package mmsig comes with a dynamic error-suppression procedure that improves specificity in important clinical and biological settings. In particular, mmsig allows accurate detection of mutational signatures with low abundance, such as those introduced by APOBEC cytidine deaminases. This is particularly important in the most recent mutational signature reference (COSMIC v3.1) where each signature is more clearly defined. Our mutational signature fitting algorithm mmsig is a robust tool that can be implemented immediately in the clinic.

Timing the initiation of multiple myeloma.

The evolution and progression of multiple myeloma and its precursors over time is poorly understood. Here, we investigate the landscape and timing of mutational processes shaping multiple myeloma evolution in a large cohort of 89 whole genomes and 973 exomes. We identify eight processes, including a mutational signature caused by exposure to melphalan. Reconstructing the chronological activity of each mutational signature, we estimate that the initial transformation of a germinal center B-cell usually occurred during the first 2nd-3rd decades of life. We define four main patterns of activation-induced deaminase (AID) and apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) mutagenesis over time, including a subset of patients with evidence of prolonged AID activity during the pre-malignant phase, indicating antigen-responsiveness and germinal center reentry. Our findings provide a framework to study the etiology of multiple myeloma and explore strategies for prevention and early detection.

Targeting of EGFR by a combination of antibodies mediates unconventional EGFR trafficking and degradation.

Antibody combinations targeting cell surface receptors are a new modality of cancer therapy. The trafficking and signalling mechanisms regulated by such therapeutics are not fully understood but could underlie differential tumour responses. We explored EGFR trafficking upon treatment with the antibody combination Sym004 which has shown promise clinically. Sym004 promoted EGFR endocytosis distinctly from EGF: it was asynchronous, not accompanied by canonical signalling events and involved EGFR clustering within detergent-insoluble plasma mebrane-associated tubules. Sym004 induced lysosomal degradation independently of EGFR ubiquitylation but dependent upon Hrs/Tsg101 that are required for the formation of intraluminal vesicles (ILVs) within late endosomes. We propose Sym004 cross-links EGFR physically triggering EGFR endocytosis and incorporation onto ILVs and so Sym004 sensitivity correlates with EGFR numbers available for binding, rather than specific signalling events. Consistently Sym004 efficacy and potentiation of cisplatin responses correlated with EGFR surface expression in head and neck cancer cells. These findings will have implications in understanding the mode of action of this new class of cancer therapeutics.

Revealing the impact of structural variants in multiple myeloma.

The landscape of structural variants (SVs) in multiple myeloma remains poorly understood. Here, we performed comprehensive analysis of SVs in a large cohort of 752 multiple myeloma patients by low coverage long-insert whole genome sequencing. We identified 68 SV hotspots involving 17 new candidate driver genes, including the therapeutic targets BCMA (TNFRSF17), SLAMF and MCL1. Catastrophic complex rearrangements termed chromothripsis were present in 24% of patients and independently associated with poor clinical outcomes. Templated insertions were the second most frequent complex event (19%), mostly involved in super-enhancer hijacking and activation of oncogenes such as CCND1 and MYC. Importantly, in 31% of patients two or more seemingly independent putative driver events were caused by a single structural event, demonstrating that the complex genomic landscape of multiple myeloma can be acquired through few key events during tumor evolutionary history. Overall, this study reveals the critical role of SVs in multiple myeloma pathogenesis.

Molecular Evolution of IDH Wild-Type Glioblastomas Treated With Standard of Care Affects Survival and Design of Precision Medicine Trials: A Report From the EORTC 1542 Study.

PURPOSE: Precision medicine trials in glioblastoma (GBM) are often conducted at tumor recurrence. However, second surgeries for recurrent GBM are not routinely performed, and therefore, molecular data for trial inclusion are predominantly derived from the primary sample. This study aims to establish whether molecular targets change during tumor progression and, if so, whether this affects precision medicine trial design. MATERIALS AND METHODS: We collected 186 pairs of primary-recurrent GBM samples from patients receiving chemoradiotherapy with temozolomide and sequenced approximately 300 cancer genes. MGMT, TERT, and EGFRvIII status was individually determined. RESULTS: The molecular profile of our cohort was identical to that of other GBM cohorts (IDH wild-type [WT], 95%; EGFR amplified, approximately 50%), indicating that patients amenable to second surgery do not represent a specific molecular subtype. Molecular events in IDH WT GBMs were stable in approximately 80% of events, but changes in mutation status were observed for all examined genes (range, approximately 90% and 60% for TERT and EGFR mutations, respectively), and such changes strongly affected targeted trial size and design. A similar pattern of GBM driver instability was observed within MGMT promoter-methylated tumors. MGMT promoter methylation status remained prognostic at tumor recurrence. The observation that hypermutation at GBM recurrence was rare (8%) and not correlated with outcome was relevant for immunotherapy-based treatments. CONCLUSION: This large cohort of matched primary and recurrent IDH WT tumors establishes the frequency of GBM driver instability after chemoradiotherapy with temozolomide. This allows per gene or pathway calculation of trial size at tumor recurrence, using molecular data of the primary tumor only. We also identify genes for which repeat surgery is necessary because of low mutation retention rate.

Genomic landscape and chronological reconstruction of driver events in multiple myeloma.

The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients.

Classification and Personalized Prognosis in Myeloproliferative Neoplasms.

BACKGROUND: Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment. METHODS: We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort. RESULTS: A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy. CONCLUSIONS: Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.).

Timing the Landmark Events in the Evolution of Clear Cell Renal Cell Cancer: TRACERx Renal.

Clear cell renal cell carcinoma (ccRCC) is characterized by near-universal loss of the short arm of chromosome 3, deleting several tumor suppressor genes. We analyzed whole genomes from 95 biopsies across 33 patients with clear cell renal cell carcinoma. We find hotspots of point mutations in the 5' UTR of TERT, targeting a MYC-MAX-MAD1 repressor associated with telomere lengthening. The most common structural abnormality generates simultaneous 3p loss and 5q gain (36% patients), typically through chromothripsis. This event occurs in childhood or adolescence, generally as the initiating event that precedes emergence of the tumor's most recent common ancestor by years to decades. Similar genomic changes drive inherited ccRCC. Modeling differences in age incidence between inherited and sporadic cancers suggests that the number of cells with 3p loss capable of initiating sporadic tumors is no more than a few hundred. Early development of ccRCC follows well-defined evolutionary trajectories, offering opportunity for early intervention.

Proteome-wide dataset supporting functional study of tyrosine kinases in breast cancer.

Tyrosine kinases (TKs) play an essential role in regulating various cellular activities and dysregulation of TK signaling contributes to oncogenesis. However, less than half of the TKs have been thoroughly studied. Through a combined use of RNAi and stable isotope labeling with amino acids in cell culture (SILAC)-based quantitative proteomics, a global functional proteomic landscape of TKs in breast cancer was recently revealed highlighting a comprehensive and highly integrated signaling network regulated by TKs (Stebbing et al., 2015) [1]. We collate the enormous amount of the proteomic data in an open access platform, providing a valuable resource for studying the function of TKs in cancer and benefiting the science community. Here we present a detailed description related to this study (Stebbing et al., 2015) [1] and the raw data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the identifier PXD002065.

ATG9A loss confers resistance to trastuzumab via c-Cbl mediated Her2 degradation.

Acquired or de novo resistance to trastuzumab remains a barrier to patient survival and mechanisms underlying this still remain unclear. Using stable isotope labelling by amino acids in cell culture (SILAC)-based quantitative proteomics to compare proteome profiles between trastuzumab sensitive/resistant cells, we identified autophagy related protein 9A (ATG9A) as a down-regulated protein in trastuzumab resistant cells (BT474-TR). Interestingly, ATG9A ectopic expression markedly decreased the proliferative ability of BT474-TR cells but not that of the parental line (BT474). This was accompanied by a reduction of Her2 protein levels and AKT phosphorylation (S473), as well as a decrease in Her2 stability, which was also observed in JIMT1 and MDA-453, naturally trastuzumab-resistant cells. In addition, ATG9A indirectly promoted c-Cbl recruitment to Her2 on T1112, a known c-Cbl docking site, leading to increased K63 Her2 polyubiquitination. Whereas silencing c-Cbl abrogated ATG9A repressive effects on Her2 and downstream PI3K/AKT signaling, its depletion restored BT474-TR proliferative rate. Taken together, our findings show for this first time that ATG9A loss in trastuzumab resistant cells allowed Her2 to escape from lysosomal targeted degradation through K63 poly-ubiquitination via c-Cbl. This study identifies ATG9A as a potentially druggable target to overcome resistance to anti-Her2 blockade.

LMTK3 Represses Tumor Suppressor-like Genes through Chromatin Remodeling in Breast Cancer.

LMTK3 is an oncogenic receptor tyrosine kinase (RTK) implicated in various types of cancer, including breast, lung, gastric, and colorectal cancer. It is localized in different cellular compartments, but its nuclear function has not been investigated so far. We mapped LMTK3 binding across the genome using ChIP-seq and found that LMTK3 binding events are correlated with repressive chromatin markers. We further identified KRAB-associated protein 1 (KAP1) as a binding partner of LMTK3. The LMTK3/KAP1 interaction is stabilized by PP1α, which suppresses KAP1 phosphorylation specifically at LMTK3-associated chromatin regions, inducing chromatin condensation and resulting in transcriptional repression of LMTK3-bound tumor suppressor-like genes. Furthermore, LMTK3 functions at distal regions in tethering the chromatin to the nuclear periphery, resulting in H3K9me3 modification and gene silencing. In summary, we propose a model where a scaffolding function of nuclear LMTK3 promotes cancer progression through chromatin remodeling.

Characterization of the Tyrosine Kinase-Regulated Proteome in Breast Cancer by Combined use of RNA interference (RNAi) and Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) Quantitative Proteomics.

Tyrosine kinases (TKs) are central regulators in cellular activities and perturbations of TK signaling contribute to oncogenesis. However, less than half of the TKs have been thoroughly studied and a global functional analysis of their proteomic portrait is lacking. Here we conducted a combined approach of RNA interference (RNAi) and stable isotope labeling with amino acids in cell culture (SILAC)-based quantitative proteomics to decode the TK-regulated proteome and associated signaling dynamics. As a result, a broad proteomic repertoire modulated by TKs was revealed, upon silencing of the 65 TKs expressed in MCF7 breast cancer cells. This yielded 10 new distinctive TK clusters according to similarity in TK-regulated proteome, each characterized by a unique signaling signature in contrast to previous classifications. We provide functional analyses and identify critical pathways for each cluster based on their common downstream targets. Analysis of different breast cancer subtypes showed distinct correlations of each cluster with clinical outcome. From the significantly up- and down-regulated proteins, we identified a number of markers of drug sensitivity and resistance. These data supports the role of TKs in regulating major aspects of cellular activity, but also reveals redundancy in signaling, explaining why kinase inhibitors alone often fail to achieve their clinical aims. The TK-SILACepedia provides a comprehensive resource for studying the global function of TKs in cancer.

Proteomic profile of KSR1-regulated signalling in response to genotoxic agents in breast cancer.

Kinase suppressor of Ras 1 (KSR1) has been implicated in tumorigenesis in multiple cancers, including skin, pancreatic and lung carcinomas. However, our recent study revealed a role of KSR1 as a tumour suppressor in breast cancer, the expression of which is potentially correlated with chemotherapy response. Here, we aimed to further elucidate the KSR1-regulated signalling in response to genotoxic agents in breast cancer. Stable isotope labelling by amino acids in cell culture (SILAC) coupled to high-resolution mass spectrometry (MS) was implemented to globally characterise cellular protein levels induced by KSR1 in the presence of doxorubicin or etoposide. The acquired proteomic signature was compared and GO-STRING analysis was subsequently performed to illustrate the activated functional signalling networks. Furthermore, the clinical associations of KSR1 with identified targets and their relevance in chemotherapy response were examined in breast cancer patients. We reveal a comprehensive repertoire of thousands of proteins identified in each dataset and compare the unique proteomic profiles as well as functional connections modulated by KSR1 after doxorubicin (Doxo-KSR1) or etoposide (Etop-KSR1) stimulus. From the up-regulated top hits, several proteins, including STAT1, ISG15 and TAP1 are also found to be positively associated with KSR1 expression in patient samples. Moreover, high KSR1 expression, as well as high abundance of these proteins, is correlated with better survival in breast cancer patients who underwent chemotherapy. In aggregate, our data exemplify a broad functional network conferred by KSR1 with genotoxic agents and highlight its implication in predicting chemotherapy response in breast cancer.

The vaginal microbiome during pregnancy and the postpartum period in a European population.

The composition and structure of the pregnancy vaginal microbiome may influence susceptibility to adverse pregnancy outcomes. Studies on the pregnant vaginal microbiome have largely been limited to Northern American populations. Using MiSeq sequencing of 16S rRNA gene amplicons, we characterised the vaginal microbiota of a mixed British cohort of women (n = 42) who experienced uncomplicated term delivery and who were sampled longitudinally throughout pregnancy (8-12, 20-22, 28-30 and 34-36 weeks gestation) and 6 weeks postpartum. We show that vaginal microbiome composition dramatically changes postpartum to become less Lactobacillus spp. dominant with increased alpha-diversity irrespective of the community structure during pregnancy and independent of ethnicity. While the pregnancy vaginal microbiome was characteristically dominated by Lactobacillus spp. and low alpha-diversity, unlike Northern American populations, a significant number of pregnant women this British population had a L. jensenii-dominated microbiome characterised by low alpha-diversity. L. jensenii was predominantly observed in women of Asian and Caucasian ethnicity whereas L. gasseri was absent in samples from Black women. This study reveals new insights into biogeographical and ethnic effects upon the pregnancy and postpartum vaginal microbiome and has important implications for future studies exploring relationships between the vaginal microbiome, host health and pregnancy outcomes.

Quantitative imaging of focal adhesion dynamics and their regulation by HGF and Rap1 signaling.

Cell migration is crucial in development, tissue repair and immunity and frequently aberrant in pathological processes including tumor metastasis. Focal adhesions (FAs) are integrin-based adhesion complexes that form the link between the cytoskeleton and the extracellular matrix and are thought to orchestrate cell migration. Understanding the regulation of FAs by (oncogenic) signaling pathways may identify strategies to target pathological cell migration. Here we describe the development of a robust FA tracker that enables the automatic, multi-parametric analysis of FA dynamics, morphology and composition from time-lapse image series generated by total internal reflection fluorescence (TIRF) microscopy. In control prostate carcinoma cells, this software recapitulates previous findings that relate morphological characteristics of FAs to their lifetime and their cellular location. We then investigated how FAs are altered when cell migration is induced by the metastasis-promoting hormone HGF and subsequently inhibited by activation of the small GTPase Rap1. We performed a detailed analysis of individual FA parameters, which identified FA size, sliding and intensity as primary targets of Rap1. HGF did not have strong effects on any of the FA parameters within the first hours of its addition. Subsequent Bayesian network inference (BNI), using all measured parameters as input, revealed little correlation between changes in cell migration and FA characteristics in this prostate carcinoma cell line. Instead BNI indicated a concerted coordination of cell size and FA parameters. Thus our results did not reveal a direct relation between the regulation of cell migration and the regulation of FA dynamics.