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Phytochemistry Reviews

, Volume 17, Issue 2, pp 291–326 | Cite as

BacHBerry: BACterial Hosts for production of Bioactive phenolics from bERRY fruits

  • Alexey Dudnik
  • A. Filipa Almeida
  • Ricardo Andrade
  • Barbara Avila
  • Pilar Bañados
  • Diane Barbay
  • Jean-Etienne Bassard
  • Mounir Benkoulouche
  • Michael Bott
  • Adelaide Braga
  • Dario Breitel
  • Rex Brennan
  • Laurent Bulteau
  • Celine Chanforan
  • Inês Costa
  • Rafael S. Costa
  • Mahdi Doostmohammadi
  • Nuno Faria
  • Chengyong Feng
  • Armando Fernandes
  • Patricia Ferreira
  • Roberto Ferro
  • Alexandre Foito
  • Sabine Freitag
  • Gonçalo Garcia
  • Paula Gaspar
  • Joana Godinho-Pereira
  • Björn Hamberger
  • András Hartmann
  • Harald Heider
  • Carolina Jardim
  • Alice Julien-Laferriere
  • Nicolai Kallscheuer
  • Wolfgang Kerbe
  • Oscar P. Kuipers
  • Shanshan Li
  • Nicola Love
  • Alberto Marchetti-Spaccamela
  • Jan Marienhagen
  • Cathie Martin
  • Arnaud Mary
  • Vincent Mazurek
  • Camillo Meinhart
  • David Méndez Sevillano
  • Regina Menezes
  • Michael Naesby
  • Morten H. H. Nørholm
  • Finn T. Okkels
  • Joana Oliveira
  • Marcel Ottens
  • Delphine Parrot
  • Lei Pei
  • Isabel Rocha
  • Rita Rosado-Ramos
  • Caroline Rousseau
  • Marie-France Sagot
  • Claudia Nunes dos Santos
  • Markus Schmidt
  • Tatiana Shelenga
  • Louise Shepherd
  • Ana Rita Silva
  • Marcelo Henriques da Silva
  • Olivier Simon
  • Steen Gustav Stahlhut
  • Ana Solopova
  • Artem Sorokin
  • Derek Stewart
  • Leen Stougie
  • Shang Su
  • Vera Thole
  • Olga Tikhonova
  • Martin Trick
  • Philippe Vain
  • André Veríssimo
  • Ana Vila-Santa
  • Susana Vinga
  • Michael Vogt
  • Liangsheng Wang
  • Lijin Wang
  • Wei Wei
  • Sandra Youssef
  • Ana Rute Neves
  • Jochen Forster
Article

Abstract

BACterial Hosts for production of Bioactive phenolics from bERRY fruits (BacHBerry) was a 3-year project funded by the Seventh Framework Programme (FP7) of the European Union that ran between November 2013 and October 2016. The overall aim of the project was to establish a sustainable and economically-feasible strategy for the production of novel high-value phenolic compounds isolated from berry fruits using bacterial platforms. The project aimed at covering all stages of the discovery and pre-commercialization process, including berry collection, screening and characterization of their bioactive components, identification and functional characterization of the corresponding biosynthetic pathways, and construction of Gram-positive bacterial cell factories producing phenolic compounds. Further activities included optimization of polyphenol extraction methods from bacterial cultures, scale-up of production by fermentation up to pilot scale, as well as societal and economic analyses of the processes. This review article summarizes some of the key findings obtained throughout the duration of the project.

Keywords

Berries Bioprospecting Microbial cell factories Polyphenols Sustainable production 

Abbreviations

4CL

4-coumaryl-CoA ligase

AD

Alzheimer’s disease

ANS

Anthocyanidin synthase

ALS

Amyotrophic lateral sclerosis; anthocyanidin synthase

ANR

Anthocyanidin reductase

C3′H

p-coumaroyl shikimate/quinate 3′-hydroxylase

C4H

Cinnamate 4-hydroxylase

CBD

Convention on biological diversity

CaN

Calcineurin

CDRE

Calcineurin-dependent responsive element

CHI

Chalcone isomerase

CHR

Chalcone reductase

CHS

Chalcone synthase

CPR

Cytochrome-P450 reductase

CVs

Column volumes

CYP

Cytochromes P450

DFR

Dihydroflavonol 4-reductase

F3H

Flavanone 3-hydroxylase

F3′H

Flavonoid 3′-hydroxylase

F3′5′H

Flavonoid 3′-5′-hydroxylase

FLS

Flavonol synthase

FNS

Flavone synthase

FP7

European Commission’s the Seventh Framework programme

GFP

Green fluorescent protein

GRAS

Generally regarded as safe

HCT

Hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyltransferase

HD

Huntington’s disease

HPLC

High-performance liquid chromatography

LAR

Leucoanthocyanidin reductase

LC-MS

Liquid-chromatography mass-spectrometry

NDs

Neurodegenerative diseases

NFAT

Nuclear Factor of Activated T-cells

NFκB

Nuclear Factor κB

NP-ABS

The Nagoya protocol on access to genetic resources and the fair and equitable sharing of benefits arising from their utilization

OMT

O-methyltransferase

PAL

Phenylalanine ammonia-lyase

PCA

Principal component analysis

PD

Parkinson’s disease

PLS

Partial least squares

RNA-seq

RNA sequencing technology

STS

Stilbene synthase

SVR

Support vector regression

TAL

Tyrosine ammonia-lyase

UFGT

Flavonoid 3-O-glucosyltransferase

Introduction

BacHBerry (BACterial Hosts for production of Bioactive phenolics from bERRY fruits, www.bachberry.eu) has received funding from the European Commission’s Seventh Framework Programme (FP7) under the theme KBBE.2013.3.1-01: “Plant High Value Productsfrom discovery to final product” with the goal of bringing together experts from the fields of plant biology, industrial biotechnology, analytical chemistry, and social sciences in order to assemble and validate a complete pipeline for discovery and microbial production of novel phenolic compounds of plant origin. The consortium consisted of twelve academic groups, five small and medium sized enterprises and one large enterprise from eleven different countries, namely Austria, Chile, China, Denmark, France, Germany, The Netherlands, Portugal, Russia, Switzerland and the United Kingdom. The diversity of partners has allowed access to a variety of expertise and to plant material that had not been characterized extensively.

Polyphenols as a source of bioactive molecules

Historically, plants have been not only important sources of nutrients and energy in the human diet but also have provided the fundamentals of traditional and modern medicine. The Plantae kingdom has been estimated to produce more than 200,000 different chemical compounds (Weckwerth 2003), including compounds with proven and potential medical applications. This is reflected in the high number of pharmaceutical products based on, or derived from, plant natural products, such as aspirin, morphine, quinine, paclitaxel and artemisinin (Cragg and Newman 2013). Among the different classes of specialized (i.e. secondary) metabolites, phenylpropanoids stand out due to their diversity and ubiquity in the plant kingdom. Fleshy fruits, a rich source of bioactive components, such as ascorbate (vitamin C), terpenoids and polyphenols, are considered to have one the highest antioxidant capacity of commonly eaten foods (Halvorsen et al. 2006). Polyphenol content and composition can vary substantially between fleshy-fruit varieties (Carvalho et al. 2013; Miladinović et al. 2014; Kula et al. 2016), different cultivation conditions (Josuttis et al. 2013; Vagiri et al. 2013; Mazur et al. 2014), harvest times and as a result of different post-harvest factors (Kårlund et al. 2014; Stavang et al. 2015), but particularly between species (Moyer et al. 2001; McDougall et al. 2008; Ruiz et al. 2010; Marhuenda et al. 2016). Furthermore, plant polyphenols are increasingly being associated with putative bioactivities offering protection against several cardiovascular (Goszcz et al. 2015) and neurological diseases (Figueira et al. 2017).

The hydroxystilbenes are a group of polyphenols with a C6–C2–C6 skeleton with two or more hydroxyl groups, where a central carbon–carbon double bond is conjugated with two phenolic moieties (Quideau et al. 2011; Kasiotis et al. 2013). The most well-known representative of this group is resveratrol (trans-3,5,4′-hydroxystilbene; Fig. 1a). The compound has gained increasing attention with the discovery of the “French paradox”, an observation that despite a diet with relatively high amounts of saturated fats, French people suffer a relatively low incidence of mortality from coronary heart disease (Renaud and de Lorgeril 1992; Catalgol et al. 2012). Like other polyphenols, stilbenes can be further decorated by O-methylation (e.g. resulting in 3-methoxy-resveratrol, or pinostilbene, and 3,5′-dimethoxy-resveratrol, or pterostilbene), acylation (hydrangenic acid) or by glycosylation (e.g. piceid and resveratroloside; Kim et al. 2002; Becker et al. 2003; Zhang et al. 2009; Wang et al. 2015).
Fig. 1

a The molecular structure of the stilbene trans-resveratrol. b Basic flavonoid structure. Rx are positions where decorating groups (e.g. hydroxyl, acyl, glycosyl, etc.), which differentiate the compounds and alter their chemical properties, could be attached

The flavonoids are one of the largest families of phenolic compounds. Flavonoids are characterized by their basic skeleton composed of three rings (Ghosh and Scheepens 2009), including two benzene rings (A & B) and one heterocyclic ring (C; Fig. 1b). So far, over 5000 naturally occurring flavonoids have been characterized from various plants and they have been classified into six subgroups, including anthocyanidins (e.g. cyanidin and delphinidin); flavan-3-ols (e.g. catechin and epicatechin); flavonols (e.g. quercetin and kaempferol); flavones (e.g. luteonin and apigenin); isoflavones (e.g. genistein and daidzein), and flavanones (e.g. hesperetin and naringenin).

Within each subgroup, considerable variation can exist when it comes to phenolic composition of different fruits, and in particular of berries. Anthocyanin composition provides an excellent example of this diversity: the red–orange color of strawberries is due to the presence of pelargonidin-type anthocyanins in the flesh and skin, whereas the deep purple-black color of blackcurrants is due to the accumulation of high amounts of delphinidin and cyanidin-type anthocyanins in the skin.

Anthocyanidins are flavonoids, which are characterized by a hydroxyl group in position 3 and a C-ring with a positively-charged oxonium ion (Fig. 1b). Anthocyanins are water-soluble glycosides of anthocyanidins, in which sugars, consisting of one or more hexoses, are linked to the 3-hydroxyl group of the pyrylium ring (Heldt and Piechulla 2011). These compounds are responsible for the orange/red-to-blue coloration observed in some plants. The most common anthocyanidins include cyanidin (red), delphinidin (bluish-purple), malvinidin (reddish-purple), pelargonidin (orange-red), peonidin (pink) and petunidin (purple). The distribution of anthocyanidins can vary greatly among different berry species (Table 1).
Table 1

Anthocyanidin structures (aglycone form) found in different berry species

Type

Pelargonidin

Cyanidin

Delphinidin

Peonidin

Petunidin

Malvinidin

Structure

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Molecular formula

C15H11O5 +

C15H11O6 +

C15H11O7 +

C16H13O6 +

C16H13O7 +

C17H15O7 +

Fragaria a

+

+

Ribes a

+

+

+

+

Rubua a

+

+

Vaccinium a

+

+

+

+

+

Vitis a

+

+

+

+

+

Lonicera b,e

+

+

+

+

+

Lycium c

+

+

+

Berberis d

+

+

+

+

+

Ugni d

+

+

Aristotelia f

+

+

The presence of a compound is noted with + whereas the absence is noted with

aPhenol explorer database (http://phenol-explorer.eu), b (Chaovanalikit et al. 2004), c (Zheng et al. 2011), d (Ruiz et al. 2010), e (Wang et al. 2016b), f (Fredes et al. 2014)

Flavonoid compounds frequently are decorated with sugar moieties, sugar-acyl moieties (Giusti and Wrolstad 2003) and can be associated with other flavonoids. The patterns of decoration differ greatly amongst species (see supplementary Tables S1 and S2).

Flavonoids are far more prevalent and diverse in berry species than in other common fruits and vegetables. High flavonol contents are observed in cranberry, lingonberry and blackcurrant (Häkkinen et al. 1999), anthocyanins are the most abundant polyphenol pigments (2–5 g kg−1 fresh weight) in berries (Määttä et al. 2001), and many simple phenolic acids are abundant in a wide range of berry species (Herrmann and Nagel 1989). Additionally, berries constitute one the most important dietary sources of ellagitannins, such as sanguiin H6 and lambertianin C (Törrönen 2009; Landete 2011), as well as condensed tannins, such as the proanthocyanidins (Rasmussen et al. 2005; Hellström et al. 2009). Stilbenes, in contrast, are not that widespread in berries: resveratrol shows highest abundance in grapes (up to 20–30 mg kg−1 fresh weight), but small amounts of resveratrol, pterostilbene and piceatannol have been also detected in blueberry, cowberry, cranberry, deerberry and lingonberry (Jeandet et al. 1991; Paredes-López et al. 2010; Manganaris et al. 2014). It is clear that soft-fruit species contain a staggering diversity of distinct polyphenol derivatives whose potential is yet to be harnessed.

The market for polyphenolic compounds has seen substantial growth over the past few years, and is expected to exceed 850 million USD by 2018 (Aranaz et al. 2016). The major factors contributing to this trend are the growing consumer awareness regarding the benefits of polyphenol consumption and the increasing use of polyphenol-containing extracts in food, beverage, and cosmetics products particularly in Asia (Jain and Ramawat 2013; Aranaz et al. 2016; Grand View Research Inc. 2016). The increase in demand also requires efficient and eco-friendly production processes, to improve on current manufacturing practices that mostly rely on extraction from various plant sources (e.g. roots, leaves or fruits) via complex downstream processing (Wang et al. 2016a). The BacHBerry consortium decided to address these challenges and set the following objectives: (1) to systematically analyze the phenolic contents in the large berry germplasm collections available from consortium members, (2) to establish a publicly-available database of transcriptomic and metabolic data obtained from berry bioprospecting within, as well as outside of the project, (3) to discover novel bioactivities in berry extracts against a range of human pathologies, such as Alzheimer’s disease and Amyotrophic Lateral Sclerosis (ALS), by high-throughput screening with subsequent identification of functional biomolecules, (4) to identify the corresponding biosynthetic genes and perform functional characterization of the respective gene products, (5) to assay a selection of the biosynthetic genes for functionality in Gram-positive bacterial hosts and use those to construct bacterial cell factories for phenolic production, (6) to improve the production efficiency further by introducing modifications to the host metabolic networks predicted via rational design or computational tools developed within the project, (7) to design and optimize cost-effective food-grade methods for extraction of phenolic compounds from bacterial fermentation broth, and (8) to optimize fermentation conditions and subsequently upscale the production to pilot plant levels.

The project commenced with the selection, sampling and whole-metabolite profiling of berries. In order to explore the potential of the phytochemical diversity present in different berry species, we undertook an untargeted characterization of a wide collection of berries from different species/cultivars utilizing metabolomics-based methods to aid the selection of candidate berry extracts for bioactivity screening.

Phytochemical diversity in sampled berries

Although significant advances have been made in characterizing the polyphenolic complement of berries, particularly in the context of cultivated species and genotypes, there is limited literature available regarding the phytochemical composition of wild and underutilized species/cultivars. Indeed, wild berries are commonly reported as particularly rich in diverse phenolic compounds, often as a result of phenolic-associated astringency having been bred out of cultivated species (Häkkinen et al. 1999). The phenolic diversity of individual berries has been highlighted in many publications (Määttä et al. 2003; Zadernowski et al. 2005; Milivojević et al. 2011), but studies that capture this broad diversity systematically are limited. In BacHBerry, we aimed not only to address this knowledge gap but also to go beyond the state-of-the-art by combining analyses of phenolic diversity with functional testing of berry extracts. The genera targeted in this study included Rubus, Ribes, Vaccinium, Lonicera, Lycium, Aristotelia, Berberis and Ugni collected from different locations in the world including Chile, China, Portugal, Russia and United Kingdom (see Supplementary Table S3).

Berry extract metabolite profiling

Fruit samples from a total of 112 species/genotypes were collected, extracted, and subsequently subjected to liquid-chromatography mass-spectrometry (LC–MS) metabolomic analysis (see details in Supplementary Materials S1). An untargeted method was used to generate a total of 1890 mass spectral features (1506 and 384 in positive and negative modes, respectively), which were integrated to generate the dataset used for statistical analysis. Principal component analysis (PCA) was used as a multivariate statistical analysis tool to identify those species, which had the most distinct phytochemical profiles (Fig. 2a, b).
Fig. 2

Principal component analysis (PCA) plots of the full dataset without reference material and blanks. Each dot represents the profile of one sample aliquot whereas lines delimit groups of samples that cluster together. a PCA plots of the full dataset without reference material and blanks in positive mode. Components (Scr) 1 and 2 explain 9.79 and 7.59% of the variation respectively. b PCA plots of the full dataset without reference material and blanks in negative mode. Components (Scr) 1 and 2 explain 13.20 and 10.30% of the variation respectively

Using the four principal components it was possible to select the species/genotypes, which represented the broadest phenolic diversity (Table 2). In addition to selecting the outlier groups of species, it was also decided to include a small subset of samples (two Rubus idaeus genotypes), which did not separate from the majority of samples in the first principal components. This provided samples with phenolic profiles distinct from the outlier samples and standards for comparison between uncultivated/underutilized species and commonly grown species; in essence a commercial control. The fruits from the selected species (Table 2) were extracted and tested for bioactivity in baker’s yeast (Saccharomyces cerevisiae) models for pathological processes associated with several chronic disorders.
Table 2

Selected species for bioactivity testing according to the phytochemical diversity

Species

Variety/line

Species

Variety/line

Aristotelia chilensis (Molina)Stuntz

Rubus armeniacus Focke

Himalayan Giant

Berberis buxfolia Lam.

Rubus occidentalis L.

Huron

Lonicera caerulea L.

S322-23

Rubus loganobaccus L.H.Bailey

Sunberry

Lycium chinense Mill.

Rubus loganobaccus L.H.Bailey

Tayberry

Ribes grossularia L.

9294

Rubus bartonii Newton

Ashton Cross

Ribes grossularoides Maxim.

H1-12-1

Rubus fruticosus L. agg.

Fantasia

Ribes nevadense Kellog

Rubus brigantinus Samp.

Ribes spp.

644217

Rubus genevierii Boreau.

Ribes spp.

1126

Rubus henriquesii Samp.

Ribes spp.

2292-1

Rubus hochstetterorum Seub.

Ribes spp.

Muravushka

Rubus sampaioanus Sudre ex Samp.

Ribes spp.

Rubus vagabundus Samp.

Ribes petraeum Wulf.

R2-1-2

Rubus idaeus L.

Octavia

Ribes x saundersii Jancz.

XB4

Rubus idaeus L.

Prestige

Ribes bethmontii Jancz.

XW6

Ugni molinae Turcz.

Red Pearl

Ribes rubrum L.

S11-3-36

Vaccinium uliginosum L.

Rubus spp.

B14

Vaccinium vitis-idaea L.

Berry extract bioactivity screening

Evidence for the protective role of polyphenols against chronic diseases has increased over the past 20 years (Figueira et al. 2017; Goszcz et al. 2015). Neurodegenerative diseases (NDs) represent a group of chronic neurological conditions affecting millions of people worldwide, among which are the Alzheimer’s Disease (AD), the Parkinson’s Disease (PD), the Huntington’s Disease (HD), and Amyotrophic Lateral Sclerosis (ALS). All these diseases have in common the aggregation and deposition of protein aggregates, namely of amyloid β42 (Aβ42; O’Brien and Wong 2011), αSynuclenin (αSyn; Shults 2006), huntingtin (HTT; Miller-Fleming et al. 2008) and FUsed in Sarcoma (FUS; Kwiatkowski et al. 2009; Vance et al. 2009), which represent the pathological hallmarks of AD, PD, HD and ALS, respectively. In addition, chronic activation of innate immune responses is a process closely associated with neurodegeneration. Its modulation is driven by persistent activation of key transcription factors, such as the Nuclear Factor of Activated T-cells (NFAT) and Nuclear Factor κB (NFκB), which upregulate pro-inflammatory responses creating a positive feedback loop further amplifying initial stimuli. It has been argued that disruption of this loop may represent an important strategy to mitigate the progression of NDs. The pleiotropic effects of polyphenols have been shown to interfere with aggregation-driven neurodegeneration as well as to attenuate chronic inflammatory processes, thereby improving health of cellular and animal models (Figueira et al. 2017). Consequently, polyphenol-based therapies represent an underexplored strategy to minimize the huge social and economic impact of NDs.

The high degree of evolutionary conservation of fundamental biological processes among eukaryotes has established baker’s yeast as a validated model organism to decipher the intricacies of human pathologies, particularly NDs, to identify molecular targets amenable to therapeutic intervention as well as lead molecules with health-promoting potential (Kritzer et al. 2009; Su et al. 2010; Tardiff et al. 2012; Menezes et al. 2015). BacHBerry aimed at identifying phenolic bioactives from harnessing the diversity of phenolics in selected berry germplasm from cultivated, wild and underutilized species of berries. The yeast-based screening platform for bioactivity identification comprised a set of genetically–modified S. cerevisiae strains expressing Green Fluorescent Protein (GFP) fused with Aβ42 (Bharadwaj et al. 2010), αSyn (Outeiro and Lindquist 2003), HTT (Krobitsch and Lindquist 2000) and FUS (Ju et al. 2011) under the control of a galactose-inducible promoter (Fig. 3a). Upon activation of expression, these proteins start forming aggregates, which consequently has a negative impact on cellular growth and results in lower GFP fluorescence levels. In case an extract possesses bioactivity against one of the diseases, addition of the extract to the activated yeast cells reduces growth inhibition in the corresponding model. These yeast strains accelerated the identification of phenolic compounds with health-promoting attributes among the most chemically diverse samples identified by the metabolomic analysis (Table 2). For information on the procedures used during the screening see Supplementary Materials S1.
Fig. 3

Schematic representation of yeast models. a Yeast models of neurodegenerative diseases. Humanized yeast strains encoding chimeric fusions of disease genes with GFP (green fluorescent protein) under the control of a galactose-inducible (GAL1) promoter. Expression of Aβ42, αSyn (SNCA gene), FUS and HTTpQ103 leads to the formation of toxic aggregates, recapitulating the pathological processes of Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD), respectively. b Yeast model of Crz1 (NFAT orthologue) activation. Crz1 is activated by the serine/threonine protein phosphatase calcineurin (composed by CnA and CnB subunits). The reporter strain encodes the lacZ gene under the control of a promoter containing Crz1-binding sites, allowing the assessment of Crz1 activation through the measurement of β-galactosidase activity

An illustrative example of how bioactivities for AD were identified is given in Fig. 4a. Upon shift of cells to galactose medium to induce expression of GFP-Aβ42, yeast growth was impaired in comparison to the control strain indicating GFP-Aβ42 proteotoxicity. The treatment with polyphenol-enriched extracts of Lycium chinense significantly recovered growth of these cells revealing its protective role via modulation of Aβ42 toxicity. The bioactivities for PD, HD and ALS were screened using a similar approach, in cells expressing the respective disease proteins.
Fig. 4

Identification of bioactivities in the berry germplasm. a Strategy used for the discovery of bioactive compounds for Alzheimer’s disease. BY4741_erg6 recombinant yeast cells expressing GFP-Aβ42 were grown in SD galactose medium and cell viability was assessed by growth curves for 24 h in cells challenged or unchallenged with Lycium chinense polyphenol extracts. Cells containing the empty vector were used as controls. The areas under the curve (AUC) were integrated using the Origin software (OriginLab, Northampton, MA). Values represent the mean ± standard error of mean (SEM) of at least three biological replicates. b Strategy used for the discovery of bioactive compounds with anti-inflammatory potential. BY4741 yeast cells encoding CDREpromoter-lacZ were treated or not with Lycium chinense polyphenol extracts and induced with 1.8 mM MnCl2. Crz1 activation was assessed by monitoring β-galactosidase activity using ortho-nitrophenyl-β-galactoside (ONPG). The immunosuppressant FK506, a well-known inhibitor of calcineurin (Dumont 2000) was used as a positive control. The values represent the mean ± SEM of at least three biological replicates, *p < 0.05, **p < 0.01, ***p < 0.001

In addition, we have also used a yeast-based model for inflammatory signaling that is based on a Crz1 reporter-strain (Prescott et al. 2012; Garcia et al. 2016; Fig. 3b). Crz1 is a yeast orthologue of NFAT, which is an important modulator of inflammation in humans that is known to be involved in development of multiple disorders, such as the inflammatory bowel disease or the rheumatoid arthritis (Pan et al. 2013). Both Crz1 and NFAT are known to be activated by a serine/threonine protein phosphatase calcineurin (CaN) in a Ca2+-dependent manner (Rusnak and Mertz 2000; Bodvard et al. 2013). The utilized reporter strain encodes the β-galactosidase gene (lacZ) under the control of a promoter bearing Crz1-binding sites, the Calcineurin-Dependent Responsive Element (CDRE), allowing the assessment of Crz1 activation through the measurement of β-galactosidase activity using chromogenic substrates (Garcia et al. 2016). Given the evolutionary conservation of NFAT and Crz1 activation mechanisms, in combination with the conserved activity of the immunosuppressant FK506 both in yeast and in humans, the yeast Ca2+/CaN/Crz1 reporter assay represents a reliable tool to identify bioactives with potential to attenuate NFAT-mediated inflammatory responses. The potential of Lycium chinense polyphenol-enriched extracts to attenuate inflammation is shown in Fig. 4b, exemplifying the approach used in BacHBerry to filter for potential anti-inflammatory activities. The activation of Crz1 by MnCl2, mimicking NFAT activation, led to high β-galactosidase activity and cell treatment with FK506 and Lycium chinense extract strongly reduced the β-galactosidase levels revealing its ability to modulate Crz1, and potentially NFAT, activation.

These examples illustrate the strategy used in the BacHBerry project to search for bioactive compounds interfering with pathological processes of NDs and inflammation. The yeast-based screening platform also included strains allowing the identification of metabolites with potential application for type II diabetes, hematological diseases and cancer (unpublished results).

Bioassay-guided fractionation compound discovery and candidate compound validation

This method of discovering new bioactive natural products ultimately depends on the availability of biological material and preparative- or semi-preparative-scale analytical methods with the capability of resolving mixtures of different classes of compounds typically present in berry extracts (Pauli et al. 2012). Semi-preparative chromatography was used to fractionate selected berry extracts with potential bioactive properties using a hybrid approach of bioassay-guided fractionation procedure (Yang et al. 2001; Weller 2012; Tayarani-Najaran et al. 2013) and screening of pure compounds (Watts et al. 2010). Bioassay-guided fractionation typically involves the following steps: assessment of bioactivity, extraction of the biological material with different solvents and testing of bioactivity. Once bioactivity of an extract has been validated, the extract gets subjected to an iterative process of sub-fractionation/bioactivity testing until pure bioactive natural products are obtained for structural characterization. This approach benefits from exclusion of extracts that do not have bioactivity. However, this procedure requires extensive use of biological material and expensive materials and may result in the isolation of already-known natural products (Duarte et al. 2012). Another disadvantage is that the approach is based on the assumption that bioactivity is conferred by a pure compound, although this method can also be used for identification of bioactivities conferred by a cumulative interactions of several polyphenols. Alternatively, pure-compound screening relies on an initial isolation and structural elucidation of the individual compounds present in the biological extract followed by bioassay screening. This strategy allows the researcher to focus solely on novel compounds, without re-discovering compounds with well-annotated bioactivity. However, it may also lead to the identification of novel compounds with no bioactivity (Duarte et al. 2012). The second method misses any synergistic interactions affecting bioactivities of berry phenolics.

The limited amount of biological material available restricted the number of iterations of fractionation and re-testing of fraction bioactivity possible for typical bioassay-guided fractionation approaches. As described previously, berry extracts typically comprise a relatively diverse and large pool of metabolites that surpass by far the throughput capability of the bioactivity assays used in this study. To overcome these challenges, a hybrid approach was adopted which consisted of several steps: (1) assessment of potential bioactivity present in extracts (as described above), (2) fractionation of bioactive extracts (see Supplementary Materials S1), (3) assessment of potential bioactivity present in fractions, (4) mass-spectrometry-based chemical characterization of bioactive fractions, (5) bioactivity testing of pure candidate compounds (Fig. 5). Although this approach shared some of the limitations of the other approaches, it did allow for the exclusion of non-bioactive biological extracts or fractions and focused on the identification novel compounds with potential bioactivities with limited requirement for biological material.
Fig. 5

Yeast as a discovery platform of therapeutic compounds. Iterative bio-guided fractionation of complex mixtures allows the discovery of small molecules improving cell growth, by rescue of protein aggregates toxicity, or inhibiting inflammatory processes. High-throughput formats aligned with chemical identification by LC–MS approaches accelerate the identification of candidate compounds

Following fractionation of the most promising berry extracts from the first round of screening using the yeast-based platform, isolated fractions were re-analyzed in order to detect bioactive fractions. Subsequently, metabolomic analyses were used for the identification of the individual compounds in each of the bioactive fractions. By utilizing this approach, a set of candidate compounds was defined and re-tested as pure compounds in the yeast-based platform, with further bioactivity validation in the mammalian cell models. Following the BacHBerry pipeline, this information then served as starting point for the engineering of the synthetic pathway for biosynthesis of one of the validated compounds in food-grade bacteria and its subsequent production in small-scale fermenters. The final step of the pipeline, namely re-confirmation of bioactivity of microbially-produced compounds extracted from fermentation broth in both yeast and mammalian cell models, is currently underway.

Identification of metabolic pathways and regulators involved in phenolic production

The next step within the BacHBerry pipeline involved the identification of the biosynthetic genes and pathways corresponding to the newly discovered bioactives, in order to establish production of these compounds in bacterial hosts. In plants, the biosynthesis of polyphenols occurs via the phenylpropanoid pathway, where the aromatic amino acid l-phenylalanine serves as a precursor. Depending on the enzyme combination, several compounds can be produced in bacteria, including cinnamic acid derivatives, lignin subunits, lignans, phenylpropenes and coumarins, all sharing a basic C6–C3 skeleton (Vogt 2010). Furthermore, the phenylpropanoid backbone can be extended with up to three acetyl-units derived from malonyl-CoA by chalcone synthase (CHS) or stilbene synthase (STS), polyketide synthases which generate various polyketides, such as flavonoids and stilbenes. The core pathways of phenolic biosynthesis are very well understood, with common enzymes, such as, phenylalanine ammonia lyase (PAL), cinnamate 4-hydroxylase (C4H), p-coumaroyl CoA ligase (4CL), hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyltransferase (HCT), p-coumaroyl shikimate/quinate 3′-hydroxylase (C3′H), stilbene synthase and chalcone synthase, found in many plant species (Fig. 6).
Fig. 6

Polyphenol biosynthetic pathway in plants (modified from Falcone Ferreyra et al. 2012). PAL phenylalanine ammonia-lyase, C4H cinnamate 4-hydroxylase, 4CL 4-coumaroyl-CoA ligase, CHS chalcone synthase, CHI chalcone isomerase, F3H flavanone 3-hydroxylase, F3′5′H flavonoid 3′-5′-hydroxylase, DFR dihydroflavonol 4-reductase, ANS anthocyanidin synthase, ANR anthocyanidin reductase, UFGT flavonoid 3-O-glucosyltransferase, LAR leucoanthocyanidin reductase, FNS flavone synthase, FLS flavonol synthase, STS stilbene synthase. P450 enzymes are highlighted in red

Phenolic chemodiversity in berries, as in other plants, derives from a variety of decorations to the backbone of phenolic structures that include hydroxylations, O-methylations, prenylations, aryl migrations, glycosylations, acylations and polymerizations. For example, anthocyanins are glycosylated on 3-OH positions of the C-ring (Fig. 1) and many harbor additional glycosyl groups (for example 5′-O-glycosylation and 3′-O-glycosylation), methoxy groups (petunidin and malvinidin), or hydroxyl groups [which differentiate pelargonidin, cyanidin and delphinidin (Fig. 6; Table 1)]. These decorations may affect the bioavailability, bioactivity and stability of polyphenolic compounds. It is therefore essential to develop a greater knowledge of the decorating enzymes specific to berry bioactives, such as those differentially decorating anthocyanins, flavonols, stilbenes, or condensed tannins. Many genes encoding enzymes involved in flavonol and anthocyanin decoration have already been characterized not only in planta, but also heterologously in microorganisms, such as Escherichia coli and S. cerevisiae (Tohge et al. 2005; Luo et al. 2007; Pandey et al. 2016; Wang et al. 2016a).

Berry bioactives identified within the project had special decorations, and those where the genes encoding the decorating activities had not been previously identified, required further research. In a recent work, the transcriptome of the commercial blueberry (Vaccinium corymbosum) was studied using RNA sequencing technology (RNA-seq) in order to discover putative genes related to antioxidant production using next-generation sequencing (NGS; Li et al. 2012). Similarly, we aimed to identify decorating enzymes and regulators of the biosynthetic pathways of berry bioactives by obtaining transcriptome sequences of target species at different stages of fruit development. This information was used to construct a database of transcriptome sequences for a broad range of berry species, especially those identified as target species containing the promising bioactive molecules. In addition, the overall production of polyphenols is regulated by a range of transcription factors and these were also investigated within the BacHBerry project, with the objective of identifying markers for breeding enhanced levels of phenolic bioactives using the diverse germplasm in selected berry species.

Firstly, a library of reference sequences consisting of plant gene sequences encoding enzymes of the core phenolic biosynthetic pathways, which was subsequently used for BLAST searches of berry transcriptomes, was compiled. This library included well-known and experimentally validated anthocyanin, flavonol, stilbene and catechin biosynthetic enzymes from a wide range of plant species. Next, 13 germplasms spanning eight genera, seven families and seven orders were selected for RNA-seq of berry transcriptomes. Sequencing of selected berries at different stages of maturation was also conducted. The data have provided significant resources not only for understanding phenolic biosynthesis in berries, but also for investigating commonalities in genes expressed in berries (Table 3).
Table 3

Transcriptome resources for berries developed on BacHBerry

Species

Plant material

Total number of raw reads

Aristotelia sp.

Ripe berries

397,707,372

Berberis sp.

Ripe berries

444,362,698

Corema sp.

Leaf

353,604,932

Lonicera sp.

Ripe berries

397,214,254

Ribes sp.1

Ripe berries

336,479,242

Ribes sp.2

Ripe berries

393,665,630

Rubus sp.1

Berries (3 stages)

1,040,224,680

Rubus sp.2

Berries (3 stages)

1,064,858,518

Rubus sp.3

Ripe berries

505,754,030

Rubus sp.4

Ripe berries

390,608,452

Ugni sp.

Ripe berries

405,024,920

Vaccinium sp.1

Ripe berries

373,159,882

Vaccinium sp.2

Ripe berries

375,778,718

Multi-layer comparisons of the metabolomics and transcriptomics data of developmental stages, and targeted, as well as, non-targeted searches of candidate transcripts were performed to elucidate genes involved in specific phenolics biosynthetic pathways. Analyses of expression of these genes from different developmental stages of selected berry species were performed with the expVIP tool (Borrill et al. 2016).

These data were used to search for genes encoding putative decorating enzymes based on the list of molecules of potential interest as bioactives and having special decorations in target berry species. Candidates were identified mostly by searching for homologues of well-characterized plant genes. This was carried out with the help of MassBlast, a software developed within the BacHBerry project (https://github.com/averissimo/mass-blast; Veríssimo et al. 2017). We annotated all major enzyme families, including cytochromes P450 (CYP), 2-oxoglutarate-dependent dioxygenases (2-OGDs), UDP-glycosyltransferases (UGTs), BAHD acyltransferases, and O-methyltransferases (OMTs), in all 13 selected berry germplasms. Average counts per transcriptome are presented in Table 4. We also detected candidate transcript(s) for 60–80% of the known biochemical steps of the phenylpropanoid pathway, representing between 109 and 180 candidates per transcriptome. Approximately 40% of these candidates identified from the transcriptomes were full-length sequences. Furthermore, biochemical activities of some of enzymes encoded by the candidate genes were tested.
Table 4

Statistics on five major enzyme families representing conserved biochemical steps of the phenylpropanoid pathway

Enzyme families

Candidates per transcriptome (min–max)

Conserved phenylpropanoid pathway enzymes

109–180

CYPs

100–177

UGTs

70–145

2-OGDs

32–88

O-methyltransferases

8–51

Acyltransferases

23–36

Candidates displayed more than 70% sequence coverage to reference query sequences. This work led to the creation of a large dataset of candidates used for pathway elucidation and reconstitution in bacterial hosts

We also looked for transcripts encoding regulators of phenolic biosynthesis. Since the R2R3-MYB gene family operates extensively in controlling phenylpropanoid metabolism in plants (Stracke et al. 2001; Hartmann et al. 2005), initial searches were focused on identifying MYB-genes that were strongly expressed in berries. However, members of the bHLH and bZIP families of transcription factors were also identified in the berry RNA-seq databases since they may also play roles in regulating accumulation of phenolic bioactives (Hartmann et al. 2005). These data could be particularly useful in breeding programs for selection of plants with improved bioactive contents. The presence of genes encoding putative transporters of phenolic compounds was also examined. Excretion of target compounds produced by bacterial hosts into the culture medium via such transporters would simplify extraction procedures, and may also be necessary in case of any toxic effects of these molecules in bacteria. One transporter is currently being characterized functionally.

Gram-positive bacteria as cell factories for the efficient production of berry high-value phenolics

While the search for novel bioactives was under way, a set of well-characterized biosynthetic pathway was used for establishing a platform for production of phenolic compounds in Gram-positive bacteria. Two major criteria were used for the selection of host organisms for this task: (1) they had to be robust, well-studied organisms with a long-standing history of industrial use and (2) the bacteria must have a generally regarded as safe (GRAS) status and/or be recognized as food-grade. Two industrially-important bacteria, namely Lactococcus lactis and Corynebacterium glutamicum, appeared as attractive candidates for the sustainable production of value-added food and pharmaceutical compounds (Lahtinen et al. 2011; Jojima et al. 2013). Both L. lactis and C. glutamicum are used worldwide in numerous industrial processes, which consequently led to significant amount of research work dedicated towards improving our knowledge of their physiology and genetics. These efforts have resulted in development of large toolboxes for (heterologous) gene expression and introduction of genetic modifications in these bacteria (Burkovski 2008; Pontes et al. 2011; Gaspar et al. 2013). Furthermore, high-throughput methodologies for transcriptome, proteome and metabolome analysis, and genome-scale metabolic models are available for these species (Krömer et al. 2004; Oliveira et al. 2005; Shinfuku et al. 2009; Gaspar et al. 2013). Combined with their general robustness, ease of handling and the GRAS status, both L. lactis and C. glutamicum have a strong potential for becoming platform organisms for production of polyphenolic compounds.

It should also be noted that during the earlier stages of the project, E. coli was also exploited as a platform for reconstruction of polyphenol biosynthetic pathways. E. coli has a long history of use in research on microbial-based production of various value-added chemicals. Moreover, it is one of the best studied organisms, easy to handle, and a much larger set of genetic tools is available for this organism compared to other bacteria (Yu et al. 2011; Dobson et al. 2012; Chen et al. 2013). Furthermore, E. coli has been previously used for heterologous production of polyphenolic compounds (Pandey et al. 2016; Wang et al. 2016a).

Lactococcus lactis

Lactococcus lactis is a Gram-positive bacterium from the order of Lactobacillales that has been widely used in the food industry for the manufacturing of cheese, buttermilk and other dairy products. It is a low G + C aerotolerant bacterium with a relatively small genome and simple sugar metabolism: utilization occurs via glycolysis (Embden–Meyerhof–Parnas pathway) with lactic acid being the major end-product (Gaspar et al. 2013). Its ability to ferment various carbohydrates, such as hexoses, pentoses and, in some strains, even complex carbohydrates of plant origin, as well as its high alcohol, acid and stress tolerance makes this bacterium suitable for the harsh environments commonly found under industrial production conditions. Lactococcus lactis strains have been engineered to produce multiple commodity chemicals (e.g. 2,3-butanediol and butanol), as well as food additives, including sweeteners (e.g. mannitol and xylitol), flavorings (e.g. acetaldehyde and diacetyl), and vitamins. Furthermore, production of plant secondary metabolites, namely isoprenoids and stilbenes, has been demonstrated in this organism (Neves et al. 2005; Katz et al. 2011; Gaspar et al. 2013; Song et al. 2014). In addition, L. lactis is well known as an excellent host for the expression of both prokaryotic and eukaryotic proteins, and has been consequently used for development of live vaccine (Kunji et al. 2005; Hernández et al. 2007; Frelet-Barrand et al. 2010; Pontes et al. 2011).

As depicted in Fig. 6, in plants the biosynthesis of the core intermediate of the phenylpropanoid pathway, p-coumaric acid, begins from l-phenylalanine and occurs in two steps: a deamination reaction catalyzed by phenylalanine ammonia-lyase (PAL) and a hydroxylation reaction catalyzed by cinnamate 4-hydroxylase (C4H). The latter enzyme belongs to the CYP family that is often challenging to express in active form in prokaryotic systems (Dvora and Koffas 2013). However, in some bacteria and fungi there exists an alternative pathway where l-tyrosine is converted directly into p-coumaric acid via the action of tyrosine ammonia-lyase (TAL), hence bypassing the C4H step (Fig. 6). As a first step towards establishing a platform strain for the production of polyphenols in L. lactis, in collaboration with Nielsen and coworkers, we identified several novel TAL enzymes that specifically produce high levels of p-coumaric acid from l-tyrosine (Jendresen et al. 2015).

To test the potential of L. lactis to produce more complex phenolic compounds, we introduced a pathway for the biosynthesis of resveratrol. The introduced pathway consisted of three enzymes: TAL, 4CL (4-coumaroyl-CoA ligase, which converts p-coumaric acid to p-coumaroyl-CoA), and STS (stilbene synthase, which catalyzes the condensation of p-coumaroyl-CoA and three molecules of malonyl-CoA to yield trans-resveratrol, Fig. 6). The corresponding genes were placed on the high copy number vector pNZ8048 under the control of the nisin-inducible promoter P nisA . This promoter is a part of a tightly-controlled inducible gene expression system which is based on the auto-regulatory mechanism controlling the production of the bacteriocin nisin (Kuipers et al. 1993, 1998). The producer L. lactis strain contains a chromosomally-integrated two-component system NisRK allowing induction of its target promoters by addition of sub-inhibitory concentrations of the bacteriocin nisin to the exponentially-growing cell culture (Linares et al. 2010). Upon cultivation of the producer strain in a chemically defined medium, production of trans-resveratrol was detected in the order of several mg l−1. When TAL was replaced with a promiscuous PAL/TAL enzyme (MacDonald and D’Cunha 2007), additional production of small amounts of pinosylvin (trans-3,5-dihydroxystilbene) from l-phenylalanine was observed. Furthermore, production of methylated variants of trans-resveratrol was also achieved (unpublished results). Obtained titers for trans-resveratrol are comparable to those obtained in an early work on trans-resveratrol production in both E. coli and yeast (Beekwilder et al. 2006; Choi et al. 2011). Current research efforts focus on improving the production efficiency through metabolic engineering of the central carbon metabolism for increased precursor supply.

Anthocyanins (glycosylated anthocyanidins) are another group of phenolic phytochemicals. In addition to their antioxidant properties, anthocyanins are valuable for their colors, which range from orange to blue (Falcone Ferreyra et al. 2012). They are of particular interest for the food industry as natural alternatives to replace synthetic dyes. Anthocyanins are synthesized from flavanones by flavanone 3-hydroxylase (F3H), which adds a hydroxyl group to the C3 position. Resulting dihydroflavonols are further processed by dihydroflavonol 4-reductase (DFR), which catalyzes reduction of the carbonyl group of the C4 position to form leucoanthocyanidins, which in turn are converted to anthocyanidins via the action of anthocyanidin synthase (ANS). Anthocyanidins are further stabilized through a C3-glycosylation reaction usually catalyzed by UDP-glucose:flavonoid 3-O-glucosyltransferase (UFGT) to form the anthocyanidin 3-O-glucosides (Falcone Ferreyra et al. 2012; Fig. 6).

In order to reconstruct the anthocyanin production pathway in L. lactis, native genes and genes codon-optimized for L. lactis from various plant species coding for F3H, DFR, ANS and UFGT were placed under control of the nisin-inducible promoter P nisA on a high copy-number vector or were integrated into the chromosome of L. lactis. Each protein was tagged with an N- or C- terminal strep-tag, and Western blots were performed to detect the heterologous proteins in cell-free extract of L. lactis. Activity of each enzyme was probed in vitro and in vivo and the reaction products were analyzed using high performance liquid chromatography (HPLC). Production of various types of cyanidin-derived anthocyanidins and anthocyanins by the engineered L. lactis strains was detected, further demonstrating the potential of this lactic acid bacterium as a production host for plant-derived bioactive compounds for food applications (unpublished results). To our knowledge, this is the first report of using lactic acid bacteria for the production of complex phenolic compounds.

Malonyl-CoA is a rate-limiting precursor in the biosynthesis of various phenolic compounds in bacteria (Xu et al. 2011; Bhan et al. 2013). Under physiological conditions, L. lactis cells use malonyl-CoA as a substrate in the chain elongation step of the fatty acid biosynthesis. FapR of Bacillus subtilis is a transcriptional repressor that directly senses the intracellular pool of malonyl-CoA. A metabolic biosensor based on the interaction of malonyl-CoA with FapR was previously designed in Escherichia coli (Xu et al. 2013). As a part of the efforts to enhance polyphenol production, a transcriptional biosensor for malonyl-CoA was constructed for the use in C. glutamicum but also proved to be fully functional in L. lactis and could therefore be used for screening of variants with an enhanced malonyl-CoA pool (unpublished results).

Among other activities that were pursued in the early stages of the project, we also tried to express plant CYPs in L. lactis, as many steps of the polyphenol biosynthetic pathway are catalyzed by these heme-bound monooxygenases. Functional expression of genes encoding membrane-anchored CYPs in prokaryotes is known to be notoriously difficult, and is limited by problems associated with the differences in intracellular localization (natively to the plant endoplasmatic reticulum), membrane composition, protein folding, and cofactor utilization. The latter element became especially crucial when attempting to express these proteins in L. lactis, because this, normally anaerobic, bacterium does not have the ability to synthetize heme, despite undergoing a metabolic shift to respiration when supplemented with an exogenous source of heme and being capable of regulating heme homeostasis (Pedersen et al. 2012).

We focused our efforts on CYP79A1 from Sorghum bicolor as it is a well-characterized CYP involved in dhurrin biosynthesis (Bak et al. 2000), and has been previously successfully produced in E. coli (Vazquez-Albacete et al. 2016). As a testbed for the expression of this CYP we have used the Waldo platform (Waldo et al. 1999) that was previously instrumental in producing functional CYP79A1 in E. coli. Briefly, the platform is based on a fusion of the membrane protein with a GFP folding reporter at the C terminus: the fused GFP is designed to act as a fluorescent reporter only when the membrane protein is properly folded and its C terminus is oriented towards the cytoplasm.

We detected CYP79A1-GFP by both fluorescence measurements of the L. lactis culture and in-fluorescence protein gels, but were not able to detect activity of the CYP in our in vitro tests. Purified membrane protein fractions from cell cultures grown both aerobically (with hemin supplementation) and anaerobically (with addition of hemin after purification of membrane proteins) did not show any product formation in our TLC-based assay (Blomstedt et al. 2012). These results suggested that CYP79A1 was probably misfolded in L. lactis, although we did not perform any follow-up experiments to confirm this hypothesis. The topic was not pursued any further because any CYP active in the biosynthetic pathways for the selected model compounds could be by-passed (for example the C4H-catalyzed step could be by-passed by using TAL instead of PAL).

Corynebacterium glutamicum

Corynebacterium glutamicum is an important organism in industrial biotechnology for the microbial production of bulk chemicals, in particular of amino acids (Becker and Wittmann 2012). C. glutamicum is a very robust microorganism, which shows high resistance to the presence of small aromatic compounds (Liu et al. 2013), which renders C. glutamicum a very promising host for the production of pharmacologically interesting plant-derived polyphenols (Marienhagen and Bott 2013). Until recently, the activity of a complex catabolic network for aromatic compounds meant that C. glutamicum was not used for the production of aromatic compounds (except for aromatic amino acids; Shen et al. 2012) due to rapid degradation of the products of interest. However, C. glutamicum can be easily modified genetically and a toolbox for the high-level expression of heterologous genes originating from other organisms, has become available, and is based on a set of strong and inducible promoters (Eggeling and Bott 2005; Pátek and Nešvera 2013; Kortmann et al. 2015).

Surprisingly, in initial experiments, it turned out that C. glutamicum is able to grow on naturally occurring phenylpropanoids such as p-coumaric acid, ferulic acid, caffeic acid and 3-(4-hydroxyphenyl)-propionic acid as sole carbon and energy sources. The maximum growth rates on the selected phenylpropanoids ranged from 0.15 to 0.23 h−1 corresponding to doubling times of 3–4.5 h (Kallscheuer et al. 2016a). We suspected that the activity of this pathway would come into conflict with any attempt to produce polyphenols as both pathways compete for phenylpropanoids. Unfortunately, the underlying metabolic pathway responsible for the degradation of phenylpropanoids in C. glutamicum was unknown at the start of the BacHBerry project. Global gene expression analyses were conducted, which led to the identification of a gene cluster consisting of seven genes (designated phdT, phdA, phdR, phdB, phdC, phdD and phdE). In response to feeding of the above-mentioned phenylpropanoids these genes were upregulated up to 100-fold (Kallscheuer et al. 2016a). In subsequent studies, it turned out that the genes in this cluster code for the enzymes of the phenylpropanoid catabolic pathway in C. glutamicum. The gene phdT codes for a phenylpropanoid transporter protein, whereas phdA and the predicted operon phdBCDE code for enzymes involved in the degradation of phenylpropanoids. The phd gene cluster is transcriptionally controlled by a MarR-type repressor encoded by phdR. Cultivation experiments conducted with C. glutamicum strains carrying single-gene deletions showed that loss of phdA, phdB, phdC or phdE abolished growth of C. glutamicum with all tested phenylpropanoid substrates. The intracellular accumulation of pathway intermediates determined via LC–ESI–MS/MS in single-gene deletion mutants showed that the phd gene cluster encodes for enzymes involved in a CoA-dependent, β-oxidative deacetylation pathway, which is essential for the utilization of phenylpropanoids in C. glutamicum. The identified pathway thereby represents a peripheral catabolic route responsible for chain shortening of phenylpropanoids to benzoic acids and acetyl-CoA (Fig. 7). C. glutamicum converts the resulting intermediates benzoate, 4-hydroxybenzoate and protocatechuate to succinyl-CoA and acetyl-CoA by central degradation pathways for aromatic compounds (especially the well-characterized β-ketoadipate pathway), and catabolites are ultimately channeled into the tricarboxylic acid cycle (Shen and Liu 2005; Fig. 7). Unexpectedly, C. glutamicum failed to grow on the phenylpropanoid, cinnamic acid, which lacks ring para-hydroxylation. Astonishingly, a C. glutamicum strain with a single deletion of the gene phdR coding for the identified transcriptional repressor was able to grow with cinnamic acid as sole carbon and energy source (Kallscheuer et al. 2016a). This indicated that PhdR has a pronounced specificity for hydroxylated phenylpropanoids, especially when hydroxylated in para-position of the aromatic ring. In the course of our studies, cinnamic acid was the only phenylpropanoid tested lacking this hydroxylation, so the complete specificity of this catabolic pathway of phenolics in C. glutamicum remains unknown.
Fig. 7

Overview of the endogenous phenylpropanoid degradation and the engineered pathway for polyphenol synthesis in C. glutamicum. One of the precursors for the production of the stilbene trans-resveratrol (catalyzed by stilbene synthase, STS) or for naringenin chalcone (catalyzed by chalcone synthases, CHS) is p-coumaroyl-CoA. Naringenin chalcone isomerizes to the (2S)-flavanone naringenin either spontaneously or catalyzed by the activity of chalcone isomerase (CHI). In C. glutamicum, p-coumaric acid can be degraded to 4-hydroxybenzoate by a CoA-dependent, β-oxidative deacetylation pathway. 4-Hydroxybenzoate is subsequently hydroxylated to protocatechuate, which is catabolized to succinyl-CoA and acetyl-CoA by the β-ketoadipate pathway. Genes coding for the underlined enzymes were deleted in the course of the construction of the platform strain C. glutamicum DelAro4

During construction of the C. glutamicum platform strain for plant polyphenol production, four gene clusters, comprising 21 genes, including four genes of the phd cluster involved in the catabolism of aromatic compounds, were deleted. The resulting platform strain C. glutamicum DelAro4 was unable to degrade any phenylpropanoid tested, and also could not utilize simple benzoic acids such as protocatechuate (Kallscheuer et al. 2016c). This strain was used as a chassis for the microbial production of polyphenols, in particular, stilbenes and (2S)-flavanones, in C. glutamicum.

Plasmid-borne expression of codon-optimized genes coding for a 4CL from parsley (Petroselinum crispum), a chalcone synthase (CHS) and a chalcone isomerase (CHI) from Petunia x hybrida from the strong T7 promoter in this strain background enabled production of (2S)-flavanones (Fig. 7). Maximal titers of 35 mg l−1 naringenin and 37 mg l−1 eriodictyol were obtained from the supplemented phenylpropanoids p-coumaric acid and caffeic acid, respectively. Expression of the 4 cl gene in combination with a codon-optimized gene coding for an STS from peanut (Arachis hypogaea) allowed for the production of the stilbenes pinosylvin, trans-resveratrol and piceatannol starting from supplemented phenylpropanoids cinnamic acid, p-coumaric acid and caffeic acid, respectively (Kallscheuer et al. 2016c; Fig. 7). Stilbene concentrations of up to 158 mg l−1 could be achieved in defined CGXII medium with 4% glucose and supplemented with cerulenin, a fatty-acid biosynthesis inhibitor that is known to enhance polyphenol production in bacteria by increasing the intracellular concentrations of malonyl-CoA (Santos et al. 2011; van Summeren-Wesenhagen and Marienhagen 2015; Cao et al. 2016). Engineering amino acid metabolism for an optimal connection to the synthetic plant polyphenol pathways was conducted to enable stilbene production directly from glucose. Indeed, the additional heterologous expression of aroH from E. coli (coding for the first enzyme of the shikimate pathway responsible for the synthesis of aromatic amino acids) together with a codon-optimized gene coding for a TAL from Flavobacterium johnsoniae led to the production of up to 60 mg l−1 trans-resveratrol in the absence of supplemented p-coumaric acid (Kallscheuer et al. 2016c). Furthermore, C. glutamicum was used to produce trans-resveratrol from a cheaper precursor 4-hydroxybenzoic acid (HBA) through reversal of a microbial β-oxidative phenylpropanoid degradation pathway (Kallscheuer et al. 2016b). This novel synthetic pathway circumvents any need for the endogenous supply of aromatic amino acids as polyphenol precursors and in C. glutamicum instantly yielded 5 mg l−1 trans-resveratrol from supplemented HBA without any further optimization. Very recently, we also engineered C. glutamicum to produce more complex polyphenols including flavonols (kaempferol and quercetin) and O-methylated stilbenes (pinostilbene and pterostilbene). The obtained pterostilbene titer was 42 mg l−1, which is comparable to the titer of 50 mg l−1 obtained in engineered E. coli. In C. glutamicum, for the flavonols kaempferol and quercetin titers of 23 and 10 mg l−1, respectively, were detected. The aforementioned titers were obtained from the phenylpropanoids p-coumaric acid and caffeic acid, respectively, as precursors and represent the highest flavonol titers obtained in engineered microorganism until today (Kallscheuer et al. 2017).

The main bottlenecks for the production of polyphenols turned out to be the TAL activity as well as the intracellular availability of malonyl-CoA, representing the cosubstrate in the STS- and CHS-reactions. On-going efforts to improve polyphenol production in C. glutamicum therefore focus mainly on improving the activity of the rate-limiting enzymes and increasing the intracellular malonyl-CoA pool.

Other activities

As a part of this work, we also aimed at reconstruction of a previously unknown biosynthetic pathway for the flavonol fisetin (3,7,3′,4′-tetrahydroxyflavone, Fig. 8). Fisetin is a potent anti-inflammatory, anti-cancer, and anti-oxidant compound (Suh et al. 2008; Funakoshi-Tago et al. 2011; Jash and Mondal 2014). Moreover, it has been demonstrated to have a neuroprotective effect and also to limit the complications of type I diabetes (Maher 2009; Maher et al. 2011). Based on the molecular structure of this compound, we hypothesized that its biosynthesis should follow the general pathway of flavonol biosynthesis (Figs. 6, 8): a flavanone precursor would be converted into dihydroflavonol by F3H, and then further converted into the corresponding flavonol via the action of flavonol synthase (FLS). In the last step of the biosynthesis, a hydroxyl group would be added at the C3′ position by flavonoid 3′-hydroxylase (F3′H). This part of the work was carried out in E. coli. Isoliquiritigenin (2′,4′,4-trihydroxychalcone) appeared to be the most suitable precursor, and when supplied to an E. coli culture expressing F3H and FLS from Arabidopsis thaliana, production of garbanzol and resokaempferol was detected. Resokaempferol could be further converted into fisetin by expression of F3′H genes from A. thaliana and Pn. hybrida fused with a cytochrome-P450 reductase (CPR) from Catharanthus roseus (Stahlhut et al. 2015). The three enzymes were then combined with the isoliquiritigenin biosynthetic pathway, consisting of TAL from Rhodobacter sphaeroides, 4CL from Pr. crispum, CHI from Medicago sativa, and a fusion protein of CHS from Pn. hybrida and CHR from Astragalus mongholicus, to yield 0.3 mg l−1 of fisetin (Stahlhut et al. 2015). This work allowed reconstruction of a complete pathway for the biosynthesis of fisetin from l -tyrosine as the starting precursor using enzymes from various sources, and the identified pathway is currently being transferred to Gram-positive hosts.
Fig. 8

Heterologous biosynthetic pathway for fisetin assembled in E. coli. TAL tyrosine ammonia-lyase, 4CL 4-coumaroyl-CoA ligase, CHS::CHR chalcone synthase::chalcone reductase fusion, CHI chalcone isomerase, F3H flavanone 3-hydroxylase, FLS flavonol synthase, F3′H::CPR flavonoid 3′-hydroxylase::cytochrome P450 reductase fusion

Bioinformatics and modelling

There were multiple activities within BacHBerry, both within the discovery and the microorganism engineering sectors, which required the use of bioinformatics tools. Hence, the mathematical modeling and computational parts of the project aimed at addressing three major problems. The first two were related to gene expression, either for performing functional genomics analysis using the RNA-seq data from berries or to design sequences for heterologous expression of plant genes in microbes. The third problem concerned metabolic network optimization for both a single population and for a community of microbes. We detail below the main results obtained on each of the above activities. Other subjects that were addressed as a part of this project were metabolic communication between the different species in a community (Milreu et al. 2014; Andrade et al. 2016) and modeling dynamic metabolic networks for potential application in strain optimization (Costa et al. 2014; Hartmann et al. 2015a, b, 2016; Costa et al. 2016).

Sequencing data analysis and plant functional genomics

Understanding which genes are differentially expressed during phenolic production by plants requires the capacity to undertake a rapid analysis of RNA-seq data. One of the main problems to be addressed in this case is the search for coding sequences that are homologous to those already present in sequence databases, which involves identification, selection and annotation of the best matches. Currently, such analyses rely heavily on manual curation as well as on the manual assembly of results obtained using different computational procedures and sequence databases. Automated processes become crucial in the light of the ever-increasing amounts of generated transcriptome data. Furthermore, automation of processes also allows improvement of the quality of the analyses, as the likelihood of human error grows with the amount of manual processing and verifications necessary. Moreover, most of the existing tools are able to annotate the transcriptome but lack the curation functionalities that are as important as the primary homology-based identification of candidate genes.

As mentioned previously we developed a pipeline called MassBlast (Veríssimo et al. 2017), to improve and accelerate the annotation procedure of RNA-seq data. Besides allowing for operators to query multiple databases for performing homology searches, the tool also provides a set of parameters that can be defined in a user-friendly way. MassBlast seeks the best alignment between the sequences in a dataset consisting of contigs obtained from RNA-seq assemblies and a database of known sequences. In the context of BacHBerry and as a general validation of the method, MassBlast was used for the analysis of the blackberry transcriptome, and preliminary results showed a significant improvement in annotation speed (5 min vs. at least 26 working days, to annotate all known CYP families in blackberry) with very high accuracies. MassBlast is freely available at https://github.com/averissimo/mass-blast/.

Sequence design for heterologous expression

Synthetic biology aims at constructing microorganisms capable of producing economically-relevant chemical products in a cost-effective way, which involves optimizing the functioning of metabolic pathways and reactions. These pathways and reactions involve proteins that are frequently heterologous. In order to increase the production of metabolites, it is possible to optimize heterologous protein production in the target host. This may be done by altering the codons in the gene that encodes for that protein. Each codon is a sequence of three nucleotides encoding one amino acid that is part of a protein. Since each amino acid can be encoded by several synonymous codons, a given protein can be expressed using a large number of different codon combinations.

To address this problem, we worked on improving the machine learning models capable of predicting protein expression levels based on their codon usage frequency (Fernandes et al. 2016) by using Support Vector Regression (SVR) and Partial Least Squares (PLS). SVR yields predictions that surpass those of PLS. It has been shown that it is possible to improve the predictive ability of the models by using two additional input features, namely codon identification number and codon count, besides the already used codon bias and minimum free energy. In addition, applying ensemble averaging to the SVR or the PLS model improved the results even further. Different ensembles and features can be tested with the aim of further improving the prediction models.

Metabolic network optimization

Another goal of the computational activities of BacHBerry was to explore methods for in silico metabolic engineering of phenolic production in L. lactis and C. glutamicum. For this we selected four model target compounds: fisetin, trans-resveratrol, pelargonidin and quercetin. Additionally, optimization of malonyl-CoA production was also considered, since this is known to be a major bottleneck in heterologous flavonoid production. The first step involved performing network optimization for a single population, which included prediction of knock-out strategies and determination of minimal sets of genetic interventions that could guarantee an increased production of the target compound production based on stoichiometric models for the two organisms. These predictions were compared with experimental results and model refinement was conducted in cases of discrepancies. For the second step, we considered a use of microbial consortia for production purposes. A method was developed that is being used for modeling the production of the compounds of interest for BacHBerry using L. lactis and C. glutamicum. Other, possibly larger consortia, may be considered in the future.

Single strain-based production

Recently, several computational methods for in silico metabolic network optimization have emerged and are currently being applied in metabolic engineering (Vera et al. 2010; Zomorrodi et al. 2012; Machado et al. 2015; Simeonidis and Price 2015; Bergdahl et al. 2015). They are based on different approaches and rationales, thereby leading to distinct solutions. One key issue is the multitude of results, known to be dependent on the specific algorithms and solvers, used. In order to address this problem, a consensus-based approach was developed, following our work on meta-analysis of transcriptomic studies (Caldas et al. 2014). This method is based on running several optimization procedures and analyzing a posteriori the solutions, looking for a consensus. The rationale is to have rankings of hypotheses that may provide confidence in particular sets of proposed genetic alterations from various aspects.

In the case of a single population of microbes (i.e. a monoculture), the genome-scale models of L. lactis and C. glutamicum were extended with the biosynthetic pathways of the four above-mentioned target compounds (Fig. 9). Subsequently, five different optimization methods were applied to the genome-scale models (extended with the heterologous pathways): OptKnock (Burgard et al. 2003), MOMA (Segrè et al. 2002), OptGene (Patil et al. 2005), RoboKod (Stanford et al. 2015), and RobustKnock (Tepper and Shlomi 2010), and the consensus ranking was obtained for each strain and compound. The hypotheses were generated with the rank product test and the outputs were lists of gene deletions that would achieve the best ranks using defined criteria, namely maximum predicted target compound production, maximizing the minimum predicted target compound production and distance from the wild-type flux distribution. These results were then further filtered and experimentally validated, resulting in several promising mutant strains where production was increased by up to three-fold (unpublished results). Implementation of this pipeline is possible via a software package called OptPipe, available at https://github.com/AndrasHartmann/OptPipe.
Fig. 9

Heterologous biosynthetic pathways introduced into the genome-scale models of C. glutamicum and L. lactis leading to the four target compounds: (i) trans-resveratrol (ii) pelargonidin (iii) quercetin and (iv) fisetin. Two heterologous enzymes (TAL and 4CL) are common for the biosynthesis of the phenolic molecules. Enzyme abbreviations: TAL tyrosine ammonia-lyase, 4CL 4-coumaroyl-CoA ligase, STS stilbene synthase, CHS chalcone synthase, CHR chalcone reductase, CHI chalcone isomerase, F3H flavanone 3-hydroxylase, FLS flavonol synthase, F3′H flavonoid 3′ hydroxylase, CPR cytochrome P450 reductase

We then explored the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables were involved in the model. In particular, we proposed multi-objective models to suggest reaction deletion strategies and to deal with problems where several functions must be optimized simultaneously, such as maximization of bioproducts while minimizing toxicity. We thus introduced Multi-Objective Mixed integer Optimization for metabolic engineering, a computational framework that aims to model and solve optimization problems, generated for predicting reaction knockout strategies by means of multi-objective programming. We compared the results from our method with those obtained by using the well-known bi-level optimization model (OptKnock). Furthermore, we studied two multi-objective optimization problems arising from the metabolic engineering of microorganisms, showing that indeed a multi-objective setting is promising, as it expands the set of generated hypotheses and allows inclusion of multiple goals, simultaneously.

For a community of microbes

Pure microbial cultures have long been used for production of high-value compounds, as illustrated by the example of S. cerevisiae producing artemisinic acid, an important precursor for the anti-malaria drug artemisinin, on a large scale (Ro et al. 2006; Lenihan et al. 2008). However, there is increasing evidence that such processes could run more efficiently if a community of microbial species is used (Logan and Rabaey 2012; Jones et al. 2016; Wen et al. 2016). There are multiple reasons including an increased production efficiency of the system due to lessening of metabolic burden for individual cells. Another important consideration is that such community systems might have higher chances of avoiding toxic effects specific for individual microorganisms. Microbial consortia are believed to be able to sustain more complex production pathways, as well as being more robust as a group. The challenge, however, remains in establishing precisely which set of strains or species is best for the production of any given compound.

As part of BacHBerry, we introduced an initial topological model and a combinatorial algorithm that enabled us to propose optimal consortia to produce compounds that were either exogenous to the consortium, or were endogenous but where interaction between the species in the consortium could improve the production line (Julien-Laferrière et al. 2016). For initial validations of the model and the method, we applied it to two case-studies taken from the literature. The first case involved production of two bioactive compounds, penicillin and cephalosporin C, both of which are antibiotics used in the pharma industry. A synthetic consortium composed of four different species, three Actinobacteria (Streptomyces cattleya, Rhodococcus jostii RAH_1 and Rc. erythropolis BG43) and one methanogenic Archaea (Methanosarcina barkeri), was then run through the algorithm, allowing introduction of genetic manipulations and regulatory processes. We showed that the best solution involved only two of the four species, namely St. cattleya and M. barkeri (Julien-Laferrière et al. 2016). Another considered case was a consortium that may be seen as artificial in the sense that it associates two wild-type populations of microbes, pathogenic γ-proteobacteria Klebsiella pneumoniae and methanogenic Archaea M. mazei, which normally do not interact in nature. K. pneumoniae is a natural producer of 1,3-propanediol, which is an important building block for polymer production. Our results demonstrated that co-cultivation of K. pneumoniae with M. mazei may enable obtaining higher yields of this compound in Klebsiella (Julien-Laferrière et al. 2016). This supported a previous study involving co-cultures of Clostridium sp. with methanogenes such as Methanosarcina sp. CHTI55 (Koesnandar et al. 1990). The developed algorithms are currently being used for developing consortia of polyphenol-producing Gram-positive bacteria. This task was inspired by the work of Jones and colleagues, who were able to achieve an overall 970-fold improvement in titers for flavan-3-ols production from phenylpropanoic acids by using a co-culture of two E. coli strains with further optimization of cultivation conditions, as compared to previous studies that used monocoltures for this purpose (Jones et al. 2016).

Recovery and purification of polyphenols from fermentation broth

Downstream processing is an important part of any production process and consists of steps required for recovery and purification of biosynthetic products from fermentation broth, which may have a significant impact on the final costs of a product (Goldberg 1997). In the design of downstream processing within BacHBerry, trans-resveratrol (Fig. 7) was used as the test product, being one of the model phenolic compounds that was successfully produced in both C. glutamicum and L. lactis.

In a typical fermentation broth, most of the components present are either hydrophilic (e.g., salts, organic acids, non-consumed carbon sources (usually glucose), nitrogen source (ammonium sulfate, for instance)) or are much larger molecules than trans-resveratrol (e.g., proteins, nucleic acids, cells in suspension). This suggested a downstream processing design strategy that used either solubility and hydrophobicity as the driving forces, or alternatively—size exclusion. Since, at low concentrations (around 100 mg l−1), crystallization would involve high energy costs per kg of product recovered, this option was discarded. Two possible extraction techniques, namely liquidliquid extraction and solidliquid adsorption, were investigated as means of recovering and purifying extracellularly produced trans-resveratrol from a C. glutamicum fermentation broth containing spent CGXII medium and glucose, due to the higher production efficiency found in this bacterium. The downstream process design aimed at a recovery of at least 80% and a purity of 95% trans-resveratrol.

Liquidliquid extraction is a technique to separate a given compound from a feed solution using an immiscible auxiliary phase that should be selective for that compound (Cox and Rydberg 2004). The logarithm (base 10) of the partition coefficient of trans-resveratrol between the aqueous and different potential organic phases was determined experimentally for several different solvents (Fig. 10). The spent fermentation broth from C. glutamicum was used as the aqueous phase in these experiments.
Fig. 10

Partition coefficients in the form of log P values of trans-resveratrol using different organic solvents using C. glutamicum fermentation broth as the aqueous phase

All tested organic solvents performed very well in extracting trans-resveratrol (minimum log P value is ~0.5, corresponding to a partition coefficient of 3.2), identifying them as suitable candidates for the recovery of this compound (when having different impurities present; Fig. 10).

One of the drawbacks of using liquid–liquid extraction, however, is that it may interfere with protein folding and stability (Prince and Smith 2004). Besides measuring the partition of the polyphenols of interest in the aqueous and organic phases, the same analysis was performed for the total protein content. Although a considerable part of the total protein remained in the aqueous phase, some of the protein seemed to be present in between the two liquid phases (probably as a result of denaturation and foaming; Fig. 11). This phenomenon could potentially reduce the recovery of the desired phenolics, which was also observed experimentally (data not shown).
Fig. 11

On the left, protein concentration (expressed as percentage of the initial broth protein concentration) in the organic and aqueous phases, after performing liquid extraction. The concentration in the organic phase is almost zero (considering the error bars), indicating that some protein stayed in the interphase. On the right, the formation of an interphase after the broth had been extracted with organic solvent (a mixture of heptanoic acid and hexyl acetate in this case) was clear

Solidliquid adsorption using hydrophobic resins is another way of purifying trans-resveratrol. As in liquid–liquid extraction, the high hydrophobicity of trans-resveratrol can be used to recover it selectively from the fermentation broth, leaving the more hydrophilic impurities behind. After an initial resin screen, one of the resins selected was the polystyrene-divinylbenzene based Amberlite XAD-16 from Sigma-Aldrich (particle diameter: 250–841 µm; dipole moment: 0.3; surface area: 900 m2 g−1; pore diameter: 100 Å).

To test the ability of this resin to recover trans-resveratrol from C. glutamicum fermentation broth (containing CGXII spent medium and glucose), a lab-scale experiment was performed with an AKTA Explorer chromatographic workstation (GE Healthcare, Sweden; see Fig. 12). A 30 ml column was packed with resin and pre-equilibrated with MilliQ water. Afterwards, 10 ml of sample was injected and a washing step with pure water was performed for 10 column volumes (CV). During elution, a gradient of water/ethanol was run for about 12 CV and the peak of trans-resveratrol showed up at around 18 CV, at an ethanol percentage of 50% (v/v). Regeneration was performed with 100% ethanol.
Fig. 12

Chromatogram following a pulse injection of 10 ml of C. glutamicum spent fermentation broth in a 30 ml column packed with Amberlite XAD-16 resin. A washing step was performed with 10 column volumes (CVs) of MilliQ water. Afterwards, elution was performed with a water/ethanol gradient for 12 CV, followed by a final regeneration step with 100% ethanol

More hydrophilic molecules such as organic acids, sugars and salts appeared in the flow-through (the first 10 CV). During the water/ethanol gradient, molecules desorbed as a function of their hydrophobicity. More hydrophobic molecules, such as trans-resveratrol, are supposed to elute at higher ethanol content in the mobile phase. In this case, trans-resveratrol appeared to be the compound eluting between 16 and 20 CV, since it also showed absorption at 304 nm (Camont et al. 2009).

Although both liquid–liquid extraction and solid–liquid adsorption present their own advantages and disadvantages, a strong point for using adsorption is the use of ethanol instead of any other organic solvent that might not be regarded as GRAS. Moreover, not using liquid–liquid extraction avoids any possible problems related with protein precipitation and emulsification. The ethanol used during elution in adsorption needs to be recovered from water by distillation (the azeotrope poses no problem as 80% (v/v) ethanol can be used).

The proposed downstream process started with a solid–liquid separation step by centrifugation to separate biomass from the liquid broth. The clarified liquid phase was then sent continuously to one of two adsorption columns, operating staggered in time. The fraction rich in trans-resveratrol was subsequently evaporated, resulting in a wet slurry containing the polyphenol in the solid phase. This slurry was filtered in a rotary vacuum filter and washed with water to remove remaining impurities adhered to the surface of the crystals. The final step was a drying step to obtain the product in its final solid form. All the outlet streams containing ethanol were directed to a distillation column in order to recover the solvent. A make-up stream ethanol was added to compensate for losses.

Optimization of phenolic production by fermentation and scale-up of bioprocesses

There are multiple differences between microbial cultivation in shake flasks versus in bioreactors. The former lacks monitoring and control of various parameters, such as dissolved O2 concentration and pH (Zelle et al. 2010). Furthermore, the fed-batch fermentation mode, where one or more nutrient is being supplied throughout cultivation (Moulton 2014), can be done in a controllable and reproducible fashion only in bioreactors. These properties make bioreactors a suitable stage for optimization of cultivation conditions, often leading to significant improvements in production yields (Hujanen et al. 2001; Ratnam et al. 2003; Li et al. 2010; Rani and Appaiah 2011).

From the beginning of the project it was clear that optimization of fermenter operation would need to be carried out using a model compound, because any newly-identified compounds would only be available at the end of the project. Hence this section of the project was focused on optimization of production of the model compound trans-resveratrol. However, it was expected that the strategies defined and the conclusions extracted from these results on trans-resveratrol will be valid for the optimization of the fermentation processes for the production of other target compounds. Several L. lactis and C. glutamicum strains designed within the project were evaluated and used for fermenter process optimization. The experiments were carried out in agitated 2–5 l fermenters with automatically–controlled pH, dissolved oxygen and temperature. Whenever required, oxygen was transferred to the medium by air injection at the bottom of the vessel.

A major goal of the project was the development of an economically feasible and sustainable industrial process, therefore the following key optimization criteria were defined: (a) minimization of the operation and investment cost and (b) maximization of the production rate and operation yield. To achieve these optimization goals, the following variables have been investigated: (1) fermentation medium composition, (2) dissolved oxygen concentration, (3) operation strategy (batch vs. fed-batch and integrated vs. separate downstream operation). Our experiments suggested that there were several constraints limiting the operation strategy to be implemented, namely: cell metabolism, the stability of trans-resveratrol inside the fermenter, and the affinity of trans-resveratrol for the adsorption medium and solvents compared to other compounds present in the fermentation broth.

Medium composition is an important parameter that often has a significant contribution towards the final production costs. According to our initial experiments, supplementation of C. glutamicum minimal medium with Brain–Heart Infusion (BHI) and a fatty-acid biosynthesis inhibitor, cerulenin, had a strong positive effect on the final titers of trans-resveratrol. However, removing these two components from the medium reduced medium costs by approximately 300-fold, resulting in a more than 50-fold drop in production costs (267 €/g compared to 5 €/g of trans-resveratrol without supplementation of BHI and cerulenin).

We also evaluated the feasibility of producing phenolic compounds from waste materials, namely cheese whey using L. lactis as a host. Whey is a lactose-rich dairy industry by-product with a high environmental impact, if dumped untreated (Palmieri et al. 2017). To achieve this task, our production strain was modified to allow it to grow and produce trans-resveratrol from lactose by introducing a plasmid carrying the lac operon (Maeda and Gasson 1986). Production at 2 litre-scale was tested using lactose as carbon source and comparing the productivity between the cultures with glucose or lactose. These experiments demonstrated that the production of trans-resveratrol from lactose, and therefore from whey, is possible with slightly inferior productivity compared to glucose. However, if whey is chosen as the fermentation medium, other, more important, issues arise. The cost of whey is comparable to the cost of glucose but the cost of the fermentation medium is only a small part of the total production costs, so variables such as whey availability and quality, as well as the stability of supply and collecting logistics will determine the feasibility of the operation more than the small losses in productivity. Clearly, if a phenolic production industrial unit were to be implemented inside a dairy company with excess whey, the whey might then be the best raw material.

Another parameter investigated was the influence of dissolved oxygen concentration on the production efficiency of L. lactis. This bacterium can activate its respiratory chain in the presence of hemin in the culture medium, and this has been demonstrated to enhance its survival allowing it to grow to higher cell densities (Duwat et al. 2001). Therefore the production strain efficiency was tested under the following conditions: aerobic with hemin added to the fermentation broth, aerobic without hemin, semi-aerobic, and fully anaerobic. In line with the available data, there was a significant increase in the final biomass when the strain was grown under aerobic conditions with hemin. However, the opposite was observed for the production of trans-resveratrol where the highest amounts of the compound were detected under anaerobic conditions.

Lastly, we have also worked on establishing a system for continuous product removal from the fermentation broth in order to simplify downstream processing and reduce trans-resveratrol oxidation due to prolonged incubation. Two strategies for continuous adsorption and storage of secreted trans-resveratrol in a resin have been devised and tested: (1) “in situ” product removal that consisted of adding a resin directly to the fermentation broth and (2) “continuous product removal” (ConPR) that involved constant pumping of the fermentation broth through a column filled with the adsorption resin. The supernatant was separated from cells via a tangential flow micro-filtration device, sent though the column, and then returned back into the fermenter (Fig. 13).
Fig. 13

Experimental set-up for the “continuous product removal” mode

The obtained results demonstrated that both the “in situ” and ConPR strategies lowered the trans-resveratrol productivity, probably due to co-adsorption of p-coumaric acid, the precursor for trans-resveratrol. This effect was most pronounced in the “in situ” operation mode due to continuous contact of the resin with the supernatant and cells. In the ConPR operation mode the effect was reduced due to low supernatant flow rate through the resin, however adsorption of trans-resveratrol was also reduced. In contrast, in the “in situ” mode the amount of adsorbed end-product was nearly identical to the amount of extracellular and intracellular trans-resveratrol produced in control experiments without addition of resin. Although the two techniques did not increase the overall productivity, they still simplified the downstream processing and may lead to a production cost reduction by eliminating the need to break down the cells. However, future research may provide a solution to avoid precursor co-adsorption resulting in increased polyphenol productivity.

Dissemination, training, societal and ethical issues

In addition to applying the latest technological advances from life sciences for establishing a pipeline for microbial-based polyphenol production, BacHBerry analyzed the economic potential of the possible products developed from this research. The assessment of economic issues of the project has been focused on the market potential of the natural colorants identified during the screening of the germplasm collection. This was done along with obtaining an overview of the relevant regulations and procedures for approval of novel food additives, including colorants, in the EU, the United States (US), and China. Regulations within both the EU and China confer a strict set of rules for bringing a novel food colorant, even a natural one, to the market. These rules consider not only the composition and purity of the substance, but also the process through which the compounds have been produced. In contrast, in the US, a new food additive can gain access to the market by simply passing the GRAS assessment. Hence, if a novel polyphenolic colorant could be produced using engineered microbial cell factories, the US market should probably be the first market to be targeted, owing to less strict regulatory requirements for the approval.

Another topic addressed in BacHBerry was access and benefit sharing of genetic resources. As the project was built upon screening of berry species from multiple countries, including China, Russia, Chile, Portugal, and the UK, it is important to harness these resources in a responsible manner. The Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization (NP-ABS) is an agreement consisting of a set of rules for fair sharing of monetary and non-monetary benefits arising from the usage and commercialization of genetic resources and traditional knowledge associated with those. It aims at achieving fairness and equity of gain distribution, especially towards indigenous and local communities (SCBD 2011; Morgera et al. 2014). The Protocol came into force in the EU on June 9th 2014. As most of the BacHBerry project partners were from EU Member States, we began collecting information about the best practices suggested from both the Convention on Biological Diversity (CBD) and the EU, and kept monitoring the relevant guidelines and updates from the EU on implementing ABS. Among the home countries of the project partners China, Denmark, France, Germany, Switzerland, and UK ratified or accepted the NP-ABS (as of November 2016) and began implementing the aforementioned regulations (European Parliament 2014). The UK also made its own regulation on implementing the Protocol (NMRO (UK) 2015; Secretary of State (UK) 2015).

Funded under the theme of “Knowledge-based Bio-Economy”, a final aim of the project was to provide scientific and technical solutions to harness berry resources, as well as to contribute to sustainable development of the economy. We consequently investigated the possible contributions of our research to build a more sustainable society based on environmental, economical, and social impacts (Pei and Schmidt 2016). Furthermore, we conducted several open dialog events, namely workshops and interviews with potential stakeholders from Austria, Denmark, and China. One of the surprising outcomes was that the general public raised concerns over the use of genetically-modified organisms (GMOs) in the pipeline a lot less frequently than originally expected. A free, science-based game for mobile devices called “Berry Maker” was produced (http://www.berrymaker.com). The game allows the user to follow the pipeline designed within our project, from acquisition of diverse berries, to extraction and analysis of biosynthetic genes, to microbial fermentation-based production of specific polyphenols and running a biotech factory that is exposed to real world situations, such as price fluctuations. The game is also available on iTunes1 and Google Play2 store. Lastly, a 22 min documentary film was produced, showing sampling of berries, analysis of interesting compounds, and experiments with natural color pigments for food products from across Europe (see: https://vimeo.com/193467652).

Conclusions

During 3 years of the project, the BacHBerry partners successfully established a complete pipeline for screening, bioprospecting, identification, and microbial production of plant phenolic compounds in Gram-positive bacteria. A summary of the most important results is presented in Table 5. The major highlights are construction of the extensive germplasm collection database, establishment of a method for high-throughput screening of bioactive extracts using the yeast-based human disease models, generation of a vast berry transcriptome database, construction of microbial cell factories in L. lactis and C. glutamicum for the production of polyphenols with yields comparable to those currently available from the literature, release of several software packages for transcriptome analysis and in silico-based metabolic engineering, as well as optimization of fermentation conditions and downstream processing for the selected organisms aimed at increasing the yield and the purify of the final products. The pipeline has been completely validated and is available to be picked up by the biotech industry for further improvement and commercialization. The developed methods and protocols should be suitable for discovery, as well as production of most groups of polyphenols in the above-mentioned organisms, without a need for significant alterations and adaptations. Overall, this project provided the scientific community with enabling tools and resources to further advancing research on berries, phenolic compounds, and metabolic engineering of microorganisms. Additional efforts were also focused on communication with stakeholders, public outreach, and popularization of science among the younger generation. The majority of the project outcomes is already available to the scientific community and the industry in form of peer-reviewed articles, or will be published within the coming year.
Table 5

Summary of the outcomes of the project

Specific application area in project

Bioprospecting for discovery of novel berry phenolics

Characterization of phenolics biosynthetic pathways

Bacterial cell factories for production of berry phenolics

Fermentation and bioprocess engineering

General aims of BacHBerry

To tap into the biodiversity of berries from around the globe and of their phenolics content

Identification and characterization of berry phenolics biosynthetic pathways

Design and generation of bacterial cell factories for production of high-value berry phenolics

Implementation of production of value-added phenolics in fermenters up to demonstration scale

Achievements of the project

 Standardization of methodologies for harvesting, extract preparation, phenolics content assessment and fractionation

 Metabolomics data from berry species originating from UK, mainland Europe, Russia, Chile, and China was obtained and made available in form of an online database

 Implementation of robust assays based on the SMART platform for discovery of bioactivity and functionality

Obtained berry extracts were assayed for bioactivity against several human diseases, and additionally evaluated for other functional uses (eq. food additives, antimicrobials). Bioactivities against Alzheimer’s, Parkinson’s and Huntington’s diseases, Amyotrophic Lateral Sclerosis and inflammation were detected

 Several pure bioactive effector compounds were identified and validated

 Transcriptomes of 13 germplasms spanning eight genera, seven families and seven orders were generated and analyzed

 New algorithms for functional genomics and improved computational methods for pathway identification were established

 Over 4000 candidate genes for various biosynthetic steps, transport and regulation of polyphenol production were identified

 Biochemical activities of some of the gene candidates were tested, thus validating the predictions

 Lactococcus lactis strains were engineered for stilbene (trans-resveratrol and pterostilbene) and anthocyanin production

 Production of various stilbenes (trans-resveratrol, pinosylvin, and piceatannol), methylated stilbenes (pinostilbene and pterostilbene), flavanones (naringenin, pinocembrin, and eryodictiol), and flavonols (kaempferol and quercetin) was achieved in C. glutamicum with yields comparable to those obtained in the model organism E. coli. Production titers for the produced flavonols are the highest titers obtained in engineered microorganism until today

 Polyphenol production was further improved by engineering enhanced precursor supply (eq. l -tyrosine, malonyl-CoA) via rational design, modeling-based prediction, and use of biosensors

 Alternative biosynthetic routes were explored though engineering of trans-resveratrol production from 4-hydroxybenzoic acid (HBA) in C. glutamicum

 Computational models were developed allowing predicting a minimal set of modifications needed for improved polyphenol production in both a single-strain population and a consortium of two or more species

 Cultivation conditions for improved productivity were developed for both L. lactis and C. glutamicum through optimization of fermentation parameters (batch vs. fed-batch operation, substrate and dissolved oxygen concentration, etc.)

 Whey was evaluated as an alternative carbon source for polyphenol production in L lactis

Multiple bioseparation methods for extraction of produced polyphenols from fermentation broth were evaluated

 Two possible designs for a system for continuous product removal from the fermentation broth were implemented

 Resveratrol production in C. glutamicum was scaled up from shake flask to 5 l fermenter to demonstration scale (250 l)

Footnotes

Notes

Acknowledgements

The authors would like to thank the European Union’s Seventh Framework Programme (BacHBerry, Project No. FP7-613793, and FP7-PEOPLE-2013-COFUND, Project No. FP7-609405) for the financial support. AD, RF, MHHN, PG, SGS, and JF would also like to acknowledge the Novo Nordisk Foundation. We express our gratitude to Dr. Martha Cyert (Stanford School of Medicine, EUA), Dr. Hitoshi Shimoi (National Research Institute of Brewing, Japan) and Dr. Yoshio Araki (Graduate School of Biosphere Science, Hiroshima University, Japan) for providing the yeast strain YAA5. We also thank Dr. Ian Macraedie, RMIT University, Australia) for providing the plasmid p416_GPDpr-GFP-A42.

Supplementary material

11101_2017_9532_MOESM1_ESM.docx (48 kb)
Supplementary material 1 (DOCX 48 kb)
11101_2017_9532_MOESM2_ESM.docx (30 kb)
Supplementary material 2 (DOCX 29 kb)

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Alexey Dudnik
    • 1
  • A. Filipa Almeida
    • 15
    • 20
  • Ricardo Andrade
    • 2
  • Barbara Avila
    • 3
  • Pilar Bañados
    • 3
  • Diane Barbay
    • 4
  • Jean-Etienne Bassard
    • 5
  • Mounir Benkoulouche
    • 4
  • Michael Bott
    • 6
  • Adelaide Braga
    • 7
    • 21
  • Dario Breitel
    • 12
  • Rex Brennan
    • 10
  • Laurent Bulteau
    • 2
  • Celine Chanforan
    • 8
  • Inês Costa
    • 15
    • 20
  • Rafael S. Costa
    • 9
  • Mahdi Doostmohammadi
    • 9
    • 22
  • Nuno Faria
    • 7
    • 21
  • Chengyong Feng
    • 17
  • Armando Fernandes
    • 9
  • Patricia Ferreira
    • 7
    • 21
  • Roberto Ferro
    • 1
  • Alexandre Foito
    • 10
  • Sabine Freitag
    • 10
  • Gonçalo Garcia
    • 15
    • 20
  • Paula Gaspar
    • 1
  • Joana Godinho-Pereira
    • 15
    • 20
  • Björn Hamberger
    • 5
  • András Hartmann
    • 9
  • Harald Heider
    • 4
  • Carolina Jardim
    • 15
    • 20
  • Alice Julien-Laferriere
    • 2
  • Nicolai Kallscheuer
    • 6
  • Wolfgang Kerbe
    • 13
  • Oscar P. Kuipers
    • 11
  • Shanshan Li
    • 17
  • Nicola Love
    • 12
  • Alberto Marchetti-Spaccamela
    • 2
  • Jan Marienhagen
    • 6
  • Cathie Martin
    • 12
  • Arnaud Mary
    • 2
  • Vincent Mazurek
    • 8
  • Camillo Meinhart
    • 13
  • David Méndez Sevillano
    • 14
  • Regina Menezes
    • 15
    • 20
  • Michael Naesby
    • 4
  • Morten H. H. Nørholm
    • 1
  • Finn T. Okkels
    • 8
  • Joana Oliveira
    • 7
    • 21
  • Marcel Ottens
    • 14
  • Delphine Parrot
    • 2
  • Lei Pei
    • 13
  • Isabel Rocha
    • 7
    • 21
  • Rita Rosado-Ramos
    • 15
    • 20
  • Caroline Rousseau
    • 4
  • Marie-France Sagot
    • 2
  • Claudia Nunes dos Santos
    • 15
    • 20
  • Markus Schmidt
    • 13
  • Tatiana Shelenga
    • 16
  • Louise Shepherd
    • 10
  • Ana Rita Silva
    • 7
    • 21
  • Marcelo Henriques da Silva
    • 14
  • Olivier Simon
    • 8
  • Steen Gustav Stahlhut
    • 1
  • Ana Solopova
    • 11
  • Artem Sorokin
    • 16
  • Derek Stewart
    • 10
    • 19
  • Leen Stougie
    • 22
    • 23
  • Shang Su
    • 17
  • Vera Thole
    • 12
  • Olga Tikhonova
    • 16
  • Martin Trick
    • 12
  • Philippe Vain
    • 12
  • André Veríssimo
    • 9
  • Ana Vila-Santa
    • 9
  • Susana Vinga
    • 9
  • Michael Vogt
    • 6
  • Liangsheng Wang
    • 17
  • Lijin Wang
    • 17
  • Wei Wei
    • 17
  • Sandra Youssef
    • 13
  • Ana Rute Neves
    • 18
  • Jochen Forster
    • 1
  1. 1.The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkLyngbyDenmark
  2. 2.Institut National de Recherche en Informatique et AutomatiqueVilleurbanne CedexFrance
  3. 3.Facultad De Agronomía e Ingeniería ForestalPontificia Universidad Católica de ChileMaculChile
  4. 4.EvolvaReinachSwitzerland
  5. 5.Department of Plant and Environmental ScienceUniversity of CopenhagenFrederiksbergDenmark
  6. 6.Forschungszentrum Jülich GmbH, Institut für Bio- und GeowissenschaftenIBG-1: BiotechnologieJülichGermany
  7. 7.Biotempo, Avepark – Edif. Spinpark, Zona Industrial da GandraBarcoGuimarãesPortugal
  8. 8.Chr. Hansen Natural Colors A/SHørsholmDenmark
  9. 9.IDMEC, Instituto Superior Técnico, Universidade de LisboaLisbonPortugal
  10. 10.The James Hutton InstituteDundeeScotland, UK
  11. 11.Groningen Biomolecular Sciences and Biotechnology Institute, Department of Molecular GeneticsUniversity of Groningen, LinnaeusborgGroningenThe Netherlands
  12. 12.John Innes Centre, Norwich Research ParkNorwichUK
  13. 13.Biofaction KGViennaAustria
  14. 14.Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
  15. 15.iBET, Instituto de Biologia Experimental e TecnológicaOeirasPortugal
  16. 16.Fruit Crops Genetic Resources DepartmentN. I. Vavilov Research Institute of Plant IndustrySt. PetersburgRussia
  17. 17.Institute of BotanyThe Chinese Academy of SciencesBeijingChina
  18. 18.Chr. Hansen Holding A/SHørsholmDenmark
  19. 19.Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical SciencesHeriot Watt UniversityEdinburghScotland, UK
  20. 20.Instituto de Tecnologia Química e Biológica António XavierUniversidade Nova de LisboaOeirasPortugal
  21. 21.Centre of Biological EngineeringUniversity of MinhoBragaPortugal
  22. 22.School of MathematicsUniversity of EdinburghEdinburghScotland, UK
  23. 23.Centrum Wiskunde & InformaticaAmsterdamNetherlands

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